In some of the simulations that we discussed in a previous blog post on molecular dynamics (MD) studies on anion exclusion, chloride never enters the montmorillonite interlayers. From such results, authors have argued for complete anion exclusion from interlayers, and thereby supported ideas of a multi-porous structure of compacted water saturated bentonite. It is, however, glaringly obvious that these simulations are not even close to being converged, and that they should not have been published in the first place. It is also clear that chloride does enter interlayers in properly conducted MD studies, in both tri- and bi-hydrated sodium montmorillonite.
Reasonably, it should only be a matter of time before researchers that support ideas of complete exclusion manage to perform MD simulations that better reflect anion equilibrium in montmorillonite. As such possible future simulations will confirm that anions have access to interlayers,1 I have in the back of my mind wondered about potential consequences. Will earlier publications be retracted? Will the entire “mainstream view” of the structure of compacted bentonite fall into oblivion? (I wish.) From this perspective I find it a bit amusing that no further MD simulation has been published to support complete anion exclusion for the last ten years (as far as I’m aware).
Hsi15 simulated bi-hydrated sodium montmorillonite interlayers in contact with a bulk compartment with two different NaCl concentrations (1.67 M and 0.55 M), and showed that these systems obey the rules for Donnan equilibrium, albeit with a substantial non-electrostatic contribution to the free energy (non-ideal conditions). Hsi22 continue this work by presenting a complementary simulation at a third NaCl concentration (1.0 M), and by performing corresponding simulations for CaCl2, at bulk concentrations 0.14 M, 0.28 M, 0.52 M, and 0.84 M (with all interlayer cations then being calcium, obviously). With chloride equilibrium simulations for several background concentrations for both Na- and Ca-montmorillonite, but for otherwise identical systems, Hsi22 are able to make thorough comparisons with Donnan equilibrium theory.
Chloride equilibrium — just as any other ion equilibrium — is conveniently expressed via the ratio \(\bar{\mathrm{c}} / c^\mathrm{ext}\), where \(\bar{\mathrm{c}}\) is the clay concentration and \(c^\mathrm{ext}\) is the corresponding bulk concentration. In a Donnan equilibrium context this concentration ratio may be identified with the ion equilibrium coefficient2 \begin{equation} \Xi_\mathrm{Cl} \equiv \frac{c^\mathrm{int}_ \mathrm{Cl}} {c^\mathrm{ext}_\mathrm{Cl}} \end{equation} where \(c^\mathrm{int}_\mathrm{Cl}\) is the interlayer concentration of chloride in a homogeneous bentonite domain in equilibrium with an external solution with chloride concentration \(c^\mathrm{ext}_\mathrm{Cl}\).
For a 1:1 system (e.g. NaCl in contact with Na-montmorillonite) a good approximation for \(\Xi_\mathrm{Cl}\) at low external concentration is3 \begin{equation} \Xi^{1:1}_\mathrm{Cl} \approx \Gamma^2 \frac{c^\mathrm{ext}_\mathrm{Cl}}{c_\mathrm{IL}} \tag{1} \end{equation} where \(c_\mathrm{IL}\) is the structural montmorillonite charge expressed as a monovalent interlayer concentration (in the model of Hsi22 and Hsi15, \(c_\mathrm{IL} = 4.23\) M) and \(\Gamma\) is a mean activity coefficient ratio for NaCl (more on that below).
While the ion equilibrium coefficient in eq. 1 depends linearly on the external concentration, the corresponding quantity for a 2:1 system (e.g. CaCl2 in contact with Ca-montmorillonite) depends on the square-root of the external concentration (note that the Cl concentration in a CaCl2 solution is twice that of CaCl2) \begin{equation} \Xi^{2:1}_\mathrm{Cl} \approx \Gamma^{3/2} \sqrt{\frac{c^\mathrm{ext}_\mathrm{Cl}}{c_\mathrm{IL}}} \tag{2} \end{equation} where \(\Gamma\) here is to be understood as a different mean salt activity coefficient ratio (for CaCl2).
The different dependencies on \(c^\mathrm{ext}_\mathrm{Cl}\) for Na- and Ca-systems, expressed in eqs. 1 and 2, are clearly reproduced in the MD results presented in Hsi22 as shown here
The dots show the chloride equilibrium coefficients as calculated in Hsi22 and Hsi15, primarily from evaluated potentials of mean force evaluated using the adaptive biasing force method. The corresponding curves in the above diagram are my attempt at fitting eqs. 1 and 2 to these MD results.
It should be noted that the linear and square-root dependencies of eqs. 1 and 2, respectively, presume that the activity coefficient ratios (\(\Gamma\)) are essentially independent of \(c_\mathrm{Cl}^\mathrm{ext}\). The successful fits of eqs. 1 and 2 thus demonstrate that this is the case for the MD equilibrium coefficients.4 Hsi22 make a deeper analysis and show that the specific values of the activity coefficient ratios correspond to differences in excess chemical potential for the salt of 1.35 kT and 1.25 kT, respectively, for the Na- and Ca-systems. Such values reflect a quite profound non-ideal behavior, which may be related to the details of the simulations (e.g. non-polarizable force fields) rather than corresponding to an actual excess barrier.
The main message in Hsi22 is nevertheless clear: Results from MD
simulations of chloride in Na- and Ca-montmorillonite are consistent
with Donnan equilibrium theory. This means, in particular
\(\Xi_\mathrm{Cl}\) is linear for NaCl and has a square-root dependence for CaCl2
For a given external chloride concentration and density, the amount chloride entering the interlayers is much larger in Ca-montmorillonite as compared to Na-montmorillonite
To be clear, the much larger amount of chloride predicted to be found
in Ca-montmorillonite has nothing to do with any notions of different
“anion-accessible” pore spaces, but is a direct consequence of
Donnan equilibrium. In these simulations, all chloride is located at
the exact same place within the clay, as shown here
This figure shows evaluated chloride density profiles in the direction perpendicular to the mineral layers in MD simulations of Na-montmorillonite (Hsi15) and Ca-montmorillonite (presented in the supporting information to Hsi22). While I have arbitrarily scaled the profiles along the y-axis in the above figure for visualization purposes, emphasis is here on the identical position within each interlayer. Note that the simulated system contains two separate interlayers, indicated by dotted vertical lines.5
One lesson from these results is that researchers who struggle with getting chloride to enter interlayers in their simulations could use CaCl2 rather than NaCl. At e.g. an external chloride concentration of \(\sim\)0.5 M, the amount chloride in the clay is about seven times larger in Ca- as compared with Na-montmorillonite, which substantially reduces the required convergence time for the simulation.
These results also highlight the urgent need for empirical data. As I
pleaded for when
concluding the assessment of chloride equilibrium concentrations in Na-bentonite, labs all over the place should routinely produce and
publish ion equilibrium measurements. It is certainly a failure of the
bentonite research field that no published empirical data exists that
can be used to compare with these theoretical results. Indeed, as far
as I’m aware, no published systematic empirical data exists at all,
for anion equilibrium concentrations in calcium dominated
bentonite.6
Note that the implication of the results discussed here is not simply
some noted interesting difference in chloride equilibrium in different
types of montmorillonite. Rather, as the results indicate that
montmorillonite interlayers play by the rules of ordinary Donnan
equilibrium, they are an additional blow to the entire contemporary
multi-porous model description of compacted water saturated bentonite.
Footnotes
[1] As I often nag about on this blog, it is quite silly to use complete anion exclusion as a starting point when studying compacted bentonite, and then trying to “confirm” such a notion with e.g. MD simulations. There is no rationale for this assumption in the first place; as we have discussed earlier, the idea seems to have originated from misunderstanding the Poisson-Boltzmann equation. Moreover, there is solid empirical evidence for salt entering interlayers, in particular from measured swelling pressure response.
[2] Hsiao and Hedström (2022) call this ratio a partition
coefficient, which complies with the scientific literature on
e.g. polymer membranes. As I discussed
here, I have chosen to stick with some of my own terminology. I
hope this does not cause unnecessary confusion.
[4] That the activity coefficient ratios do not depend strongly on external concentration in this concentration interval is also compatible with the mean salt approach that I have suggested to use for compacted bentonite. For the external solutions, mean salt activities varies quite little in this concentration range, and since the interlayer concentrations only varies with a few percent, it make sense to assume that the interlayer activity coefficients basically remain constant. Hsiao and Hedström (2022) actually note that the undulation pattern in the potential of mean force in the direction of the reaction coordinate is essentially independent of the external solution, and conclude that the interlayer environment is essentially independent of external conditions.
[5] The nearly
identical profiles within each interlayer is also a confirmation
that these simulations are properly converged.
[6] An indication that CaCl2 in Ca-montmorillonite behaves as discussed is found here.
Over a long period on the blog, we have systematically examined studies on chloride equilibrium in sodium dominated bentonite. We have now individually assessed each study that was deemed to have potential to provide relevant information. In this blog post we make some overall conclusions and give an updated picture of what is actually known empirically regarding chloride equilibrium in bentonite.
The assessment included seven studies, which are summarized in the table below. The table also provides links to each individual assessment.
These studies are the only ones, to my knowledge, that meet the
following criteria:
They involve chloride
There are both theoretical and empirical arguments for that different anions may have different equilibrium concentrations (for otherwise similar conditions). In the assessment it has therefore been important to stick to one and the same type of equilibrating anion. Moreover, chloride is certainly the anion that has been studied the most within bentonite research, with iodide as its closest “competitor”.
They involve sodium dominated bentonite
This include commercial products, such as “MX-80”, “Kunigel V1” or “Kunipia F”, or materials that were intentionally prepared for the study (more or less pure Na-montorillonite).
Some studies exist where ion equilibrium is explored in other systems, e.g. claystone or bentonites dominated by divalent counter-ions. But, since we have every reason to belive that the conditions for ion equilbrium are different in such systems, as compared to Na-bentonite, we must be careful not to include them in the analysis. We shouldn’t compare apples and oranges.
They have a specified external sodium solution
Without some knowledge of the composition of the solution in contact with the sample, an evaluated chloride concentration cannot be related to any relevant equilibrium condition. Furthermore, if the water chemistry of the equilibrating solution is too complex (e.g. involving several cations), the equilibrium cannot in a reasonably straghtforward manner be related to chloride concentrations in a sodium dominated system.
They have a systematic variation of either density or external background concentration or both
My main motivation for making these assessments is for using equilibrium data to better understand salt exclusion in bentonite. This can reasonably only be achieved if density and/or background concentration has been systematically varied.
In the following we will refer to each study with the identifying
label listed in the table above.
Comments
Through-diffusion is unneccesary
A majority of the examined studies are
through-diffusion studies (Mu88, Mo03, Vl07, Is08, Gl10). A
through-diffusion test set-up is, in fact, much more complex than
required for only studying equilibrium quantities: it involves
monitoring the chemical evolution of the external solutions (often
using radiochemical methods), and the final state (steady-state)
concentration profile is often extracted, by meticulously sectioning
and analyzing the sample (studies where final state profiles were
extracted are indicated by a “p” in the above table).
Additionally, extracting relevant information from flux data requires fitting a two-parameter model. In all assessed diffusion studies, one parameter relates to mobility (either an “effective” or an “apparent” diffusion coefficient) and one to ion equilibrium (“effective porosity”, “anion-accessible porosity”, or a “capacity factor”).1 Consequently, through-diffusion tests, despite their complexity, only provide indirect estimates of equilibrium concentrations, and the accuracy of the estimated parameters naturally depends on details of the fitting procedure and the sampled data. In this regard, most of the studies we have examined report inferior fitting procedures and flux data, where the transient stage of the process has not been adequately sampled (the only exception being Gl10).2 Estimated “effective porosities” are therefore not very reliable. This imprecision can sometimes be mitigated by also using information on the final state concentration profile. But this part of the analysis then essentially corresponds to making a quite complicated equilibrium test. Two of the five diffusion studies — Mu88 and Mo03 — were discarded because evaluated parameters (and the underlying data) are too uncertain.
From an ion equilibrium perspective, through-diffusion tests are
consequently not very “economical”.
The obvious alternative are straightforward equilibrium
tests, where samples simply are equilibrated with specified external
solutions. This can in principle be done without monitoring, and only
requires the patience to wait long enough. The lack of any requirement
to monitor these types of tests also makes them suitable, I imagine,
for involving many samples without significantly increasing the
experimental workload.
Most equilibrium tests have not been adequately performed
Although they are conceptually much simpler, only two of the assessed
studies are pure equilibrium tests (Mu04 and Mu07).
A third (Vl07) performed explicit equilibrium measurements
as part of a diffusion study.
Essentially all studies in the assessment that have recorded concentration profiles show interface excess, i.e. an increased amount of ions near the edges as compared with the interior of the samples. As this effect seems to be universal,3 it must be accounted for when making equilibrium tests, or evaluated concentrations will be overestimated. Doing this should be quite straightforward, by, for example, quickly sectioning off the first few millimeters on both sides of the samples during dismantling. Unfortunately, this has not been done in the assessed equilibrium studies,4 which makes them unsuitable. Vl07, on the other hand, recorded full profiles, and the excess effect was accounted for.
Relevant parameter ranges
After discarding two diffusion studies and two equilibrium studies,
only three studies remain for which the evaluated equilibrium
concentrations are deemed sufficiently accurate: Vl07, Is08 and Gl10.
But we should also consider the relevance of the chosen density and background concentration ranges — something that has not been discussed to any greater extent in the individual assessments. My main motivation for performing this assessment is for using equilibrium concentration data for testing models for salt exclusion in compacted bentonite. A full understanding of ion equilibrium in such systems is crucial for e.g. a relevant chemical description of bentonite buffers in radioactive waste repositories. Therefore, a preferred effective montmorillonite density range is approximately 1.2 — 1.7 g/cm3, say.
With also this criteria in mind, we may therefore rule out two of the three remaining studies; Is08 treats low density systems (\(< 1.0\) g/cm3), and Gl10 only considers an extremely high high density (1.9 g/cm3).5 This leaves us with a single study that passes both the test of providing accurate data on chloride equilibrium concentrations and being measured in relevant parameter ranges: Vl07. This study covers the approximate density range 1.15 — 1.75 g/cm3, and concentration range 0.01 — 1.0 M.
A single relevant study
On the one hand, it is great news that we have verified some data as
actually useful for evaluating salt exclusion in compacted
bentonite. On the other hand, it is very unfortunate that there only
is one single study!
Moreover, although the results of Vl07 most definitely are useful, they are not optimal. A more “pragmatic” problem with this study is that it reports whole sets of “Cl-accessible porosities”1 for each sample tested, together with an average value. But these different values simply reflect the uncertainty of the parameter for individual samples. If the study had no issues (experimental or modeling related), these values should all be the same, as they are evaluated from one and the same sample. In our assessment we identified that the major part of this uncertainty stems from evaluations from diffusion modeling, while estimations made from equilibrium considerations are more robust (total out-diffusion and stable chloride content). It is thus these estimations in Vl07 that are deemed useful, while the diffusion estimations should be discarded. Note that, since “Cl-accessible porosities” estimated from flux data are sub-optimal, so are the reported average values.
Unfortunately,
severalstudies have
used or
reported
the Vl07 data (as well as other data we have assessed) without
sufficient rigor when evaluating salt exclusion in compacted
bentonite. As a relatively recent example of this,
Gimmi and Alt-Epping (2018) compare two models for chloride exclusion with empirical data
in a figure that looks very similar to this6
In addition to the VL07 data, this plot also compare with data from Mu886 — a study that we have discarded. Taking the above plot at face value it is hard not to wonder what use the experimental data really has — the spread at certain places is almost an order of magnitude (indicated in the figure). You can basically fit any favorite model to this data (or rather, you can fit no model to this data). Gimmi and Alt-Epping (2018) anyway compare the data with two Donnan equilibrium models. One (“full Donnan”) is essentially equivalent to the homogeneous mixture model (all pore space is treated equally), while the other includes several specific additional model components (“free” porosity, exchange “sites”). Gimmi and Alt-Epping (2018) use this plot to argue for that these particular additional components become significant for bentonite at the lower density. But if we “clean up” the plot and only use data points that has passed the present assessment, the picture is instead this
With this version of the data we can at least convince ourselves that
it obeys the rules for Donnan equilibrium. But I mean that it is hard
to draw any more detailed conclusions than that. In particular, it is
a hard stretch to believe that the suggested more complex model has
any particular significance.7
The Vl07 data also has the more fundamental problem that the detailed
ionic composition of the system is not fully controlled. This is
actually the case for all assessed studies that use “natural”
bentonite rather than specifically prepared homoionic clay, and
relates to to the presence of uncontrolled amounts of divalent
cations.
Problems with ignoring the detailed equilibrium conditions
When “natural” bentonites — which generally contain more than one
type of cation — are contacted with a pure sodium solution, it is
inevitable that the material and the solution begin exchanging
cations. Furthermore, since these materials contain accessory
minerals, dissolution/precipitation processes are most probably also
initiated. Thus, at the time when the equilibrium concentration is
recorded, the exact chemical conditions are typically not known. In
particular, it is not clear exactly what e.g. the Na/Ca ratio is in
the clay. To make issues worse, the extent of this effect depends
significantly on the concentration of the external solution, where we
expect a purer sodium clay for higher external concentrations. Since
the external concentration is often varied by orders of magnitude in
these studies, this implies that quanities evaluated at different
concentrations most likely correspond to slightly different systems
(e.g. clay samples with different Na/Ca ratios). Thus, even if we have
taken measures when selecting studies to not compare apples and
oranges, this problem partly remains.
Relevant data for chloride equilibrium concentrations
Below is plotted the chloride equilibrium data that has been found
robust and relevant in the assessment (i.e. part of the data reported
in Vl07)
These values have been evaluated from the “Cl-accessible porosities” reported in table 6 in Vl07.8 The exact values of equilibrium concentration ratios and effective montmorillonite densities depend on adopted values for grain density and montmorillonite content. Here we have adopted \(\rho_s\) = 2800 kg/m3 and 80% montmorillonite. Note that equilibrium concentrations and densities are burdened with additional uncertainties that are not indicated in the above diagram. Note also that although most conditions in the above plot have two data points, these correspond to a single sample. For more details we refer to the individual assessment.
Comparing the above plot with
the one presented in the initial blog post on the assessment —
which included all available data — we note a considerably
less chaotic picture. At least, the robust Vl07 data gives evidence
for the two main features that we discussed in the initial blog post:
Chloride exclusion increases with increasing density at constant background concentration
Chloride exclusion decreases with increasing background concentration at constant density
It must be emphasized that the Vl07 data most probably has a
systematic “error”, in the sense that the data for lower background
concentrations (0.01 — 0.1 M) most probably is influenced by a
significant amount of divalent exchangable cations in the clay (Ca and
Mg). In contrast, for higher background concentrations (0.4 M, 1.0 M),
the clay is most probably in a purer sodium state.
A hundred labs should each make a hundred equilibrium tests!
After finishing this assessment the loudest question in my head is: why are not a hundred labs already on their way to each make a hundred equilibrium tests? Not only has the bentonite research sector failed when we must rely on a single soon 20-year-old study to have some idea of chloride equilibrium in sodium dominated bentonite. For other anions we essentially have no systematic data! As mentioned above, a general understanding of ion equilibrium is required in order to perform relevant chemical modeling of e.g. bentonite buffers in radioactive waste repositories.
[1] Here we do not discuss the
reasonability of these models and model parameters. I am, however,
arguing heavily in many
otherplaces on the blog that none of them are conceptually
sound. Here I have described how experimentally accessible equilibrium
concentrations can be extracted from “anion-accessible porosity”
parameters.
[2] This bad test design
isstillverycommon.
Through-diffusion tests should reasonably be designed so that the
outflux curve can be adequately sampled. As this curve behaves
drastically differently in the transient and in the steady-state
stages, the sampling frequency should reasonably be adapted.
As an example, if a lab has the capacity to make measurements at most every second day (as is done in e.g. Vl07), I suggest starting diffusion tests on a Friday and design them so that essentially no tracers reaches the target reservoir during the weekend. This can be achieved by aiming for a breakthrough time of about 20 days. The breakthrough time is related to diffusivity (\(D\)) and sample length (\(L\)) as \begin{equation*} t_\mathrm{bt} = \frac{L^2}{6D} \end{equation*} Consequently, to keep \(t_\mathrm{bt}\) relatively constant, sample lengths should be adjusted depending on the expected value of the diffusivity. For a breakthrough time of 20 days, \(D = 10^{-10}\) m2/s corresponds to \(L=32\) mm, and \(D = 10^{-11}\) m2/s to \(L=10\) mm.
With a breakthrough time of about 20 days, and tests started on
Fridays, I suggest the following measurement protocol
3 times a week the first 3 weeks (Monday, Wednesday, Friday)
2 times a week the following 3 weeks (Monday, Friday)
1 time a week the following 5 weeks (Friday)
This would give sampling at 20 occasions over about 80 days that
ideally corresponds to four times the breakthrough time, like this
However, I further argue for that through-diffusion tests generally
should be avoided. Diffusivities are more conveniently (and quickly)
measured in closed-cell tests. Likewise, for equilibrium properties
it is obviously better to perform equilibrium
tests. Through-diffusion tests, in my opinion, are only motivated
under
particular circumstances, e.g. for making several non-destructive
measurements in the same sample under various conditions.
[3] I am fully convinced that this is an effect due to swelling during sample dismantling. Molera et al. (2003) and Glaus et al. (2011) have presented other interpretations, which we have briefly discussed in the assessments. I intend to write a future separate blog post on this topic.
[4] Sample
information in Mu07 is sparse and it is not clear how dismantling
has been performed, but nothing suggests that interface excess has
neither been identified nor handled. In the individual assessment of
this study I came to the conclusion that this data after all can be
useful for evaluating models for salt exclusion. Here I anyway
discard the results, mainly due to the above mentioned lack of
information. This data should be kept in mind, however.
[5] Both Is08 and Gl10 provide interesting information,
which should not be completely forgotten. In particular, Is08 report
results for extremely high background concentrations (5.0 M). Gl10,
on the other hand, show a dependency on background concentration of
the diffusivity not seen in other tests. I was not able to rule out
this effect as an artifact and therefore encourage the bentonite
research community to help clarify what is occurring in these
specific systems.
[6] The reference for
the points labeled “M89” in
Gimmi and Alt-Epping (2018) is Muurinen et al. (1988), i.e. Mu88. I have not changed the label,
however, because the plot contains more data than what is reported
in Mu88. I have not been able to identify the source for this
additional data. We may also note that the Vl07 data reported here
appears to be quite randomly chosen; for some systems are chosen
data evaluated from diffusion, for others, data evaluated from
equilibrium measurements.
[7] On the contrary, there are many additional arguments for that sodium bentonite at 1.3 g/cm3 does not contain significant amounts of “free” porosity. Moreover, in my head, the procedure of treating ion exchange with both a Donnan equilibrium model and a surface site sorption model can only lead to overparameterization problems. It is also unreasonable in this context to add conceptually completely different features before the “full Donnan” model is treated in full, e.g. by including activity corrections.
[8] One entry in that table, for stable chloride at 1.9 g/cm3 and background concentration 0.01 M, has been discarded. The table also appears to contain a couple of typos, which have been corrected.
“Constitutive equations for diffusion in bulk, diffuse
layer, and interlayer water”
This section presents a mathematical formulation of ion diffusion in
bentonite,1
based on the material descriptions in the earlier sections. As we have
previously noted, these descriptions are fundamentally flawed in
several respects. In particular, compacted bentonite is presented as
consisting of
stacks (called “particles”), where it is supposed to make sense to
differ between external and internal interface water. TS15 also mean
that compacted bentonite (sometimes?) is supposed to contain a bulk
water phase.
As I have commented on in earlier parts, the only reason I can see to provide this nonsensical material description is as an attempt to to motivate a macroscopic, multi-porous model of bentonite. Here, TS15 make this claim quite explicit, as they write
Still it is possible to define three porosity domains, or water
domains, that can be handled separately: the bulk water, the diffuse
layer water and the interlayer water, the properties for which can
be each defined independently.
This is in essence what I have referred to as “the mainstream view” of bentonite. It is basically “possible” to define anything, but the real question is if provided definitions are relevant and useful. And, as we have already discussed in detail, there is no rationale for introducing these “porosity domains” when modeling water saturated, compacted bentonite.2
Here we will first comment on the conceptual aspects of the provided mathematical description. Thereafter, we will delve into the mathematical formulations, as I’m quite convinced that these are not correct. Unfortunately, this latter part will be quite burdened with equations and notation, but for the motivated reader I think it may be worth going through.
Conceptual aspects
TS15 choose the Nernst-Planck description of ion diffusion, and begin by commenting that this is more rigorous than using Fick’s law. I certainly agree with that a general description of ion diffusion in bentonite requires treating electrostatic couplings between the various system components (TOT-layers, ions). I don’t think, however, that putting up a massively complex description of multi-component diffusion in “three porosity domains” is the appropriate starting point for including such couplings. Since we have every reason to believe that e.g. no bulk water phase is present, I mean that this type of treatment only leads us astray from understanding the actual processes involved (we will return to this aspect in later parts of the review).
Also, as the “Fickian” aspect was the focus of the earlier section on diffusion, a reader of TS15 could here understandably get the impression that a Nernst-Planck treatment will “fix” the “issues” addressed there. But, as we have already discussed in some detail, the shortcomings of the traditional sorption-diffusion model are not solved by including multi-component diffusion in a bulk water phase. They are solved by removing the bulk water phase.
Although the above quotation states that the various “porosity domains” can be handled separately, and that their properties can be defined independently, this is not what is done in TS15. Rather, the treatment of any “porosity domain” assumes equilibrium with the corresponding bulk water phase. The entire description in TS15 is thus fully centered around the bulk water phase.
TS15 insist on treating their model quantities as functions of two
spatial coordinates (\(x\) and \(y\)), in what they refer to as a “pseudo
2-D Cartesian system” (I don’t fully understand what that
means). Diffusive flux is only assumed to take place in the
\(x\)-direction, while the “\(y\)”-dimension is used for stacking the
different “porosity domains”. The description can be schematically
illustrated like this
Here we have for illustrative purposes discretized the various components in \(x\)- and \(y\)-directions. The bulk water domain is colored blue, the “interlayer” domain pink, and the “diffuse layer” domain green. For a given \(x\)-position, the “diffuse layer” and the “interlayer” domains are assumed to always be in equilibrium with the corresponding bulk water phase. TS15 nowhere consider the length scale in the \(y\)-direction (is it therefore the coordinate system is referred to as “pseudo 2-D”?), which in practice makes the model a collection of 1-dimensional domains that are in equilibrium locally. Note that even though diffusion only is accounted for in the \(x\)-direction, transport occurs also in the \(y\)-direction, as a consequence of equilibration between the “porosity domains”.
This description is exactly what we have investigated in the blog post on why multi-porous models cannot be taken seriously. To summarize what was said there, without properly defining the length scales, it makes no sense to “short-cut” the model in the \(y\)-direction (to assume equilibrium for all domains at the same \(x\) is in a sense equivalent to assuming infinitely high mobility of all components in the \(y\)-direction). And even if we assume that such an assumption is valid — which would mean that we consider a thin strip of stacked parallel domains, where the extension in \(y\) is negligible in comparison to the extension in \(x\) — the resulting model has really nothing to do with actual bentonite. As we concluded in the multi-porosity blog post, the only way to make sense of this type of description is as a set of macroscopic continua that are assumed to be locally in equilibrium. How this equilibrium is supposed to be maintained has never been suggested by any proponent of this description. Note that this description (in particular the existence of a bulk water phase in equilibrium) disqualifies the model for describing swelling and swelling pressure.
Incorrect application of the Nernst-Planck framework
While the presented model makes little sense conceptually, TS15 also
fail in applying the Nernst-Planck framework. The problem arises, as
far as I can see, from that they don’t fully recognize the role of the
electric potential.
As we now begin scrutinizing the details of the formulation, we will suppress the variables \(x\) and \(y\) in order to, hopefully, make the equations a little more readable. It should be understood that any quantity is evaluated for some specific value of \(x\), and that all “porosity domains” are supposed to be in equilibrium at the same value of \(x\).
The electro-chemical potential
In most standard thermodynamic text books we learn that the chemical potential governs the equilibrium associated with mass transfer. Just as e.g. pressure and temperature (which govern mechanical and thermal equilibrium, respectively), the chemical potential is defined by a specific derivative of a thermodynamic potential, e.g.3
where \(c\) is concentration, \(D\) the diffusion coefficient4,
and \(RT\) the usual absolute temperature factor. Here, and in the following, we use the
symbol \(\nabla\), which denotes the general
gradient operator, but
since the model is effectively one-dimensional, it can simply be seen
as a neat way of writing \(\partial/\partial x\).
For charged species, it is common to refer to the quantity defined in eq. 1 as the electro-chemical potential, and write it as composed of an “ordinary” and a purely “electrical” part
where \(z\) is the charge number of the considered species, \(F\) is the
Faraday constant and \(\Psi\) is the electric potential. The
“ordinary” chemical potential \(\mu\) (without bar) is, perhaps a bit
confusingly, also often referred to as the chemical potential. I will
here continue to refer to this part as “ordinary”. The “ordinary”
chemical potential is furthermore conventionally expressed in terms of
a reference potential (\(\mu^0\)) and an activity \(a\)
\begin{equation}
\mu = \mu^0 + RT\ln a \tag{4}
\end{equation}
A lot can be said about the decomposition in eq. 3, but it is clear that singling out an electric potential term is useful in e.g. electrochemistry or for describing charged clay. It should, however, be kept in mind that mass transfer is fundamentally governed by gradients in \(\bar{\mu}\); always keeping eqs. 1 and 2 in mind will avoid us from making mistakes, because the mass transfer rate relates to the “total” (i.e. electrochemical) potential, and for charge neutral species the description reduces to gradients in the “ordinary” chemical potential.
\begin{equation}
j = -cD\nabla \ln a -\frac{cDzF}{RT} \nabla \Psi
\end{equation}
Expressing the activity in terms of an activity coefficient, \(a = \gamma c\), the flux can also be written (TS15 are quite fond of including activity coefficients explicitly)
Considering an arbitrary set of diffusing charged species (using the
index \(i\)), and utilizing that the electric current is zero, lead to
an expression for the electric potential gradient
For the bulk water phase, TS15 indeed provide an expression for the flux that is essentially the same as eq. 6 (their eq. 37), and which they refer to as the Nernst-Planck equation. They claim, however, that the electrochemical potential in this case lack an electric potential term (my emphasis)5,6
In absence of an external electric potential, the electrochemical potential in the bulk water can be expressed as (Ben-Yaakov 1981; Lasaga 1981) \begin{equation} \bar{\mu}_\mathrm{bulk} = \mu^0+RT \ln a_\mathrm{bulk} \end{equation}
But even without an externally applied electric field,7 a zero bulk electric potential cannot be assumed, of course, if the goal is to treat individual ion mobilities; as just shown, the gradient in electric potential that appears in eq. 6 is a result of a corresponding term in the electrochemical potential (eq. 5). Oddly, TS15 seem to treat the electric potential term in the flux as a quantity unrelated to the electrochemical potential, giving it a separate symbol, \(^\mathrm{b}\Psi_\mathrm{diff}\), and writing
\(^\mathrm{b}\Psi_\mathrm{diff}\) is the diffusion potential that
arises because of the diffusion of charged species at different
rates.
It may be natural for a reader at this point to simply assume that
TS15 have missed writing out the term \(zF^\mathrm{b}\Psi_\mathrm{diff}\) when
stating the electrochemical potential. But this seems to be a genuine
misunderstanding rather than a mistake/typo, because the pattern
repeats in the derivation of the flux in the other “porosity
domains”.
For e.g. the “diffuse layer”,8 TS15 recognize the presence of an electric potential in the expression for the electrochemical potential, writing it (this is more awkwardly expressed in eq. 42 in TS15)
TS15 don’t further comment what \(^{\mathrm{DL}}\Psi_\mathrm{diff}\) is supposed to represent, but it must reasonably be understood as “the diffusion potential that arises because of the diffusion of charged species at different rates”, in analogy with what was claimed for the bulk water phase. Note that when eq. 8 is combined with eq. 9, the flux expression contains two different electric potential gradients! (TS15 never address this oddity.)
It is thus quite clear that TS15 misunderstand the function of the electric potential in the Nernst-Planck framework. When presenting the expression for the “diffuse layer” flux (eq. 9), they also refer to Appelo and Wersin (2007), who, in turn, express the misconception explicitly9
The gradient of the electrical potential [in the expression for the
flux] originates from different transport velocities of ions, which
creates charge and an associated potential. This electrical
potential may differ from the one used in [the expression for the
electro-chemical potential], which comes from a charged surface and
is fixed, without inducing electrical current.
I cannot understand this passage in any other way than that Appelo and
Wersin (2007) are under the impression that different electric
potentials can simultaneously act independently in a given point. And
it seems like TS15 are under some similar impression.
This ignorance leads to more errors in the description of the
“diffuse layer” in TS15. We should remember that the promoted model
requires the “diffuse layer” and bulk water domains to be in
equilibrium (for the same coordinate value \(x\)). When TS15 express
this condition, i.e.
\(\bar{\mu}_\mathrm{DL} = \bar{\mu}_\mathrm{bulk}\), they again leave
out the electric potential in the bulk water (eq. 42 in TS 15)
TS15 utilize a simplified version of eq. 11, expressed in terms of concentrations rather than activities, by assuming identical activity coefficients in the two domains10
Note that the exponential in eqs. 11 and 12 actually should contain the electric potential difference between “diffuse layer” and bulk (see below).
As TS15 have not included any electric potential in the bulk water phase, they continue by incorrectly substituting \(RT\nabla \ln a_\mathrm{bulk}\) for \(\nabla\bar{\mu}_\mathrm{DL}\) in eq. 9 (i.e. they use the incorrect relation in eq. 10), giving (TS15 eq. 47)
By substituting eq. 12 into this expression, we end up with the formula for the gradient of the mysterious potential \(^{\mathrm{DL}}\Psi_\mathrm{diff}\) (TS15 eq. 48)
At face value, eq. 15 is a quite weirdly looking equation, as it relates two electric potentials — \(^{\mathrm{DL}}\Psi_\mathrm{diff}\) and \(\Psi_\mathrm{DL}\) — that both are supposed to be associated with the “diffuse layer”. But, as we will see below, there is actually a way to make some sense of eq. 15, by completely reinterpreting what these potentials represent.
A “correct” formulation
Most of the errors pointed out above are corrected by including the electric potential in the bulk water and writing the condition for equilibrium as (compare eq. 10)
But if we now plug in eq. 18 in eq. 19 we of course get
\begin{equation} j_\mathrm{DL} = -c_\mathrm{DL} D_\mathrm{DL} \nabla \ln a_\mathrm{bulk} + \frac{c_\mathrm{DL} D_\mathrm{DL} zF}{RT} \nabla \Psi^\star – \frac{c_\mathrm{DL} D_\mathrm{DL} zF}{RT} \nabla \Psi_\mathrm{DL}, \end{equation} which can be simplified to \begin{equation} j_\mathrm{DL} = -c_\mathrm{DL} D_\mathrm{DL} \nabla \ln a_\mathrm{bulk} – \frac{c_\mathrm{DL} D_\mathrm{DL} Fz}{RT} \nabla \Psi_\mathrm{bulk}, \tag{20} \end{equation} and, by identifying the electro-chemical potential in the bulk \begin{equation} j_\mathrm{DL} = -\frac{c_\mathrm{DL} D_\mathrm{DL}}{RT} \left ( RT \nabla \ln a_\mathrm{bulk} + Fz \nabla \Psi_\mathrm{bulk} \right ) = -\frac{c_\mathrm{DL} D_\mathrm{DL}}{RT} \nabla \bar{\mu}_\mathrm{bulk} \end{equation}
This whole “derivation” leads back to the rather trivial result that the flux in the diffuse layer is given by eq. 2, which we could have written down from the start! (because the model assumes \(\bar{\mu}_\mathrm{bulk} = \bar{\mu}_\mathrm{DL}\); eq. 16)
As TS15 have established the expression for the gradient of the electrochemical potential in the bulk water phase (which is implicit in their eq. 40), there should strictly be no need to consider a new expression for the same quantity in any other phase. Rather, they could simply have used the bulk water expression in all “porosity domains”, as a consequence of the assumption that these are all supposed to be in equilibrium. In a sense, this is actually what is done in TS15 — mainly by chance! — by establishing eq. 13 (their eq. 47).
Comparing with eq. 20, we see that the incorrect eq. 13 can be “saved” by reinterpreting \(^{\mathrm{DL}}\Psi_\mathrm{diff}\) as \(\Psi_\mathrm{bulk}\). Similarly, as TS15 assume the bulk electric potential to be zero, eq. 15 can be “saved” by also reinterpreting \(\Psi_\mathrm{DL}\) as \(\Psi^\star\) in that expression.12 I find this quite hilarious: By making several errors in its derivation, eq. 15 is in a sense a correct expression for the electric potential gradient in the bulk water — a potential that TS15 has put identically equal to zero.
But even if the total flux in the “diffuse layer” is correctly given by combining eqs. 12, 13 and 15 (and by completely ignoring what TS15 mean \(\Psi_\mathrm{DL}\) and \(^{\mathrm{DL}}\Psi_\mathrm{diff}\) represent), TS15 continue by defining the separate terms in eq. 13 as contributions from the “concentration gradient”, and the “diffusion potential”. As we will explore next, this interpretation fails miserably.
“Relative contributions of concentration, activity coefficient and
diffusion potential gradients to total flux”
According to TS15, the “concentration gradient” and the “diffusion potential” contributions to the “diffuse layer” flux are given by, respectively (TS15 eq. 50 and below)
Here we use the index “conc” for the “concentration gradient” contribution, and “E” for the “diffusion potential” contribution. \(A\) is referred to as a “DL enrichment factor”, and is essentially defined as the concentration ratio \(c_\mathrm{DL}/c_\mathrm{bulk}\). Using the incorrect relation in eq. 12, TS15 write these as \(A = e^{-\frac{zF}{RT}\Psi_\mathrm{DL}}\), but, as we see from eq. 18, they are really given by13 (we continue assuming identical activity coefficients in the two domains)
\begin{equation}
A = e^{-\frac{zF}{RT}\Psi^\star} \tag{23}
\end{equation}
TS15 also define a third contribution, related to the gradient of the bulk water activity coefficient. Here we will not further discuss this contribution, as it does not give any additional insight. Moreover, since TS15 anyway derive their model under the unjustified assumption that activity coefficients in the “diffuse layer” and the bulk water are identical, I cannot see the use of including their spatial variation in the description.14 (TS15 spend a couple of pages on activity coefficient models that we will ignore.)
Examples
To explore the various couplings in the presented model, TS15 apply
the Nernst-Planck framework in three examples. We can see immediately
from the presented graphs that their partitioning of the total flux in
“concentration gradient” and “diffusion potential” contributions
makes no sense.
“Example 2” imposes constant concentration gradients in the bulk water of NaCl and corresponding \(^{22}\mathrm{Na}^+\) and \(^{36}\mathrm{Cl}^-\) tracers; the NaCl concentration drops from 0.1 M to 0.001 M, and the tracer concentrations drop from 10-9 M to 10-11 M (domain length is 10 mm).
The corresponding sodium and chloride tracer concentrations in the “diffuse layer” look like this15
These profiles make sense: bulk water ionic strength decreases with distance, but so do the tracer concentrations. For the process of accumulating \(^{22}\mathrm{Na}^+\) in the diffuse layer, these two effects oppose each other, resulting in a quite flat profile. We thus expect the corresponding “concentration gradient” contribution to the flux to be quite moderate, and to fall off with distance (as the profile flattens with distances). The corresponding flux graph presented in TS15, however, looks completely different16
This plot makes no sense: The “concentration gradient” contribution is seen to increase quite dramatically with distance, rather than falling off. The value of this contribution is also orders of magnitude too large, given the imposed sodium diffusion coefficient of 1.33⋅10-10 m2/s. Moreover, the “concentration gradient” contribution is “compensated” by an equally nonsensical “diffusion potential” contribution. Note, for instance, that the “diffusion potential” contribution is negative, which implies that the corresponding electric field is supposed to be directed towards higher concentrations. This can certainly not be the case, as the electric potential gradient is caused by the negative ion having higher mobility than the positive ion (chloride diffusivity is set to 2.03⋅10-10 m2/s).
In “example 3”, the tracer concentrations in the bulk is set to a constant value (1⋅10-9 M), while the same concentration gradient as in “example 2” is maintained for the main NaCl electrolyte (from 0.1 M to 0.001 M). We thereby expect the corresponding \(^{22}\mathrm{Na}^+\) concentration in the “diffuse layer” to strongly increase with distance, which is also what is presented in TS15
while the corresponding flux plot looks like this16
This plot is almost comically absurd. According to TS15, the highly skewed concentration profile above is supposed to give no (zero, nil, 0) contribution to the flux (we see from eq. 21 that this is a consequence of that this “contribution” is directly proportional to the concentration gradient in the bulk). Instead, the huge flux is supposed to be caused entirely by an electric field that has the wrong direction! I can’t even really begin to imagine how these two plots have ended up next to each other in a peer-reviewed published article.
Note that the flux associated with a concentration gradient is what we may reasonably call a “Fickian” contribution. If TS15 mean (and they do) that these examples demonstrate how ion diffusion in bentonite works, we can understand the focus on the “Fickian” aspect at the beginning of the article (covered here). But the only reasonable response to these outlandish results is that they demonstrate that the definitions of eqs. 21 and 22 simply make no sense.
The real concentration gradient and electric field contributions
The only reasonable way to define “concentration gradient” and “diffusion potential” contributions to the “diffuse layer” flux is as the two terms in eq. 19, respectively. To rewrite these, we utilize eq. 16 (or 18), giving for the “concentration gradient” contribution (we continue ignoring activity coefficients)
where we have utilized that \(\nabla \Psi_\mathrm{bulk}\) is actually what is expressed in eq. 15 (where \(\Psi_\mathrm{DL}\) should be replaced by \(\Psi^\star\)).
We note that, to compensate the nonsensical expressions given in TS15, we should add the term \(j^\star\) (eq. 25) to the “concentration concentration” contribution (eq. 21), and subtract the same term from the “diffusion potential” contribution (eq. 22). Making these corrections gives the following components of the tracer fluxes in “example 2”
This is an infinitely more reasonable situation than what is depicted in TS15. Although the sodium flux has a non-negligible contribution from the electric field, the larger contribution is still from the concentration gradient (and none of these are gigantic terms that cancel). The concentration contribution also falls off with distance, in accordance with the shape of the concentration profile.
For chloride, the field contribution to the flux is negligible, i.e. this flux is essentially fully governed by the concentration gradient. The electric field contributions for both ions are also seen to have the correct signs: the electric field is directed from high to low concentration, and mainly functions to boost the sodium transport, in order to “keep up” with the faster chloride ions.
For “example 3” we get the following picture
The corrected \(^{22}\mathrm{Na}^+\) flux is essentially fully due to the concentration gradient, in absolute contrast to what is concluded in TS15, who mean that this flux is completely governed by an electric field in the wrong direction. Also the \(^{36}\mathrm{Cl}^-\) transport is basically solely governed by the concentration gradient, rather than by an incorrectly directed electric field (as stated in TS15). In conclusion, most of the “diffuse layer” diffusion in these examples can actually be classified as “Fickian”.
We may also investigate the electric potential profile in the
“diffuse layer” in both of these examples (this is the same in the
two cases, as the main electrolyte distribution does not change)
Here we have chosen the reference \(\Psi_\mathrm{DL}(0) = 0\). The total potential drop is only about 1 mV. Such a relatively small drop is reasonable because the denominator in the Nernst-Planck expression for the electric potential gradient (eq. 7) will always be large due to the ever-present counter-ions in the “diffuse layer”. The electric potential gradient — and thus the corresponding electric potential drop — is therefore suppressed. Physically, this means that since many (equally charged) charge carriers are always present, smaller potential differences are required to cancel electric currents caused by differences in mobility (a “diffuse layer” is a quite good conductor).
Even worse problems?
Even though some sense can be made out of the derived expression for the flux in the “diffuse layer” domain — by completely reinterpreting the electric potentials involved — it seems as the overall model is too constrained. Specifically, for an imposed set of concentration profiles in one domain it is not possible, as far as I can see, to simultaneously have zero current in all domains, while also maintaining (Donnan) equilibrium. As this blog post is already quite massive, I will elaborate on this point in the next part of the review.
Summary
Here is an attempt to sum up the main messages of this blog post.
Conceptually, the clay model presented in TS15 is exactly what was discussed in the blog post on multi-porous models, and the same issues that are identified there are present here. In particular, no attention is paid to length scales (perhaps that is why TS15 call the coordinate system “pseudo-2D”…), and no mechanism whatsoever is suggested for how the different diffusing domains are supposed to maintain equilibrium.
Mathematically (or perhaps physically), the presented Nernst-Planck flux expressions are incorrectly derived. The source of the error, as far as I can see, appears to be a misunderstanding of how electric potentials function.
TS15 define “contributions” to the “diffuse layer” flux, claimed to be related to the concentration gradient and the “diffusion potential” (i.e. the electric field), respectively. It is, however, quite obvious that these “contributions” are completely nonsensical: highly skewed concentration profiles are claimed to not have any concentration gradient contributions, and several “diffusion potential” “contributions” have the electric field in the wrong direction. We have shown that these “contributions” can be corrected, where the correction term involves the gradient of the Donnan potential. With these corrections, fluxes in the provided examples must be interpreted completely differently (they’re basically “Fickian”).
As far as I can see, the proposed model has even larger problems, related to the imposed Donnan equilibrium. We will address this issue in the next part.
Footnotes
[1] As I have commented in the earlier parts: TS15 are
fond of using the general terms “clays” and “clay minerals”, while
it is clear that the publication mainly focus on systems with
substantial ion exchange capacity and swelling properties. Here we
will continue to use the term “bentonite” for these systems, and
ignore the frequent references in TS15 to more general terms.
[2] It is of course crucial to include a component that represents compartments where the exchangeable ions reside. This is done in the TS15 model by both the “diffuse layer water” and the “interlayer water” domains. But the distinction made between these domains is based on the flawed “stack” concept.
[3] This equation assumes a single component. The
formulation of the Nernst-Planck framework naturally involves
several different charged species. When several species are
involved, we will indicate this with an index \(i\) in the equations.
[4] In some of their equations, TS15 use (electrical) mobility, \(u\), rather than diffusivity, \(D\). These quantities are related via the Einstein relation \(D = uRT/(F|z|)\). I don’t see the point in involving \(u\), as it typically makes expressions even more cluttered, and since we here ultimately are interested in diffusion coefficients anyway.
[5] In order to not cause too much confusion,
and to try to simplify a bit, I use slightly different mathematical
notation than what is actually used in the quotation. In particular,
I use the notation \(\bar{\mu}\) for the electro-chemical potential,
while TS15 don’t use a bar (\(\mu\)). I also try to avoid the index
\(i\) as much as possible.
[7] I
whined about electrostatics being poorly understood in the
bentonite research field in an earlier part of this review, but here
is more fuel for my argument. The statement “absence of an
[external] potential” has no physical meaning, as we are free to
choose the reference point (the absolute value of a potential has no
physical meaning). What TS15 must mean in the quote is “the absence
of an external electric field”. The electric field relates to the
potential as \(E = -\nabla \Psi\). Thus, all gradients of electric
potentials that occur in this text are synonymous with electric
fields (electric fields drive electric currents).
[8] This post focus almost
entirely on the “diffuse layer” domain, but a similar analysis can
be made for the “interlayer” domain. This is left as an exercise
for the reader.
[9] It should of course also rather read “…which creates a charge separation and an associated potential gradient.”, or simply “…which induces an electric field.” (showing that this part of the sentence is redundant). See also footnote 7.
[10] TS15 write cryptically that
equating the activity coefficients (and the reference potentials) in
bulk and “diffuse layer” is assumed “by following the [Modified
Gouy-Chapman] model”. But I don’t see why this model has to be
alluded to here, these assumptions can just be made.
[11] Yes, this is a Donnan potential. We will discuss this more in the next part part of the review.
[12] Again, this is related to Donnan equilibrium between the bulk and “diffuse layer” domains, that we will discuss further in the next part.
[13] This is \(f_D^{-z} \), where \(f_D\) is the Donnan factor.
[14] Rather, I would argue for that
the activity coefficients in a “diffuse layer” domain will be
quite insensitive to the imposed external (bulk) concentration, for
details see Birgersson (2017).
[15] In producing these graphs we have used the Donnan equilibrium framework to calculate the “diffuse layer” concentrations. These are given from eq. 23, where \(\Psi^\star\) is calculated from
where \(q\) is a measure of the structural charge in the “diffuse layer”, in the examples set to \(q\) = 0.33 M.
[16] Note that I have not included activity coefficient gradients when producing the plots in this section. They may therefore differ slightly from the published plots. This does not in any way influence the conclusions drawn here.
The subsection we focus on here, “Adsorption processes in clays”,
contains very little descriptions of fundamental properties of
bentonite, and is instead almost exclusively devoted to detailed
discussions on various models. As an example, already in the
first paragraph the text digresses into dealing with the problem of
defining “surface species activity” in the “DDL”2 model…
TS15 discuss adsorption separately on “outer basal surfaces”, “interlayer basal surfaces”, and “edge surfaces”. Note that the distinction between “outer” and “interlayer” basal surfaces requires that we view the compacted bentonite as composed of stacks (referred to as “particles” in TS15). But this idea is just fantasy, as we have discussed in the previous part and in a separate blog post. Moreover, central to the description of adsorption processes in TS15 is the idea of a Stern layer. This concept was briefly introduced in the previous subsection (“Electrostatic properties, high surface area, and anion exclusion”)
The [electrical double layer] can be conceptually subdivided into a Stern layer containing inner- and outer-sphere surface complexes […] and a diffuse layer (DL) containing ions that interact with the surface through long-range electrostatics […].
The next time this concept is brought up is at the beginning of the
discussion on adsorption on “outer basal surfaces”
The high specific basal surface area and their electrostatic properties give rise to adsorption processes in the diffuse layer, but also in the Stern layer.
I have written a separate blog post arguing for that the idea of Stern layers on montmorillonite basal surfaces is unjustified. Note that the notion of Stern layers on montmorillonite basal surfaces in the contemporary bentonite literature de facto means that these surfaces are supposed to be full-fledged chemical systems. In particular, the basal surface is supposed to contain localized “sites” that interact generally with ions to form surface complexes and that can involve covalent bonding.
Note further that the Stern layer was originally introduced as a model (or a model component) that extends the Gouy-Chapman description of the electric double layer. TS15, on the other hand, use the term “Stern layer” to refer to an actual physical structural component. And just as in the case of several other “components” that has been introduced in the article (“particles”, “inter-particle water”, “free or bulk water”, “aggregates”…), the existence of a Stern layer is just declared rather than argued for. And just like with the other components, these are not universally adopted. I don’t think it is appropriate to include Stern layers in this way in a review article when established parts of the colloid science community refer to them as an “intellectual cul de sac”.
So in order to even begin to criticize what TS15 actually write about adsorption processes here, one has to accept both the flawed idea of stacks as fundamental structural units and the far from universally accepted idea of Stern layers on montmorillonite basal surfaces. I will therefore refrain from doing that, and simply proclaim that I don’t accept the premises. (I believe I will have reasons to return to the models presented here when reviewing later sections of TS15.)
Additional remarks
But I think it is worth reminding ourselves that at the end of the previous section (covered in part I) we were promised that this section should qualitatively link “fundamental properties of the clay minerals” to the diffusional behavior of compacted bentonite. A reader of TS15 will thus expect this section to contain, in particular, a reasonable description and discussion on how compacted montmorillonite works. Instead a very specific (and flawed) model is imposed on the reader: the first subsection (covered in part II), introduced the fictional stack concept, and gave a confused and irrelevant explanation of anion exclusion; the presently discussed subsection is centered around Stern layers.
If the authors truly did what they claimed, in this section they should have addressed the consequences of montmorillonite TOT-layers being charged — a universally accepted fact — without introducing further assumptions. This would naturally lead to a discussion on osmosis, swelling, swelling pressure and semi-permeable boundary conditions (all simple empirical facts). These topics, in turn, should lead to considerations of e.g. ionmobility and chemical interface equilibrium. Not a single one of these topics are, in any meaningful sense, actually addressed in this section.
Before ending this part of the review, I also would like to focus on what is being said bout “interlayers”. We should keep in mind that TS15 — together with a large part of the contemporary bentonite research community — assume “interlayers” to be something different than simply the space between adjacent basal surfaces: these are supposed to be internal to the fantasy construct of a stack. When discussing adsorption in these presumed compartments they write
The interlayer space can be seen as an extreme case where the
diffuse layer vanishes leaving only the Stern layer of the adjacent
basal surfaces.
Of everything I’ve read in the bentonite literature, this is the closest I’ve come to see some actual description of what the fundamental difference between an “outer basal surface” and an “interlayer” is supposed to be. But let’s think this through. TS15 have claimed that an electric double layer is composed of a Stern layer and a diffuse layer, and we have vaugley been told that ions in the Stern layer are immobile. The above quotation thus implicitly says that that “interlayer” ions are not mobile, and that diffuse layers are only supposed to exist on “outer basal surfaces” (which, remember, is a fantasy component). But — disregarding that the stack-internal “interlayer” also is a fantasy concept — it is an indisputable experimental fact that has been known for a longtime that interlayers provide the only relevant transport mechanism in compacted bentonite.
Thus, either TS15 here provide us with yet another incorrect description of the behavior of compacted bentonite (that “interlayer” ions are immobile) or they are claiming, somewhat contradictorily, that Stern layer ions are mobile after all. But if Stern layer ions diffuse, such a structural component could reasonably not have been singled out in the first place! (The diffuse layer is supposed to have “vanished”.) As with many other issues in TS15, this question is left vague and unanswered.3 The continuation of the text does not make things clearer
For this reason, the interlayer space is often considered to be
completely free of anions (Tournassat and Appelo 2011), although
this hypothesis is still controversial (Rotenberg et al. 2007c;
Birgersson and Karnland 2009).
An interlayer completely devoid of anions certainly play by other rules than an “ordinary” electric double layer. Does this mean that TS15 assume “interlayer” ions to be immobile?4 Anyway, it is an indisputableexperimental fact that anions occupy interlayers, and I find it quite bizarre to find myself referenced in connection with the “controversial hypothesis”. The idea of compartments completely devoid of anions is widespread in the contemporary bentonite research community, but no one has ever suggested a mechanism for how such an exclusion is supposed to work; here, it apparently should be related to “Stern layers” in some (unexplained) manner. At the same time, the simplest application of Donnan equilibrium principally explains e.g. the behavior of the steady-state flux in anion tracer through-diffusion tests.
The agreement between [Poisson-Boltzmann] calculations and MD
simulation predictions was somewhat worse in the case of the
\(\mathrm{Cl^-}\) concentration profiles than in the case of the
\(\mathrm{Na^+}\) profiles (Figure 3), perhaps reflecting the poorer
statistics for interlayer Cl concentrations or the influence of
short-range ion-ion interactions (and possibly ion- water
interactions, as noted above) that are not accounted for in the
[Poisson-Boltzmann] equation. Nevertheless, reasonable quantitative
agreement was found (Table 2).
Here they acknowledge not only that anions do occupy interlayers, but also that the interlayer plays by the same rules as the “ordinary” electric double layer (“Poisson-Boltzmann calculations”). What happened to the “vanishing” diffuse layer, and to “considering” the interlayer to be “completely free of anions”? I find it quite outrageous that they fail to acknowledge these blatantly mixed messages with so much as a single word.
Update (251106): Part IV of this review is found here.
Footnotes
[1] As I have commented in the
earlier parts: TS15 are fond of using the very general terms
“clays” and “clay minerals”, while it is clear that the
publication mainly focus on systems with substantial ion exchange
capacity and swelling properties. Here we will continue to use the
term “bentonite” for these systems, and ignore the frequent
references in TS15 to more general terms.
[2] For some
reason, “DDL” is short for (the very generically sounding) “double
layer model”. Why not “DLM”?
[3] Spoiler: in later sections describing models, TS15 allow for the possibility of transport in “interlayers”.
[4] Questions like these can often not be answered because so many statements in TS15 are vague and ambiguous. In this discussion we have to refer to statements such as (my emphasis)
“The EDL can be subdivided into a Stern layer […] and a diffuse layer […].”
“The interlayer can be seen as an extreme case where the diffuse layer vanishes […]”
“The interlayer space is often considered to be completely free of anions […]”
I get annoyed by too much of such language in scientific
publications.
I argue that the only significant pore type in water saturated compacted bentonite is interlayers, by which I mean pores where the exchangeable cations reside (together with any other dissolved species). From this perspective it naturally follows that a homogeneous view is a suitable starting point for modeling compacted bentonite. I have presented, used, and discussed the homogeneous mixture model in many places on the blog, the main sources being
For reasons I can’t get my head around, a homogeneous view of
compacted bentonite is not the mainstream in contemporary
bentonite research. Instead we are stuck with
“the mainstream view”, which postulates several distinctly
different pore structures within the bentonite; in particular, the
mainstream view uses a bulk water phase as a starting point and also
distinguishes between “outer” and “inner” basal surfaces. Electric
double layers are assumed to only exist on “outer” surfaces, while
the function of the “inner” basal surfaces is mostly shrouded in
mystery.
On the blog I have also presented plenty of experimental support for a
homogeneous view. A main argument is that the conditions for swelling
pressure — the most profound feature of bentonite in equilibrium with an external solution — are essentially fulfilled automatically in the
homogeneous mixture model. The mainstream view, in contrast, requires
handling of the seemingly contradictory situation of having swelling
pressure while the water chemical potential is supposedly restored
without pressurization. Proponents of the mainstream view often deal
with this by simply ignoring swelling phenomena altogether.
I have also on the blog dissected several studies that argue for a
non-homogeneous view, but that actually provide evidence for the
opposite when examined more carefully. Consider in particular:
By systematically varying background concentration, material, and diffusing tracer, Glaus et al. (2007) clearly demonstrate, not only that the exchangeable cations are mobile, but that they dominate the flux in through-diffusion tests in highly compacted montmorillonite. While this certainly is an argument for that compacted bentonite is homogeneously structured, Glaus et al. (2007) still analyze their results from the perspective of the mainstream view, and do not — in my view — fully conclude what their results imply.
In particular they postulate the presence of an interlayer domain and a “free pore water” domain, and write for the “total” flux1 (their eq. 3)
where \(J_\mathrm{il}\) is a presumed diffusive flux in the interlayer domain and \(J_\mathrm{pw}\) is the presumed diffusive flux in the “free pore water” domain.
Their subsequent analysis shows that the measured flux in montmorillonite scales as
where \(C_\mathrm{bkg.}\) is the concentration of the background electrolyte (NaClO4), and \(Z\) is the charge number of the diffusing tracer (\(Z = 1\) for sodium and \(Z=2\) for strontium). Moreover, by considering ion exchange equilibrium, Glaus et al. (2007) show that also \(J_\mathrm{il}\) is expected to scale according to eq. 2. As they also confirm that this scaling behavior is not observed in systems without interlayer pores (kaolinite), they could have confidently concluded that their results imply that interlayers are the only significant pore structure in montmorillonite at these densities (as the title suggests).
Unfortunately, the discussion part of the article is considerably more tentative, focusing mainly on “interpretations” of the resulting flux
The present work shows that the interpretation of cation diffusion experiments in highly compacted swelling clays in terms of the concentration gradient in the aqueous phase may result in a nonsensical dependence of the effective diffusion coefficients on the salt concentration in the external aqueous phase. An alternative interpretation using an effective diffusion coefficient in the interlayer water (\(D_\mathrm{il}\)), being independent of the external salt concentration, with a corresponding concentration gradient in the interlayer water is more consistent with the experimental observations.
and the article ends on a quite apologetic note
The proposed interpretation should in turn not be blindly applied to
other experimental conditions. Diffusion of cations via the free
pore water may become increasingly important in swelling clays with
lower degrees of compaction or in clays in which the interlayer gel
pores are not that adjacent as they are in compacted
montmorillonite. In such cases, the assumption of
\(J_\mathrm{tot} \cong J_\mathrm{il}\) may no longer hold, and a
double-porous diffusion model would have to be applied in such
cases. The present concept may also reach its limits when dealing
with cations that rather sorb by surface complexation than by ion
exchange. Further work is therefore planned to extend the
investigations to such systems.
Given that the mainstream view to this day continues to be the default approach, one may think that this “further work” did show some convincing evidence for e.g. “diffusion of cations via the free pore water” at lower density. But what has actually been shown is that the “assumption of \(J_\mathrm{tot} \cong J_\mathrm{il}\)“ continues to be true for lower density!
Before we look at the additional results, we summarize the findings of
Glaus et al. (2007).
Findings in Glaus et al. (2007)
In the following we will consider the so-called “effective diffusion
coefficient”, here strictly defined as the experimental parameter
where \(j_\mathrm{ss}\) denotes the steady-state flux when an external
tracer concentration difference \(\Delta c^\mathrm{ext}\) is maintained
across a bentonite sample of length \(L\). We have
discussed through-diffusion and
the role of \(D_e\) in
many places on the blog, but in the present discussion we simply
view \(D_e\) as a normalized version the steady-state flux.
Note that we are required to compare diffusive fluxes in
different montmorillonite samples (an alternative test protocol
is suggested below). \(D_e\) varies both due to varying background
concentration (which is our object of study) and due to the variation
of different samples. It is thus crucial to minimize the latter type
of variation. This should be done (I suppose) by employing as
identical preparation protocols as possible. We will get back to this
complication of sorting out signal from noise as we comment the
results.
Glaus et al. (2007) present their results in diagrams where the logarithm of the evaluated quantities (diffusion parameters) is plotted against background concentration. This is of course convenient, as e.g. \(D_e\) can be expected to vary by two orders of magnitude as the background concentration is varied between 0.01 M and 1.0 M. But to remind ourselves what the actual dependency looks like between the normalized steady-state flux and background concentration, I will here insist on plotting the results in lin-lin diagrams.
The results for sodium in Glaus et al. (2007) plotted in lin-lin
diagrams, look like this (the data is the same in these three
diagrams)
We see that the data complies with the scaling law (eq. 2) and is quite well constrained (click on pictures to enlarge). \(D_e\) is evaluated in two ways in Glaus et al. (2007): by examining at the breakthrough curve, and by examining the internal tracer profile at test termination. These methods of evaluation give more or less identical results, with the exception of the test performed at 0.01 M background concentration. In this low concentration limit, the confining filters increasingly restrict the flux, making it difficult to extract actual clay transport parameters. We have discussed this issue (and this particular study) at length in a previous blog post.
Even with the problem of accurately measuring \(D_e\) at the lowest background concentration, the results clearly demonstrate the behavior of a homogeneous system (eq. 2): e.g., \(D_e\) undoubtedly increases by a factor of approximately 10 when the background concentration is lowered from 1.0 M to 0.1 M.
The data for strontium in Glaus et al. (2007) only covers the
background concentration interval 0.5 M — 1.0 M, and is consequently
less constrained, as seen here
This data also has the peculiarity that the diffusivity of samples of length 5.4 mm is almost twice as large as for samples of length 10.4 mm. This clearly demonstrates how sample preparation becomes crucial when conducting these types of tests. In the plots above, I have allowed myself to treat samples of different length separately (Glaus et al. (2007) use average values). It is clear from the data, that also strontium is compatible with the scaling law of eq. 2. In particular, it can be distinguished that sodium and strontium have different dependencies.
The take away message from these results is clear: montmorillonite at this density (1950 kg/m3) behave as a homogeneous system and shows no indication of containing additional pore structures.
Glaus et al. (2013) and NTB-17-12
After the publication of Glaus et al. (2007), corresponding results for lower densities has been presented. Glaus et al. (2013) — which is mostly recognized for demonstrating the seeming “uphill” diffusion effect — also contains measured \(D_e\) of sodium as a function of background concentration in conventional through-diffusion tests, both for density 1600 kg/m3 and 1300 kg/m3. These results are also published in more detail,2 together with new strontium results, in the NAGRA technical report NTB-17-12. We therefore look at these two publications together.
The additional data for sodium is here compared with the results from
Glaus et al. (2007)
For some of the additional tests, both through- and out-diffusion were performed. These points are labelled “TD” and “OD”, respectively, in the diagrams. We see that even for density as low as 1300 kg/m3, the data complies with the behavior of a homogeneous system (eq. 2) and is quite well constrained; in particular, there is nothing in the data for 1300 kg/m3 that suggests that these systems behave principally different than the 1950 kg/m3 samples.
For the system at 1300 kg/m3 and background concentration 0.1 M, two different values of \(D_e\) are presented in NTB-17-12. Only the lower of these values (\(7.0\cdot 10^{-10}\) m2/s) was published in Glaus et al. (2013), but NTB-17-12 presents a continued analysis that includes filter resistance, giving the value of \(D_e\) presented in the diagram. I think this is quite interesting, as the tests made at 0.1 M used “flushed” filters in order to minimize filter resistance. Apparently, filter resistance is still influential and it is not that easy to “design away” this problem.
NTB-17-12 also presents measured values of \(D_e\) for strontium under similar conditions (1300 — 1900 kg/m3, 0.1 — 1.0 M NaClO4 background), and are here compared with the earlier results
Although it naturally contains some scatter, we note that the additional data for ~1900 kg/m3 strengthens the earlier conclusion that also strontium scales in accordance with eq. 2. And just as for sodium, we see that the behavior does not qualitatively change, even for densities as low as 1300 kg/m3.
In the above diagrams are plotted single values for \(D_e\) for strontium at the lowest background concentration (0.1 M). It should be noted that these are burdened with large uncertainties as the transport restriction of the confining filters is severe; in NTB-17-12 are presented a whole set of simulations of the underlying flux evolution and concentration profiles with variations of the filter transport parameters. It is thus very clear that the problem of eliminating transport restrictions at the sample interfaces are not easy to completely eliminate. This is not surprising, as the theory suggests that \(D_e\) increases without limit with decreasing background concentration. Note that this behavior is strongly enhanced for divalent strontium; the measured values are many times larger than the corresponding diffusivity in bulk water (\(0.79\cdot 10^{-9}\) m2/s).
Even if the value of \(D_e\) is quite uncertain at the lowest background concentration, the mere observation that filter diffusivity strongly influence the process is, in a sense, itself a confirmation that the system still is governed by the behavior of interlayers.
The picture is quite clear from these findings: the combined results of Glaus et al. (2007), Glaus et al. (2013) and NTB-17-12 validates a homogeneous view of compacted bentonite, at essentially any relevant density!
The curious case of Bestel et al. (2018)
Bestel et al. (2018) further examine how \(D_e\) for sodium varies with background concentration. This publication shares some of the same authors with the previous studies, and presents additional measurements of \(D_e\) for sodium in essentially identical systems (similar preparation protocols, “Milos” montmorillonite, NaClO4 background electrolyte, flushed filters). Given the substantial evidence for homogeneous behavior collected in the publications discussed above, I find the conclusions of Bestel et al. (2018) rather odd.
Bestel et al. (2018) perform subsequent measurements of the steady-state flux in the same samples at different temperatures. The dependency of \(D_e\) on background concentration, however, looks essentially the same for each temperature, and — just as Bestel et al. (2018) — we here focus mainly on the results for 25 \(^\circ\mathrm{C}\). This data looks like this3
In their analysis, Bestel et al. (2018) include the results from Glaus et al. (2007) and Glaus et al. (2013), but treat them separately. They consequently conclude implicitly that, although the earlier studies found that \(D_e\) depends on background concentration in accordance with eq. 2, the new results show a different behavior. Specifically, they conclude that \(D_e\) scale with background concentration as \(C_\mathrm{bkg}^{-0.52}\) for density 1300 kg/m3 and as \(C_\mathrm{bkg}^{-0.76}\) for density 1600 kg/m3. Bestel et al. (2018) write
The results obtained in the present work for a broad variety of bulk
dry densities of Na-montmorillonite and concentrations of the
background electrolyte, give clear evidence that the equilibrium
distribution of cations between the clay phase and the external
aqueous phase is the main parameter influencing the observed overall
diffusive fluxes of cations. Whether the observed overall diffusive
fluxes are described by a physical subdivision of the pore space
into domains containing different species (e.g. the model proposed
in Appelo and Wersin (2007) or Bourg et al. (2007)), or whether
they are the result of the concentration gradients of such species
in a single type of pore (e.g. the model proposed by Birgersson and
Karnland (2009)), cannot be decided unambiguously from the available
data — notably because of the wide similarity of the model
predictions and because of some internal inconsistencies in the
experimental data. Both types of models would require some
adjustments in order to fully match the data. The diffusion data of
\(^{22}\mathrm{Na}^+\) can equally be described by a surface diffusion
model with a reduced, but non-zero mobility of sorbed cations,
similar to the median value determined in Gimmi and Kosakowski
(2011).
I think this is a problematic way of arguing and presenting data.
The data obviously has scatter
To begin with, why are the results from this study and the ones from Glaus et al. (2007) and Glaus et al. (2013) treated separately? When treated separately — according to Bestel et al. (2018) — these results are vaguely supposed to be incompatible: the dependence of \(D_e\) either comply with eq. 2 or it does not. I think that the appropriate thing to do is to discuss possible causes for why the new results supposedly differ from the earlier ones. As we have made clear above, all factors that determine \(D_e\) are not fully controlled in tests like these (e.g, what causes the difference in diffusivity for strontium in 5.4 mm and 10.4 mm samples, respectively, in Glaus et al. (2007)?). We have also seen that it is difficult to make accurate measurements at low enough background concentration, even with flushed filters.
Look e.g. at the specific values of \(D_e\) at background concentrations 1.0 and 0.1 M, respectively, in NTB-17-12 and Bestel et al. (2018) (unit is m2/s).
Under ideal conditions, these values would not differ for the same conditions in the two studies. The scatter of these values is moreover quite random, e.g. one study do not have values that are systematically larger than in the other. In Bestel et al. (2018) we also see that the mere disturbance of a sample in form of a temperature pulse may alter the diffusivity significantly (temperature is first increased in steps from 25 \(^\circ\mathrm{C}\) to 80 \(^\circ\mathrm{C}\), then decreased in steps to 0 \(^\circ\mathrm{C}\), and finally increased again to 25 \(^\circ\mathrm{C}\)). In e.g. one sample of density 1600 kg/m3 and background concentration 0.1 M is reported \(D_e = 3.4\cdot 10^{-10}\) m2/s at 25 \(^\circ\mathrm{C}\) before the conducted temperature changes, and \(2.3\cdot 10^{-10}\) m2/s after. One should also consider that the samples are not prepared equally, as they are saturated directly with the corresponding background solution. (This is also true for the previous studies.) Could this cause differences in diffusivity?
Bestel et al. (2018) should thus either argue for why the new results
are more accurate (or why the results of Glaus et al. (2007, 2013)
are less accurate) or treat the data from all studies in accumulation
and admit substantial experimental uncertainty. My impression is that
Bestel et al. (2018) make a little of both.
The data still complies with a homogeneous view
Looking at the aggregated sodium data, a somewhat different picture
emerges
Here is also included a model labelled “Full Donnan”, which takes into account the excess salt that is expected to enter the interlayers. For all other samples we have discussed, this contribution is only minor and can be neglected, and this assumption underlies eq. 2. For the sample of density 1300 kg/m3 with background concentration 5.0 M, however, the excess salt is not negligible and must be included in the analysis of the behavior of a homogeneous system (the deviation from eq. 2 is seen to become significant around 1.0 M background concentration). Bestel et al. (2018) actually present a full Donnan calculation for the excess salt, but, for unknown reasons, do not compare it directly with the experimental results (it is plotted in a separate diagram next to the data).
For 1300 kg/m3, I would claim that the “Full Donnan” model fits better to the accumulated data than the scaling law suggested in Bestel et al. (2018) (exponent \(-0.52\)). For 1600 kg/m3, the suggested scaling law (exponent \(-0.76\)) indeed fits better to the data than eq. 2, but the data is not that well constrained. To use this singular result to argue for a non-homogeneous bentonite structure basically boils down to claiming that the values measured at 0.1 M — a concentration range that is documented to be difficult to measure accurately — could not possibly be underestimated by, say, 50% (while also ignoring all other results).
If we also consider the results for strontium presented in NTB-17-12, I mean that the only reasonable conclusion that Bestel et al. (2018) can draw is that the results comply with a homogeneous bentonite structure.
Additional model components should not be motivated
solely by the ability of a model to be fitted to some arbitrary
data
A major motivation for measuring how \(D_e\) depends on background concentration at lower densities, according to Glaus (2007), is that “the assumption of \(J_\mathrm{tot} \cong J_\mathrm{il}\) may no longer hold”. What (I mean) has been demonstrated in the subsequent studies is that this assumption actually does hold. In particular, from the aggregated data it is not possible to claim that the behavior of \(D_e\) is qualitatively different at 1950 kg/m3 and 1300 kg/m3. Thus, there is no valid justification for introducing more complex model components. Moreover, introducing e.g. a bulk water phase causes fundamental conflicts with the description of other well-established properties of these systems, particularly swelling pressure. Adding such components merely to improve agreement with a specific dataset, while ignoring their broader implications, undermines the model’s overall coherence and validity. The data cannot“equally be described by a surface diffusion model”.
What does some alternative model actually predict?
Eq. 2 (or a full Donnan calculation) is a clean statement of the expected behavior of a homogeneous system (based on how interlayers function). If actual deviations from this behavior could be established we may conclude that a homogeneous description is not sufficient. However, any arbitrary deviation from eq. 2 does not automatically validate any specific alternative model. Validating a model requires that we can experimentally reproduce some of its non-trivial predictions. Bestel et al. (2018) don’t discuss what the exponents \(-0.52\) and \(-0.76\) are suppose to represent.
Note also that the arbitrary exponent \(-0.52\) is inferred by fitting to the data at 5.0 M background concentration. But we saw above that a full Donnan calculation within the homogeneous view actually explains the behavior in this concentration limit (Bestel et al. (2018) show this!). We have thus every reason to believe that the exponent \(-0.52\) is just spurious and do not represent some actual physical mechanisms.
A suggestion for how to preferably conduct these types of tests
The discussed studies are enough to convince me that cation tracer
diffusion behave in accordance with a homogeneous bentonite view at
any relevant density.
It is however also clear that the full variation of \(D_e\) in these
tests is caused by more factors than just background concentration and
density. To eliminate as much as possible of this scatter — and thus
to more accurately determine the dependence of background
concentration on \(D_e\) — I suggest the following test protocol.
Measure tracer flux at several background concentrations in the same sample.
This would eliminate both the unavoidable (small) variation in density between different samples as well as several unknown factors that determine the exact value of diffusivity (these may e.g. be related to variation in material or equipment and to sample handling)
Prepare samples by saturating them all with the same low concentration solution (e.g. 0.05 M).
To me it seems reasonable that the way samples saturate may influence the resulting detailed structure and thus the diffusivity. Saturating all samples in the same manner with the same solution will minimize variations from such effects.
Keep temperature constant.
I don’t think this is a crucial factor, but we see in Bestel et al. (2018) that larger temperature pulses may significantly alter the diffusivity.
Increase background concentration in steps and record the steady-state flux at each concentration.
I think a good range may be between 0.2 M and 1.0 M. For a homogeneous system, this corresponds to a variation in \(D_e\) by a factor 5 for monovalent and 25 for divalent cations.5 At the same time, the problem of filter transport resistance can hopefully be kept under control.
Decrease the background concentration (perhaps in steps) back to the first concentration where steady-state flux was measured.
Measure steady-state flux again and assert that no significant change in \(D_e\) has occurred as a consequence of the disturbance introduced by the background concentration pulse.
Final thoughts
The only reasonable conclusion to draw from the studies we have looked
at is that the behavior of cation tracer diffusion indicates a
relatively homogeneous structure, dominated by interlayers, in any
relevant bentonite system. Despite this, the contemporary scientific
bentonite literature is crammed with non-homogeneous descriptions of
compacted bentonite, centered around a bulk water phase (the
“mainstream view”). As we have seen here, this can even be the case
for studies that provide evidence for homogeneity.
What I find most frustrating is that interlayer effects often are viewed as some additional feature to be handled in specific cases. In reality, virtually all experimental findings (diffusion, swelling pressure, temperature response, Donnan effects, fluid flow, hyperfiltration, …) indicate that the behavior of compacted bentonite is fully governed by interlayers. The question is not if a presumed bulk water phase may dominate under certain conditions, but if such a phase is at all relevant. I want to emphasize this point: up until this day, no convincing evidence has ever been presented that compacted bentonite contains significant amounts of bulk water.
Even if the structure becomes more complex at lower densities, a
homogeneous model centered around interlayers guarantees to cover at
least some aspects of the system. On the contrary — if the goal is
process understanding — most experimental evidence rules out
bentonite models that assume a bulk water phase.
[2] As
far as I can see, these tests were done in duplicates for Na
diffusion with background concentrations 0.5 M and 1.0 M, and the
the numbers reported in Glaus et al. (2013) are averages.
[3] Bestel et
al. (2018) use a normalization scheme in their analysis that
involves corresponding measured water diffusivities and parameters
from “Archie’s law” (note, it is the
quotation marks version of the law). I think this handling makes
the presented results less transparent, and here we use the actual
reported values of \(D_e\).
[4] These are only values from the first phase at 25 \(^\circ\mathrm{C}\).
[5] I assume that measurements are being made in pure Na-montmorillonite.
The paper follows a structure recognizable from several others that we have considered previously on the blog: It starts off with an introduction section containing several incorrect or unfounded statements1 regarding bentonite.2 It then presents some experimental results that makes it evident that no real progress has been made for a long time regarding e.g. experimental design.3 The major part of the paper is devoted to a “results and discussion” section with several incorrect statements and inferences, speculation, and irrelevant modeling.
[I]nfluence of a background electrolyte concentration gradient on the diffusion of anionic and cationic species at trace concentrations has […] been rarely investigated. Notable exceptions are the DR-A in situ diffusion experiment conducted at the Mont-Terri laboratory (Soler et al., 2019), and an “uphill” diffusion experiment of a \(^{22}\mathrm{Na}^+\) tracer in a compacted sodium montmorillonite (Glaus et al., 2013). These two studies demonstrated the marked influence of background electrolyte concentration gradient on tracer diffusion, and thus the necessity to understand the couplings between diffusion of several charged species present at contrasting concentrations and experiencing different concentration gradients. The experiment from Glaus et al. (2013) also demonstrated the importance of considering diffusion processes occurring in the porosity next to the charged surface of clay minerals (i.e., the porosity associated to the EDL of particles).
This quotation contains two statements relating to Glaus et al. (2013), both of which I think are problematic4
It basically claims that the “uphill” phenomenon is due to diffusive couplings between several types of ions. Of course, ion diffusion always involves couplings between different types of ions, due to the requirement of electroneutrality. But it is clear that Tertre et al. (2024) mean that the “uphill” effect is caused by additional couplings that are not present in chemically homogeneous systems.
It says that Glaus et al. (2013) demonstrates the importance to consider diffuse layers. I agree with this, but it is written in a way that implies that there also are other relevant “porosities”, and that there are other types of tests where ion diffusion in bentonite is not significantly influenced by the presence of diffuse layers.
As one of the authors of the “uphill” study, I would here like to argue for why I think the above statements are problematic and give some background context.
The “uphill” diffusion experiment
The “uphill” study actually originated from a prediction presented by me in a conference poster session. This poster discussed the role of the quantity \(D_e\), using the exact same theory that we had previously used to explain the diffusive behavior of tracer ions in compacted bentonite as an effect of Donnan equilibrium in a homogeneous system. In particular, it pointed out that \(D_e\) — although universally referred to as the (effective) “diffusion coefficient” — is not a diffusion coefficient in the context of compacted bentonite. I have continued this discussion in laterpapers, and in several postson this blog.
In the poster, we suggested the “uphill” experiment as a demonstration of the shortcoming of \(D_e\). If the two reservoirs in a through-diffusion test are maintained at different background concentrations, the theory predicts a non-zero tracer flux for a vanishing external tracer concentration difference, i.e. an “infinite” value of \(D_e\). The suggestion caught the interest of an experimental group, and after a successful collaboration we could present the results of an actual “uphill” experiment. Without making too much of an exaggeration, I would say that the results of this experiment were basically exactly as predicted.
Given this background, it should be clear that the tests in Glaus et al. (2013) follow exactly the same rules as tests in chemically homogeneous systems, rather than demonstrating “the necessity to understand the couplings between diffusion of several charged species present at contrasting concentrations”. Although it is quite clearly stated already in the abstract in Glaus et al. (2013), there is apparently still a need to communicate this explanation. Let me therefore try that here.
The “uphill” diffusion phenomenon explained
Consider an ordinary aqueous solution containing radioactive \(^{22}\mathrm{Na}\) and stable \(^{23}\mathrm{Na}\). The fraction of \(^{22}\mathrm{Na}\) ions can be written \(c_\mathrm{ext}/C_\mathrm{bkg}\), where \(c_\mathrm{ext}\) is the \(^{22}\mathrm{Na}\) concentration, and \(C_\mathrm{bkg}\) is the total sodium concentration (the “tracer” and “background” concentrations, respectively).
Since \(^{23}\mathrm{Na}\) and \(^{22}\mathrm{Na}\) are basically chemically indistinguishable, the same \(^{22}\mathrm{Na}\)-fraction will be maintained in any system with which this solution is in equilibrium. In particular, if the solution is in equilibrium with a montmorillonite interlayer solution, we can write
where \(c_\mathrm{int}\) and \(C_\mathrm{int}\) are the \(^{22}\mathrm{Na}\) and total interlayer concentrations, respectively. The total interlayer cation concentration (\(C_\mathrm{int}\)) can be handled in different ways, but it is important to note that this is a substantial number under all conditions, relating to the cation exchange capacity.5 Rearranging eq. 1 gives
Since the interlayer cation concentration is always larger than the corresponding background concentration, the above equation tells us that the corresponding interlayer tracer concentration becomes enhanced, by the factor \(C_\mathrm{int}/C_\mathrm{bkg}\).
Conventional through-diffusion
This enhancement mechanism causes the diffusional behavior of \(^{22}\mathrm{Na}\) in conventional through-diffusion experiments in bentonite. In such experiments, the tracer concentration in the target reservoir is usually kept near zero, and the actual steady-state concentration gradient in the interlayers is
where we have indexed the tracer concentration in the source reservoir with “\((1)\)”, labeled the sample length \(L\), and assumed that ions diffuse in the \(x\)-direction. The corresponding flux is thus (Fick’s law)
where \(D_c\) denotes the (macroscopic) diffusivity in the interlayers, and \(\phi\) is porosity. Keeping \(c_\mathrm{ext}^{(1)}\) constant, eq. 2 shows that the \(^{22}\mathrm{Na}\) steady-state flux increases indefinitely as the background concentration is made small, in full agreement with experimental observation.6
The picture below illustrates the concentration conditions in an conventional through-diffusion test.
Here we have chosen \(C_\mathrm{int}=\) 4.0 M, the background concentration in the two reservoirs (blue) is put equal to 0.1 M, and the tracer concentration (orange) is put to 0.1 mM in reservoir 1 (and zero i reservoir 2). The corresponding internal tracer gradient is plotted in the right side diagram, and the resulting diffusive flux is indicated by the arrow.
“Uphill” diffusion
To explain the “uphill” effect the only modifications needed in the above derivation is to allow for different background concentrations in the external reservoirs, and to recognize that the tracer concentration in the clay on the “target” side (indexed “\((2)\)”) no longer is zero. Considering the tracer concentration enhancement at both interfaces, the steady-state interlayer concentration gradient then reads
To be more concrete, let’s assume that \(C_\mathrm{bkg}^{(2)} = 5\cdot C_\mathrm{bkg}^{(1)}\), which is the same ratio as in Glaus et al. (2013). We then have
Note that we recover the conventional through-diffusion result (eq. 2) from this expression, if we put \(c_\mathrm{ext}^{(2)}= 0\). But if we e.g. set the tracer concentration equal in both reservoirs, we still have a flux from side \((1)\) to side \((2)\), of size \(j = 4/5 \cdot \phi D_c\cdot C_\mathrm{int}/C_\mathrm{bkg}^{(1)}\cdot c_\mathrm{ext}^{(1)}\). And even if we make \(c_\mathrm{ext}^{(2)}\) larger than \(c_\mathrm{ext}^{(1)}\) — as long as \(c_\mathrm{ext}^{(1)}< c_\mathrm{ext}^{(2)} < 5\cdot c_\mathrm{ext}^{(1)}\) — we still have a diffusive flux from side \((1)\) to side \((2)\), i.e seeming “uphill” diffusion.
Below is illustrated the concentration conditions in an “uphill” configuration.
In contrast to the above illustration for conventional through-diffusion, the background concentration in reservoir 2 is here raised to 0.5 M and the tracer concentration in reservoir 2 is put equal to 0.2 mM. We see that, although tracers are transported to the reservoir with higher concentration, the process is still ordinary Fickian diffusion, as the internal tracer gradient has the same direction as in the conventional case.
We can now conclude what was stated above: The “uphill” diffusion effect is caused by exactly the same mechanism that cause the behavior of cation diffusion in conventional bentonite through-diffusion tests. This mechanism is ion equilibrium between clay and external solutions at the two interfaces. In this particular case, with sodium tracers diffusing in a sodium background, we don’t need to invoke the full ion equilibrium framework in order to quantify the fluxes, but can rely on the very robust result that any two systems in equilibrium have the same tracer fraction (eq. 1).
Reexamining the Tertre et al. (2024) statements
With the explanation for the “uphill” effect established, let’s re-examine the problematic statements in Tertre et al. (2024) identified above
Glaus et al. (2013) cannot be used to support a claim of “marked influence” of additional diffusional couplings. The opposite is true: Glaus et al. (2013) found no significant influence from mechanisms beyond those in chemically homogeneous conditions.
The “uphill” effect was predicted from taking the idea seriously that diffusion in compacted bentonite is fully governed by interlayer properties. Singling out Glaus et al. (2013) as the study that demonstrates the importance of diffuse layers7 therefore gives the wrong impression. Rather, what Glaus et al. (2013) demonstrates, in conjunction with corresponding conventional through-diffusion results, is that compacted bentonite contains insignificant amounts of bulk water (what Tertre et al. (2024) call “interparticle water”).
A way forward (if anybody cares)
After the uphill study was published I was for a while under the illusion that things would begin to change within the compacted bentonite research field. Not only did the study, to my mind, deal a fatal blow to any bentonite model that relies on the presence of a bulk water phase in the clay. It also opened up a whole new area of interesting studies to conduct. Now, some 11 years later, I can disappointingly conclude that not a single additional study has been presented that explore the ideas here discussed.8 And, regarding bentonite models, bulk water is apparently alive and kicking, as has been discussed ad nauseum on this blog.
Experimentally, there are a number of interesting questions looking for answers. In particular, we actually do expect additional mechanisms to play a role in chemically inhomogeneous systems, e.g. osmosis, and other effects due to presence of salt concentration gradients and electrostatic potential differences. It may be argued for why such effects are not significant in Glaus et al. (2013), but it is of course both of fundamental and practical interest to understand under which conditions they are. The original “uphill” study is e.g. performed at quite extreme density (\(1900\;\mathrm{m^3/kg}\)). How would the result differ at \(1600\;\mathrm{m^3/kg}\) or \(1300\;\mathrm{m^3/kg}\)? Also, how would the results change with other choices of the reservoir concentrations, and how would the results differ if one of the cations is not at trace level (e.g. a system with comparable amounts of sodium and potassium)?
Even under the conditions of the original study, there are several predictions left to verify. If e.g. \(c^{(1)}_\mathrm{ext} = c^{(2)}_\mathrm{ext}/5\), the theory predicts zero flux (implying \(D_e = 0\)). The theory also implies that when performing “conventional” through-diffusion, the actual level of the background concentration in the target reservoir is irrelevant, as long as the tracer concentration is kept at zero.
In fact, one can imagine making a whole cycle of through-diffusion tests to explore the ideas here discussed, as illustrated in this animation
The resulting steady-state flux for various external conditions is indicated by the arrow. Here, the full ion equilibrium framework was used to calculate the internal concentrations (giving an internal gradient also in \(C_\mathrm{int}\)). Background concentrations and total interlayer concentration is chosen to be comparable with Glaus et al. (2013), while the choice for tracer concentration is arbitrary.9
With the risk of sounding hubristic, the number of experiments suggested in the above animation could have given enough material for several Ph.D. theses. But here we are, in the year 2024, without even a replication of the “uphill” effect. Instead, a basically entire research field has been stuck for decades with the ludicrous idea that models of compacted bentonite should be based on a bulk water description. I find this both hilarious and horrific.
Footnotes
[1] For example (follow links to discussions on these issues):
Related to using the traditional diffusion-sorption model, it assumes \(D_e\) to be a real diffusion coefficient, which it is not. I find this particularly remarkable in a paper that deals with the presence of “saline gradients”. A motivation behind e.g. the “uphill” test is to point out the shortcomings of \(D_e\), as discussed in the rest of this blog post.
It claims that “anionic and cationic tracers do not experience the same overall accessible porosity”, which is unjustified.
It claims that “diffusion rates” of anions are decreased and “diffusion rates” of cations are increased, compared to “neutral species”, due to different interactions with diffuse layers. But this is not true generally.
[2] I use the word “bentonite” here quite loosely. Tertre et al. (2024) use wordings such as “clayey samples”, “argillaceous rocks” and “clayey formation”, but it is clear that the presented material is supposed to apply to actual bentonite.
[3] I’m specifically thinking about that cation tracer through-diffusion tests at low background concentration is not a good idea, and that it is completely clear from the results presented in Tertre et al. (2024) that some of these are mainly controlled by diffusion in the confining filters. Estimating a “rock capacity factor” larger than 750 for sodium tracers in a sodium-clay (at 20 mM background concentration) should have set off all alarm bells.
[4] Regarding Soler et al. (2019), I think that whole study is problematic, which I might argue for in a separate blog post.
[5] Glaus et al. (2013) invoke the “exchange site” activity \([\mathrm{NaX}]\) to discuss this quantity. I personally prefer relating it to the quantity \(c_\mathrm{IL}\) that is defined within the homogeneous mixture model.
[6] This agreement has been shown to be quantitative, see e.g. Glaus et al. (2007), Birgersson and Karnland (2009) and Birgersson (2017). Note that this result is quite independent of how many “porosities” you choose to include in a model; it’s merely a consequence of treating the dominating pores (interlayers) adequately. Further, note that measuring the diverging fluxes in the limit of low background concentration becomes increasingly difficult, as the confining filters becomes rate limiting.
[7] In the present context, I presume the terms “diffuse layer” and “interlayer” to be more or less equivalent. Other authors instead make an unjustified distinction, that I have addressed here.
[8] There are afewexamples of published studies where effects of the kind discussed here are present, but where the authors don’t seem to be aware of it.
[9] Tracer concentrations in Glaus et al. (2013) is much smaller, but this value does not affect any behavior, as long as it is small in comparison with total concentration.
To celebrate that I have built myself a tool for solving the Poisson-Boltzmann equation for two parallel charged plates and specified external solution, here is a cosy little animation
The animation shows the anion concentration profile (blue) between the plates as the distance varies, in systems in equilibrium with an external 100 mM 1:1 salt solution. Also plotted is the corresponding internal concentration level as calculated from the ideal Donnan equilibrium formula (orange). The layer charge density in the Poisson-Boltzmann calculation is 0.111 C/m2, and the corresponding cation exchange capacity in the Donnan calculation is 0.891 eq/kg.
As the distance between the plates increases, the Poisson-Boltzmann profile increasingly deviates from the Donnan concentration. At lower density (larger plate distance) it is clear that the Poisson-Boltzmann solution allows for considerably more anions between the plates as compared with the Donnan result. On the other hand, for denser systems, the difference between the two solutions decreases; this is especially true when considering the relative difference — keep in mind that the external concentration is kept constant, at 100 mM.
In fact, in systems relevant for e.g. radioactive waste storage — spanning an effective montmorillonite density range from \(\rho_\mathrm{mmt} =\) 1.60 g/cm3 to \(\rho_\mathrm{mmt} =\) 1.15 g/cm3, say — the difference between the Poisson-Boltzmann and the Donnan results is virtually negligible (it should also be kept in mind that the continuum assumption underlying the Poisson-Boltzmann calculation is not valid in this density range). Here are plotted snapshots of these two limiting cases, together with the Poisson-Boltzmann solution for a single plate (the Gouy-Chapman model)
“Multi-porosity” models1 — i.e
models that account for both a bulk water phase and one, or several,
other domains within the clay — have become increasingly
popular in bentonite research during the last couple of decades. These
are obviously macroscopic, as is clear e.g. from the benchmark
simulations described in
Alt-Epping et
al. (2015), which are specified to be discretized into 2 mm thick
cells; each cell is consequently assumed to contain billions and
billions individual montmorillonite particles. The macroscopic
character is also relatively clear in their description of two
numerical tools that have implemented multi-porosity
PHREEQC and CrunchFlowMC have implemented a Donnan approach to describe the electrical potential and species distribution in the EDL. This approach implies a uniform electrical potential \(\varphi^\mathrm{EDL}\) in the EDL and an instantaneous equilibrium distribution of species between the EDL and the free water (i.e., between the micro- and macroporosity, respectively). The assumption of instantaneous equilibrium implies that diffusion between micro- and macroporosity is not considered explicitly and that at all times the chemical potentials, \(\mu_i\), of the species are the same in the two porosities
On an abstract level, we may thus illustrate a multi-porosity approach
something like this (here involving two domains)
The model is represented by one
continuum for the “free water”/”macroporosity” and one for the
“diffuse layer”/”microporosity”,2 which are
postulated to be in equilibrium within each macroscopic cell.
But such an equilibrium (Donnan equilibrium)
requires a
semi-permeable component. I am not aware of any suggestion for such
a component in any publication on multi-porosity
models. Likewise, the co-existence of diffuse layer and free water
domains requires
a mechanism that prevents swelling and maintains the pressure
difference — also the water chemical potential should of course be
the equal in the two “porosities”.3
Note that the questions of what constitutes the semi-permeable
component and what prevents swelling have a clear answer in
the homogeneous mixture model. This answer also corresponds to an
easily identified real-world object: the metal filter (or similar
component) separating the sample from the external solution.
Multi-porosity models, on the other hand, attribute no particular
significance to interfaces between sample and external
solutions. Therefore, a candidate for the semi-permeable component has
to be — but isn’t — sought elsewhere. Donnan equilibrium
calculations are virtually meaningless without identifying this
component.
The partitioning between diffuse layer and free water in
multi-porosity models is, moreover, assumed to be controlled by water
chemistry, usually by means of the
Debye length. E.g. Alt-Epping et al. (2015) write
To determine the volume of the microporosity, the surface area of montmorillonite, and the Debye length, \(D_L\), which is the distance from the charged mineral surface to the point where electrical potential decays by a factor of e, needs to be known. The volume of the microporosity can then be calculated as \begin{equation*} \phi^\mathrm{EDL} = A_\mathrm{clay} D_L, \end{equation*} where \(A_\mathrm{clay}\) is the charged surface area of the clay mineral.
I cannot overstate how strange the multi-porosity description
is. Leaving the abstract representation, here is an attempt to
illustrate the implied clay structure, at the “macropore” scale
The view emerging from the above description is actually even more
peculiar, as the “micro” and “macro” volume fractions are supposed
to vary with the Debye length. A more general illustration of how the
pore structure is supposed to function is shown in this animation
(“I” denotes ionic strength)
What on earth could constitute such magic semi-permeable membranes?!
(Note that they are also supposed to withstand the inevitable pressure
difference.)
Here, the informed reader may object and point out that no researcher
promoting multi-porosity has this magic pore structure in
mind. Indeed, basically all multi-porosity publications instead
vaguely claim that the domain separation occurs on the nanometer scale
and present microscopic illustrations, like this (this is a
simplified version of what is found in
Alt-Epping et
al. (2015))
In the remainder of this post I will discuss how the idea of a domain separation on the microscopic scale is even more preposterous than the magic membranes suggested above. We focus on three aspects:
The implied structure of the free water domain
The arbitrary domain division
Donnan equilibrium on the microscopic scale is not really a valid concept
Implied structure of the free water domain
I’m astonished by how little figures of the microscopic scale are
explained in many publications. For instance, the illustration above
clearly suggests that “free water” is an interface region with
exactly the same surface area as the “double layer”. How can that
make sense? Also, if the above structure is to be taken seriously it
is crucial to specify the extensions of the various water layers. It
is clear that the figure shows a microscopic view, as it depicts an
actual diffuse layer.4 A diffuse layer width varies, say, in the
range 1 – 100 nm,5 but authors seldom reveal if we are
looking at a pore 1 nm wide or several hundred nm wide. Often we are
not even shown a pore — the water film just ends in a void, as in the
above figure.6
The vague nature of these descriptions indicates that they are merely “decorations”, providing a microscopic flavor to what in effect still is a macroscopic model formulation. In practice, most multi-porosity formulations provide some ad hoc mean to calculate the volume of the diffuse layer domain, while the free water porosity is either obtained by subtracting the diffuse layer porosity from total porosity, or by just specifying it. Alt-Epping et al. (2015), for example, simply specifies the “macroporosity”
The total porosity amounts to 47.6 % which is divided into 40.5 % microporosity (EDL) and 7.1 % macroporosity (free water). From the microporosity and the surface area of montmorillonite (Table 7), the Debye length of the EDL calculated from Eq. 11 is 4.97e-10 m.
Clearly, nothing in this description requires or suggests that the
“micro” and “macroporosities” are adjacent waterfilms on the
nm-scale. On the contrary, such an interpretation becomes quite
grotesque, with the “macroporosity” corresponding to half a
monolayer of water molecules! An illustration of an actual pore of
this kind would look something like this
This interpretation becomes even more bizarre, considering that
Alt-Epping et
al. (2015) assume advection to occur only in this half-a-monolayer
of water, and that the diffusivity is here a factor 1000 larger than
in the “microporosity”.
As another example, Appelo
and Wersin (2007) model a cylindrical sample of “Opalinus clay”
of height 0.5 m and radius 0.1 m, with porosity 0.16, by discretizing
the sample volume in 20 sections of width 0.025 m. The void volume of
each section is consequently
\(V_\mathrm{void} = 0.16\cdot\pi\cdot 0.1^2\cdot 0.025\;\mathrm{m^3} =
1.257\cdot10^{-4}\;\mathrm{m^3}\). Half of this volume (“0.062831853”
liter) is specified directly in the input file as the volume of the
free water;7 again, nothing suggests that this water
should be distributed in thin films on the nm-scale. Yet,
Appelo and Wersin (2007)
provide a figure, with no length scale, similar in spirit to that
above, that look very similar to this
They furthermore write about this figure (“Figure 2”)
It should be noted that the model can zoom in on the nm-scale suggested by Figure 2, but also uses it as the representative form for the cm-scale or larger.
I’m not sure I can make sense of this statement, but it seems that they imply that the illustration can serve both as an actual microscopic representation of two spatially separated domains and as a representation of two abstract continua on the macroscopic scale. But this is not true!
Interpreted macroscopically, the vertical dimension is fictitious, and
the two continua are in equilibrium in each paired cell. On a
microscopic scale, on the other hand, equilibrium between paired cells
cannot be assumed a priori, and it becomes crucial to specify
both the vertical and horizontal length scales. As
Appelo and Wersin (2007)
formulate their model assuming equilibrium between paired cells, it is
clear that the above figure must be interpreted macroscopically (the
only reference to a vertical length scale is that the “free
solution” is located “at infinite distance” from the surface).
We can again work out the implications of anyway interpreting the model microscopically. Each clay cell is specified to contain a surface area of \(A_\mathrm{surf}=10^5\;\mathrm{m^2}\).8 Assuming a planar geometry, the average pore width is given by (\(\phi\) denotes porosity and \(V_\mathrm{cell}\) total cell volume)
The double layer thickness is furthermore specified to be 0.628 nm.9 A microscopic interpretation of this particular model thus implies that the sample contains a single type of pore (2.51 nm wide) in which the free water is distributed in a thin film of width 1.25 nm — i.e. approximately four molecular layers of water!
Rather than affirming that multi-porosity model formulations are macroscopic at heart, parts of the bentonite research community have instead doubled down on the confusing idea of having free water distributed on the nm-scale. Tournassat and Steefel (2019) suggest dealing with the case of two parallel charged surfaces in terms of a “Dual Continuum” approach, providing a figure similar to this (surface charge is -0.11 C/m2 and external solution is 0.1 M of a 1:1 electrolyte)
Note that here the perpendicular length scale is specified,
and that it is clear from the start that the electrostatic potential
is non-zero everywhere. Yet,
Tournassat and Steefel
(2019) mean that it is a good idea to treat this system as if it
contained a 0.7 nm wide bulk water slice at the center of the
pore. They furthermore express an almost “postmodern” attitude
towards modeling, writing
It should be also noted here that this model refinement does not imply necessarily that an electroneutral bulk water is present at the center of the pore in reality. This can be appreciated in Figure 6, which shows that the Poisson–Boltzmann predicts an overlap of the diffuse layers bordering the two neighboring surfaces, while the dual continuum model divides the same system into a bulk and a diffuse layer water volume in order to obtain an average concentration in the pore that is consistent with the Poisson–Boltzmann model prediction. Consequently, the pore space subdivision into free and DL water must be seen as a convenient representation that makes it possible to calculate accurately the average concentrations of ions, but it must not be taken as evidence of the effective presence of bulk water in a nanoporous medium.
I can only interpret this way of writing (“…does not imply
necessarily that…”, “…must not be taken as evidence of…”)
that they mean that in some cases the bulk phase should be
interpreted literally, while in other cases the bulk phase
should be interpreted just as some auxiliary component. It is my
strong opinion that such an attitude towards modeling only contributes
negatively to process understanding (we may e.g. note that later in
the article, Tournassat
and Steefel (2019) assume this perhaps non-existent bulk water to
be solely responsible for advective flow…).
I say it again: no matter how much researchers discuss them in microscopic terms, these models are just macroscopic formulations. Using the terminology of Tournassat and Steefel (2019), they are, at the end of the day, represented as dual continua assumed to be in local equilibrium (in accordance with the first figure of this post). And while researchers put much effort in trying to give these models a microscopic appearance, I am not aware of anyone suggesting a reasonable candidate for what actually could constitute the semi-permeable component necessary for maintaining such an equilibrium.
Arbitrary division between diffuse layer and free water
Another peculiarity in the multi-porosity descriptions showing that they cannot be interpreted microscopically is the arbitrary positioning of the separation between diffuse layer and free water. We saw earlier that Alt-Epping et al. (2015) set this separation at one Debye length from the surface, where the electrostatic potential is claimed to have decayed by a factor of e. What motivates this choice?
Most publications on multi-porosity models define free water as a region where the solution is charge neutral, i.e. where the electrostatic potential is vanishingly small.10 At the point chosen by Alt-Epping et al. (2015), the potential is about 37% of its value at the surface. This cannot be considered vanishingly small under any circumstance, and the region considered as free water is consequently not charge neutral.
The diffuse layer thickness chosen by Appelo and Wersin (2007) instead corresponds to 1.27 Debye lengths. At this position the potential is about 28% of its value at the surface, which neither can be considered vanishingly small. At the mid point of the pore (1.25 nm), the potential is about 8%11 of the value at the surface (corresponding to about 2.5 Debye lengths). I find it hard to accept even this value as vanishingly small.
Note that if the boundary distance used by Appelo and Wersin (2007) (1.27 Debye lengths) was used in the benchmark of Alt-Epping et al. (2015), the diffuse layer volume becomes larger than the total pore volume! In fact, this occurs in all models of this kind for low enough ionic strength, as the Debye length diverges in this limit. Therefore, many multi-porosity model formulations include clunky “if-then-else” clauses,12 where the system is treated conceptually different depending on whether or not the (arbitrarily chosen) diffuse layer domain fills the entire pore volume.13
In the example from Tournassat and Steefel (2019) the extension of the diffuse layer is
1.6 nm, corresponding to about 1.69 Debye lengths. The potential is
here about 19% of the surface value (the value in the midpoint is
12% of the surface
value). Tournassat
and Appelo (2011) uses yet another separation distance — two Debye
lengths — based on
misusing the concept of exclusion volume in the Gouy-Chapman model.
With these examples, I am not trying to say that a better criterion is needed for the partitioning between diffuse layer and bulk. Rather, these examples show that such a partitioning is quite arbitrary on a microscopic scale. Of course, choosing points where the electrostatic potential is significant makes no sense, but even for points that could be considered having zero potential, what would be the criterion? Is two Debye lengths enough? Or perhaps four? Why?
These examples also demonstrate that researchers ultimately do not
have a microscopic view in mind. Rather, the “microscopic”
specifications are subject to the macroscopic constraints.
Alt-Epping et
al. (2015), for example, specifies a priori that the system
contains about 15% free water, from which it follows that the diffuse
layer thickness must be set to about one Debye length (given the
adopted surface area). Likewise,
Appelo and Wersin (2007)
assume from the start that Opalinus clay contains 50% free water, and
set up their model accordingly.14Tournassat and Steefel
(2019) acknowledge their approach to only be a “convenient
representation”, and don’t even relate the diffuse layer
extension to a specific value of the electrostatic
potential.15 Why
the free water domain anyway is considered to be positioned in the
center of the nanopore is a mystery to me (well, I guess because
sometimes this interpretation is supposed to be taken literally…).
Note that none of the free water domains in the considered models are actually charged, even though the electrostatic potential in the microscopic interpretations is implied to be non-zero. This just confirms that such interpretations are not valid, and that the actual model handling is the equilibration of two (or more) macroscopic, abstract, continua. The diffuse layer domain is defined by following some arbitrary procedure that involves microscopic concepts. But just because the diffuse layer domain is quantified by multiplying a surface area by some multiple of the Debye length does not make it a microscopic entity.4
Donnan effect on the microscopic scale?!
Although we have already seen that we cannot interpret multi-porosity models microscopically, we have not yet considered the weirdest description adopted by basically all proponents of these models: they claim to perform Donnan equilibrium calculations between diffuse layer and free water regions on the microscopic scale!
The underlying mechanism for a Donnan effect is the establishment of charge separation, which obviously occur on the scale of the ions, i.e. on the microscopic scale. Indeed, a diffuse layer is the manifestation of this charge separation. Donnan equilibrium can consequently not be established within a diffuse layer region, and discontinuous electrostatic potentials only have meaning in a macroscopic context.
Consider e.g. the interface between bentonite and an external solution
in
the
homogeneous mixture model. Although this model ignores the
microscopic scale, it implies charge separation and a continuously
varying potential on this scale, as illustrated here
The regions where the potential varies are exactly what we categorize
as diffuse layers (exemplified in two ideal microscopic geometries).
The discontinuous potentials encountered in multi-porosity model descriptions (see e.g. the above “Dual Continuum” potential that varies discontinuously on the angstrom scale) can be drawn on paper, but don’t convey any physical meaning.
Here I am not saying that Donnan equilibrium calculations cannot be performed in multi-porosity models. Rather, this is yet another aspect showing that such models only have meaning macroscopically, even though they are persistently presented as if they somehow consider the microscopic scale.
An example of this confusion of scales is found in
Alt-Epping et
al. (2018), who revisit the benchmark problem of
Alt-Epping et
al. (2015) using an alternative approach to Donnan equilibrium:
rather than directly calculating the equilibrium, they model the clay
charge as immobile mono-valent anions, and utilize the
Nernst-Planck
equations. They present “the conceptual model” in a figure very
similar to this one
This illustration simultaneously conveys both a micro- and macroscopic view. For example, a mineral surface is indicated at the bottom, suggesting that we supposedly are looking at an actual interface region, in similarity with the figures we have looked at earlier. Moreover, the figure contains entities that must be interpreted as individual ions, including the immobile “clay-anions”. As in several of the previous examples, no length scale is provided (neither perpendicular to, nor along the “surface”).
On the other hand, the region is divided into cells, similar to the
illustration in Appelo and Wersin (2007). These can hardly have any other meaning
than to indicate the macroscopic discretization in the adopted
transport code (FLOTRAN). Also, as the “Donnan porosity” region
contains the “clay-anions” it can certainly not represent a diffuse
layer extending from a clay surface; the only way to make sense of
such an “immobile-anion” solution is that it represents a
macroscopic homogenized clay domain (a homogeneous mixture!).
Furthermore, if the figure is supposed to show the microscopic scale
there is no Donnan effect, because there is no charge separation!
Taking the depiction of individual ions seriously, the interface
region should rather look something like this in equilibrium
This illustrates the fundamental problem with a Donnan effect between microscopic compartments: the effect requires a charge separation, whose extension is the same as the size of the compartments assumed to be in equilibrium.16
Despite the confusion of the illustration in Alt-Epping et al. (2018), it is clear that a macroscopic model is adopted, as in our previous examples. In this case, the model is explicitly 2-dimensional, and the authors utilize the “trick” to make diffusion much faster in the perpendicular direction compared to the direction along the “surface”. This is achieved either by making the perpendicular diffusivity very high, or by making the perpendicular extension small. In any case, a perpendicular length scale must have been specified in the model, even if it is nowhere stated in the article. The same “trick” for emulating Donnan equilibrium is also used by Jenni et al. (2017), who write
In the present model set-up, this approach was implemented as two connected domains in the z dimension: one containing all minerals plus the free porosity (z=1) and the other containing the Donnan porosity, including the immobile anions (CEC, z=2, Fig. 2). Reproducing instantaneous equilibrium between Donnan and free porosities requires a much faster diffusion between the porosity domains than along the porosity domains.
Note that although the perpendicular dimension (\(z\)) here is referred
to without unit(!), this representation only makes sense in a
macroscopic context.
Jenni et al. (2017) also provide a statement that I think fairly well sums up the multi-porosity modeling endeavor:17
In a Donnan porosity concept, cation exchange can be seen as resulting from Donnan equilibrium between the Donnan porosity and the free porosity, possibly moderated by additional specific sorption. In CrunchflowMC or PhreeqC (Appelo and Wersin, 2007; Steefel, 2009; Tournassat and Appelo, 2011; Alt-Epping et al., 2014; Tournassat and Steefel, 2015), this is implemented by an explicit partitioning function that distributes aqueous species between the two pore compartments. Alternatively, this ion partitioning can be modelled implicitly by diffusion and electrochemical migration (Fick’s first law and Nernst-Planck equations) between the free porosity and the Donnan porosity, the latter containing immobile anions representing the CEC. The resulting ion compositions of the two equilibrated porosities agree with the concentrations predicted by the Donnan equilibrium, which can be shown in case studies (unpublished results, Gimmi and Alt-Epping).
Ultimately, these are models that, using one approach or the other,
simply calculates Donnan equilibrium between two abstract,
macroscopically defined domains (“porosities”,
“continua”). Microscopic interpretations of these models lead — as
we have demonstrated — to multiple absurdities and errors. I am not
aware of any multi-porosity approach that has provided any kind of
suggestion for what constitutes the semi-permeable component required
for maintaining the equilibrium they are supposed to describe.
Alternatively expressed: what, in the previous figure,
prevents the “immobile anions” from occupying the entire clay
volume?
The most favorable interpretation I can make of multi-porosity approaches to bentonite modeling is a dynamically varying “macroporosity”, involving magical membranes (shown above). This, in itself, answers why I cannot take multi-porosity models seriously. And then we haven’t yet mentioned the flawed treatment of diffusive flux.
[1] This category has many other names,
e.g. “dual
porosity” and “dual continuum”, models. Here, I mostly use the term
“multi-porosity” to refer to any model of this kind.
[3] This lack of a full
description is very much related to the incomplete description of
so-called
“stacks” — I am not aware of any reasonable suggestion of a
mechanism for keeping stacks together.
[4] Note the difference between a diffuse layer and a diffuse layer domain. The former is a structure on the nm-scale; the latter is a macroscopic, abstract model component (a continuum).
[5] The scale of an electric double layer is
set by the Debye length, \(\kappa^{-1}\). From the formula for a 1:1
electrolyte, \(\kappa^{-1} = 0.3 \;\mathrm{nm}/\sqrt{I}\), the Debye
length is seen to vary between 0.3 nm and 30 nm when ionic strength
is varied between 1.0 M to 0.0001 M (\(I\) is the numerical value of
the ionic strength expressed in molar units). Independent of the
value of the factor used to multiply \(\kappa^{-1}\) in order to
estimate the double layer extension, I’d say that the estimation 1 –
100 nm is quite reasonable.
[6] Here, the informed reader may perhaps point out that authors don’t really mean that the free water film has exactly the same geometry as the diffuse layer, and that figures like the one above are more abstract representations of a more complex structure. Figures of more complex pore structures are actually found in manymulti-porositypapers. But if it is the case that the free water part is not supposed to be interpreted on the microscopic scale, we are basically back to a magic membrane picture of the structure! Moreover, if the free water is not supposed to be on the microscopic scale, the diffuse layer will always have a negligible volume, and these illustrations don’t provide a mean for calculating the partitioning between “micro” and “macroporosity”.
It seems to me that not specifying the extension of the free water is a way for authors to dodge the question of how it is actually distributed (and, as a consequence, to not state what constitutes the semi-permeable component).
[7] The PHREEQC input files are provided as
supplementary material to Appelo and Wersin (2007). Here I consider the input corresponding
to figure 3c in the article. The free water is specified with
keyword “SOLUTION”.
[8] Keyword
“SURFACE” in the PHREEQC input file for figure 3c in the paper.
[9] Using the identifier “-donnan” for the “SURFACE”
keyword.
[10] We assume a boundary
condition such that the potential is zero in the solution infinitely
far away from any clay component.
[11] Assuming exponential decay, which is only strictly true for a single clay layer of low charge.
[12] For example,
Tournassat and Steefel
(2019) write
(\(f_{DL}\) denotes the volume fraction of the diffuse layer):
In PHREEQC and CrunchClay, the volume of the diffuse layer (\(V_{DL}\) in m3), and hence the \(f_{DL}\) value, can be defined as a multiple of the Debye length in order to capture this effect of ionic strength on \(f_{DL}\): \begin{equation*} V_{DL} = \alpha_{DL}\kappa^{-1}S \tag{22} \end{equation*} \begin{equation*} f_{DL} = V_{DL}/V_{pore} \end{equation*} […] it is obvious that \(f_{DL}\) cannot exceed 1. Equation (22) must then be seen as an approximation, the validity of which may be limited to small variations of ionic strength compared to the conditions at which \(f_{DL}\) is determined experimentally. This can be appreciated by looking at the results obtained with a simple model where: \begin{equation*} \alpha_{DL} = 2\;\mathrm{if}\;4\kappa^{-1} \le V_{pore}/S\;\mathrm{and,} \end{equation*} \begin{equation*} f_{DL} = 1 \;\mathrm{otherwise.} \end{equation*}
[13] Some tools (e.g. PHREEQC) allow to put a maximum size limit on the diffuse layer domain, independent of chemical conditions. This is of course only a way for the code to “work” under all conditions.
[14] As icing on the cake, these estimations of free water in bentonite (15%) and Opalinus clay (50%) appear to be based on the incorrect assumption that “anions” only reside in such compartments. In the present context, this handling is particularly confusing, as a main point with multi-porosity models (I assume?) is to evaluate ion concentrations in other types of compartments.
[16] Donnan equilibrium between microscopic
compartments can be studied in
molecular dynamics simulations, but they require the considered
system to be large enough for the electrostatic potential to reach
zero. The semi-permeable component in such simulations is
implemented by simply imposing constraints on the atoms making up
the clay layer.
When discussing semi-permeability, we noted that a bentonite sample that is saturated with a saline solution probably contains more salt in the initial stages of the process than what is dictated by the final state Donnan equilibrium. This salt must consequently diffuse out of the sample before equilibrium is reached.
The reason for such a possible “overshoot” of the clay concentration is that an infiltrating solution is not subject to a Donnan effect (between sample and external solution) when it fills out the air-filled voids of an unsaturated sample. Also, even if the region near the interface to the external solution becomes saturated — so that a Donnan effect is active — a sample may still take up more salt than prescribed by the final state, due to hyperfiltration: with a net inflow of water and an active Donnan effect, salt will accumulate at the inlet interface (unless the interface is flushed). This increased concentration, in turn, alters the Donnan equilibrium at the interface, with the effect that more salt diffuses into the clay.
These effects are relevant for our ongoing assessment of studies of chloride equilibrium concentrations. If bentonite samples are saturated with saline solutions, without taking precautions against these effects, evaluated equilibrium concentrations may be overestimated. Note that, even if saturating a sample may be relatively fast, it may take a long time for salt to reach full equilibrium, depending on details of the experimental set-up. In particular, if the set-up is such that the external solution does not flow past the inlet, equilibration may take a very long time, being limited by diffusion in filters and tubing.
Interface excess salt
Another way for evaluated salt concentrations to overestimate the true equilibrium value — which is independent of whether or not the sample has been saturated with a saline solution — is due to excess salt at the sample interfaces.
Suppose that you determine the equilibrium salt concentration in a bentonite sample in the following way. First you prepare the sample in a test cell and contact it with an external salt solution via filters. When the system (bentonite + solution) has reached equilibrium (taking all the precautions against overestimation discussed above), the concentration profile may be conceptualized like this
The aim is to determine \(\bar{c}_\mathrm{clay}\), the
clay concentration of the species of interest
(e.g. chloride), and to relate it to the corresponding concentration in the
external solution (\(c_ \mathrm{ext}\)).
After ensuring the value of \(c_\mathrm{ext}\) (e.g. by sampling or controlling the external solution), you unload the test cell and isolate the bentonite sample. In doing so, we must keep in mind that the sample will begin to swell as soon as the force on it is released, if only water is available. In the present example it is difficult not to imagine that some water is available, e.g. in the filters.1
It is thus plausible that the actual concentration profile look
something like this directly after the sample has been isolated
We will refer to the elevated concentration at the interfaces as the
interface excess. The exact shape of the resulting
concentration profile depends reasonably on the detailed procedure for
isolating the sample.2 If the ion content of the sample is measured
as a whole, and/or if the sample is stored for an appreciable amount
of time before further analysis (so that the profile evens out due to
diffusion), it is clear that the evaluated ion content will be larger
than the actual clay concentration.
To quantify how much the clay concentration may be overestimated due
to the interface excess, we introduce an effective penetration
depth, \(\delta\)
\(\delta\) corresponds to a depth of the external concentration that
gives the same interface excess as the actual distribution. Using this
parameter, it is easy to see that the clay concentration evaluated as
the average over the entire sample is
This expression is quite interesting. We see that the relative
overestimation, reasonably, depends linearly on \(\delta\) and on the
inverse of sample length. But the expression also contains the ratio
\(r \equiv c_\mathrm{ext}/\bar{c}_\mathrm{clay}\), indicating that the effect may
be more severe for systems where the clay concentration is small in
comparison to the external concentration (high density, low
\(c_\mathrm{ext}\)).
An interface excess is more than a theoretical concept, and is frequently observed e.g. in anion through-diffusion studies. We have previously encountered them when assessing the diffusion studies of Muurinen et al. (1988) and Molera et al. (2003).3Van Loon et al. (2007) clearly demonstrate the phenomenon, as they evaluate the distribution of stable chloride (the background electrolyte) in the samples after performing the diffusion tests.4 Here is an example of the chloride distribution in a sample of density 1.6 g/cm3 and background concentration of 0.1 M5
The line labeled \(\bar{c}_\mathrm{clay}\) is evaluated from the average of only the interior sections (0.0066 M), while the line labeled \(\bar{c}_\mathrm{eval}\) is the average of all sections (0.0104 M). Using the full sample to evaluate the chloride clay concentration thus overestimates the value by a factor 1.6. From eq. 1, we see that this corresponds to \(\delta = 0.2\) mm. For a sample of length 5 mm with the same penetration depth, the corresponding overestimation is a factor of 2.1.
Here is plotted the relative overestimation (eq. 1) as a function of \(\delta\) for several systems of varying length and \(r\) (\(= c^\mathrm{ext}/\bar{c}_\mathrm{clay}\))
We see that systems with large \(r\) and/or small \(L\) become hypersensitive to this effect. Thus, even if it may be expected that \(\delta\) decreases with increasing \(r\),6 we may still expect an increased overestimation for such systems.
To avoid this potential overestimation of the clay concentration, I
guess the best practice is to quickly remove the first couple of
millimeters on both sides of a sample after it has been unloaded. In
many through-diffusion tests, this is done as part of the study, as
the concentration profile across the sample often is measured. In
studies where samples are merely equilibrated with an external
solution, however, removing the interface regions may not be
considered.
Summary
We have here discussed some plausible reasons for why an evaluated
equilibrium salt concentration in a clay sample may be overestimated:
If samples are saturated directly with a saline solution. Better practice is to first saturate the sample with pure water (or a dilute solution) and then to equilibrate with respect to salt in a second stage.
If the external solution is not circulated. Diffusion may then occur over very long distances (depending on test design). The reasonable practice is to always circulate external solutions.
If interface excess is not handled. This is an issue even if saturation is done with pure water. The most convenient way to deal with this is to section off the first millimeters on both sides of the samples as quickly as possible after they are unloaded.
Footnotes
[1] One way to minimize this possible effect could be to
empty the filter before unloading the test cell. This may, however,
be difficult unless the filter itself is flushable. Also, you may
run into the problem of beginning to dry the sample.
[2] The only study I’m aware of that has
systematically investigated these types of concentration profiles is
Glaus et
al. (2011). They claim, if I understand correctly, that the
interface excess is not caused by swelling during
dismantling. Rather, they mean that the profile is the result of an
intrinsic density decrease that occurs in interface regions. Still,
they don’t discuss how swelling are supposed to be inhibited,
neither during dismantling, nor in order for the density
inhomogeneity to remain. Under any circumstance, the conclusions in
this blog post are not dependent on the cause for the presence of a
salt interface excess.
[3] In through-diffusion tests, the problem of the
interface excess is usually not that the equilibrium clay
concentration is systematically overestimated, since the detailed
concentration profile often is sampled in the final state. Instead,
the problem becomes how to separate the linear and non-linear parts
of the profile.
What do authors mean when they say that bentonite has semi-permeable properties? Take for example this statement, from Bradbury and Baeyens (2003)1
[…] highly compacted bentonite can function as an efficient semi-permeable membrane (Horseman et al., 1996). This implies that the re-saturation of compacted bentonite involves predominantly the movement of water molecules and not solute molecules.
Judging from the reference to Horseman et al. (1996) — which we look at below — it is relatively clear that Bradbury and Baeyens (2003) allude to the concept of salt exclusion when speaking of “semi-permeability” (although writing “solute molecules”). But a lowered equilibrium salt concentration does not automatically say that salt is less transferable.
A crucial question is what the salt is supposed to permeate. Note that a semi-permeable component is required for defining both swelling pressure and salt exclusion. In case of bentonite, this component is impermeable to the clay particles, while it is fully permeable to ions and water (in a lab setting, it is typically a metal filter). But Bradbury and Baeyens (2003) seem to mean that in the process of transferring aqueous species between an external reservoir and bentonite, salt is somehow effectively hindered to be transferred. This does not make much sense.
Consider e.g. the process mentioned in the quotation, i.e. to
saturate a bentonite sample with a salt solution. With
unsaturated bentonite, most bets are off regarding Donnan equilibrium,
and how salt is transferred depends on the details of the saturation
procedure; we only know that the external and internal salt
concentrations should comply with the rules for salt exclusion once
the process is finalized.
Imagine, for instance, an unsaturated sample containing bentonite
pellets on the cm-scale that very quickly is flushed with the
saturating solution, as illustrated in this state-of-the-art,
cutting-edge animation
The evolution of the salt concentration in the sample will look
something like this
Initially, as the saturating solution flushes the sample, the
concentration will be similar to that of the external concentration
(\(c_\mathrm{ext}\)). As the sample reaches saturation, it contains more
salt than what is dictated by Donnan equilibrium (\(c_\mathrm{eq.}\)),
and salt will diffuse out.
In a process like this it should be obvious that the bentonite not in any way is effectively impermeable to the salt. Note also that, although this example is somewhat extreme, the equilibrium salt concentration is probably reached “from above” in most processes where the clay is saturated with a saline solution: too much salt initially enters the sample (when a “microstructure” actually exists) and is later expelled.
Also for mass transfer between an external solution and an already saturated sample does it not make sense to speak of “semi-permeability” in the way here discussed. Consider e.g. a bentonite sample initially in equilibrium with an external 0.3 M NaCl solution, where the solution suddenly is switched to 1.0 M. Salt will then start to diffuse into the sample until a new (Donnan) equilibrium state is reached. Simultaneously (a minute amount of) water is transported out of the clay, in order for the sample to adapt to the new equilibrium pressure.2
There is nothing very “semi-permeabilic” going on here — NaCl is
obviously free to pass into the clay. That the equilibrium clay
concentration in the final state happens to be lower than in the
external concentration is irrelevant for how how difficult it is to
transfer the salt.
But it seems that many authors somehow equate “semi-permeability” with salt exclusion, and also mean that this “semi-permeability” is caused by reduced mobility for ions within the clay. E.g. Horseman et al. (1996) write (in a section entitled “Clays as semi-permeable membranes”)
[…] the net negative electrical potential between closely spaced clay particles repel anions attempting to migrate through the narrow aqueous films of a compact clay, a phenomenon known as negative adsorption or Donnan exclusion. In order to maintain electrical neutrality in the external solution, cations will tend to remain with their counter-ions and their movement through the clay will also be restricted (Fritz, 1986). The overall effect is that charged chemical species do not move readily through a compact clay and neutral water molecules may be able to pass more freely.
It must be remembered that Donnan exclusion occurs in many systemsother than “compact clay”. By instead considering e.g. a ferrocyanide solution, it becomes clear that salt exclusion has nothing to do with how hindered the ions are to move in the system (as long as they move). KCl is, of course, not excluded from a potassium ferrocyanide system because ferrocyanide repels chloride, nor does such interactions imply restricted mobility (repulsion occurs in all salt solutions). Similarly, salt is not excluded from bentonite because of repulsion between anions and surfaces (also, a negative potential does not repel anything — charge does).
In the above quotation it is easy to spot the flaw in the argument by switching roles of anions and cations; you may equally incorrectly say that cations are attracted, and that anions tag along in order to maintain charge neutrality.
The idea that “semi-permeability” (and “anion” exclusion) is
caused by mobility restrictions for the ions within the
bentonite, while water can “pass more freely” is found in many
places in the bentonite
literature. E.g. Shackelford and Moore (2013) write (where, again, potentials are
described as repelling)
In [the case of bentonite], when the clay is compressed to a sufficiently high density such that the pore spaces between adjacent clay particles are minimized to the extent that the electrostatic (diffuse double) layers surrounding the particles overlap, the overlapping negative potentials repel invading anions such that the pore becomes excluded to the anion. Cations also may be excluded to the extent that electrical neutrality in solution is required (e.g., Robinson and Stokes, 1959).
This phenomenon of anion exclusion also is responsible for the existence of semipermeable membrane behavior, which refers to the ability of a porous medium to restrict the migration of solutes, while allowing passage of the solvent (e.g., Shackelford, 2012).
[…] TOT layers bear a negative structural charge that is compensated by cation accumulation and anion depletion near their surfaces in a region known as the electrical double layer (EDL). This property gives clay materials their semipermeable membrane properties: ion transport in the clay material is hindered by electrostatic repulsion of anions from the EDL porosity, while water is freely admitted to the membrane.
and Tournassat and Steefel (2019) write (where, again, we can switch roles of “co-” and “counter-ions”, to spot one of the flaws)
The presence of overlapping diffuse layers in charged nanoporous media is responsible for a partial or total repulsion of co-ions from the porosity. In the presence of a gradient of bulk electrolyte concentration, co-ion migration through the pores is hindered, as well as the migration of their counter-ion counterparts because of the electro-neutrality constraint. This explains the salt-exclusionary properties of these materials. These properties confer these media with a semi-permeable membrane behavior: neutral aqueous species and water are freely admitted through the membrane while ions are not, giving rise to coupled transport processes.
I am quite puzzled by these statements being so commonplace.3 It does not surprise me that all the quotations basically state some version of the incorrect notion that salt exclusion is caused by electrostatic repulsion between anions and surfaces — this is, for some reason, an established “explanation” within the clay literature.4 But all quotations also state (more or less explicitly) that ions (or even “solutes”) are restricted, while water can move freely in the clay. Given that one of the main features of compacted bentonite components is to restrict water transport, with hydraulic conductivities often below 10-13 m/s, I don’t really know what to say.
Furthermore, one of the most investigated areas in bentonite research is the (relatively) high cation transport capacity that can be achieved under the right conditions. In this light, I find it peculiar to claim that bentonite generally impedes ion transport in relation to water transport.
Bentonite as a non-ideal semi-permeable membrane
As far as I see, authors seem to confuse transport between external
solutions and clay with processes that occur between two
external solutions separated by a bentonite component. Here is
an example of the latter set-up
The difference in concentration between the two solutions implies
water transport — i.e. osmosis — from the reservoir with lower salt
concentration to the reservoir with higher concentration. In this
process, the bentonite component as a whole functions as the membrane.
The bentonite component has this function because in this process it
is more permeable to water than to salt (which has a driving force to
be transported from the high concentration to the low concentration
reservoir). This is the sense in which bentonite can be said to be
semi-permeable with respect to water/salt. Note:
Salt is still transported through the bentonite. Thus, the bentonite component functions fundamentally only as a non-ideal membrane.
Zooming in on the bentonite component in the above set-up, we note that the non-ideal semi-permeable functionality emerges from the presence of two ideal semi-permeable components. As discussed above, the ideal semi-permeable components (metal filters) keep the clay particles in place.
The non-ideal semi-permeability is a consequence of salt exclusion. But these are certainly not the same thing! Rather, the implication is: Ideal semi-permeable components (impermeable to clay) \(\rightarrow\) Donnan effect \(\rightarrow\) Non-ideal semi-permeable membrane functionality (for salt)
The non-ideal functionality means that it is only relevant during non-equilibrium. E.g., a possible (osmotic) pressure increase in the right compartment in the illustration above will only last until the salt has had time to even out in the two reservoirs; left to itself, the above system will eventually end up with identical conditions in the two reservoirs. This is in contrast to the effect of an ideal membrane, where it makes sense to speak of an equilibrium osmotic pressure.
None of the above points depend critically on the membrane material being bentonite. The same principal functionality is achieved with any type of Donnan system. One could thus imagine replacing the bentonite and the metal filters with e.g. a ferrocyanide solution and appropriate ideal semi-permeable membranes. I don’t know if this particular system ever has been realized, but e.g. membranes based on polyamide rather than bentonite seems more commonplace in filtration applications (we have now opened the door to the gigantic fields of membrane and filtration technology). From this consideration it follows that “semi-permeability” cannot be attributed to anything bentonite specific (such as “overlapping double layers”, or direct interaction with charged surfaces).
I think it is important to remember that, even if bentonite is semi-permeable in the sense discussed, the transfer of any substance across a compacted bentonite sample is significantly reduced (which is why we are interested in using it e.g. for confining waste). This is true for both water and solutes (perhaps with the exception of some cations under certain conditions).
“Semi-permeability” in experiments
Even if bentonite is not semi-permeable in the sense described in many
places in the literature, its actual non-ideal semi-preamble
functionality must often be considered in compacted clay
research. Let’s have look at some relevant cases where a bentonite
sample is separated by two external solution reservoirs.
The traditional tracer through-diffusion test maintains identical
conditions in the two reservoirs (the same chemical compositions and
pressures) while adding a trace amount of the diffusing substance to
the source reservoir. The induced tracer flux is monitored by
measuring the amount of tracer entering the target reservoir.
In this case the chemical potential is identical in the two reservoirs for all components other than the tracer, and no additional transport processes are induced. Yet, it should be kept in mind that both the pressure and the electrostatic potential is different in the bentonite as compared with the reservoirs. The difference in electrostatic potential is the fundamental reason for the distinctly different diffusional behavior of cations and anions observed in these types of tests: as the background concentration is lowered, cation fluxes increase indefinitely (for constant external tracer concentration) while anion fluxes virtually vanish.
Tracer through-diffusion is often quantified using the parameter
\(D_e\), defined as the ratio between steady-state flux and
the external concentration
gradient.5 \(D_e\) is thus a
type of ion permeability coefficient, rather than a diffusion
coefficient, which it nevertheless
often is assumed to be.
Typically we have that
\(D_e^\mathrm{cation} > D_e^\mathrm{water} > D_e^\mathrm{anion}\) (where
\(D_e^\mathrm{cation}\) in principle may become
arbitrary large). This behavior both demonstrates the underlying
coupling to electrostatics, and that “charged chemical species”
under these conditions hardly can be said to move less readily through
the clay as compared with water molecules.
Measuring hydraulic conductivity
A second type of experiment where only a single component is
transported across the clay is when the reservoirs contain pure water
at different pressures. This is the typical set-up for measuring the
so-called hydraulic conductivity of a clay
component.6
Even if no other transport processes are induced (there is nothing
else present to be transported), the situation is here more complex
than for the traditional tracer through-diffusion test. The difference
in water chemical potential between the two reservoirs implies a
mechanical coupling to the clay, and a
corresponding response in density distribution. An inhomogeneous
density, in turn, implies the presence of an electric field. Water
flow through bentonite is thus fundamentally coupled to both
mechanical and electrical processes.
In analogy with \(D_e\), hydraulic conductivity is defined as the ratio
between steady-state flow and the external pressure
gradient. Consequently, hydraulic conductivity is an effective mass
transfer coefficient that don’t directly relate to the fundamental
processes in the clay.
An indication that water flow through bentonite is more subtle than what it may seem is the mere observation that the hydraulic conductivity of e.g. pure Na-montmorillonite at a porosity of 0.41 is only 8·10-15 m/s. This system thus contains more than 40% water volume-wise, but has a conductivity below that of unfractioned metamorphic and igneous rocks! At the same time, increasing the porosity by a factor 1.75 (to 0.72), the hydraulic conductivity increases by a factor of 75! (to 6·10-13 m/s7)
Mass transfer in a salt gradient
Let’s now consider the more general case with different chemical
compositions in the two reservoirs, as well as a possible pressure
difference (to begin with, we assume equal pressures).
Even with identical hydrostatic pressures in the reservoirs, this
configuration will induce a pressure response, and consequently a
density redistribution, in the bentonite. There will moreover be both
an osmotic water flow from the right to the left reservoir, as well
as a diffusive solute flux in the opposite direction. This general
configuration thus necessarily couples hydraulic, mechanical,
electrical, and chemical processes.
This type of configuration is considered e.g. in the study of osmotic effects in geological settings, where a clay or shale formation may act as a membrane.8 But although this configuration is highly relevant for engineered clay barrier systems, I cannot think of very many studies focused on these couplings (perhaps I should look better).
For example, most through-diffusion studies are of the tracer type discussed above, although evaluated parameters are often used in models with more general configurations (e.g. with salt or pressure gradients). Also, I am not aware of any measurements of hydraulic conductivity in case of a salt gradient (but the same hydrostatic pressure), and I am even less aware of such values being compared with those evaluated in conventional tests (discussed previously).
A quite spectacular demonstration that mass transfer may occur very differently in this general configuration is the seeming steady-stateuphill diffusion effect: adding an equal concentration of a cation tracer to the reservoirs in a set-up with a maintained difference in background concentration, a tracer concentration difference spontaneously develops. \(D_e\) for the tracer can thus equal infinity,9 or be negative (definitely proving that this parameter is not a diffusion coefficient). I leave it as an exercise to the reader to work out how “semi-permeable” the clay is in this case. Update (240822):The “uphill” diffusion effect is further discussed here.
A process of practical importance for engineered clay barrier systems
is hyperfiltration of salts. This process will occur when a sufficient
pressure difference is applied over a bentonite sample contacted with
saline solutions. Water and salt will then be transferred in the same
direction, but, due to exclusion, salt will accumulate on the
inlet side. A steady-state concentration profile for such a process
may look like this
The local salt concentration at the sample interface on the inlet side
may thus be larger than the concentration of the injected
solution. This may have consequences e.g. when evaluating hydraulic
conductivity using saline solutions.
Hyperfiltration may also influence the way a sample becomes saturated, if saturated with a saline solution. If the region near the inlet is virtually saturated, while regions farther into the sample still are unsaturated, hyperfiltration could occur. In such a scenario the clay could in a sense be said to be semi-permeable (letting through water and filtrating salts), but note that the net effect is to transfer more salt into the sample than what is dictated by Donnan equilibrium with the injected solution (which has concentration \(c_1\), if we stick with the figure above). Salt will then have to diffuse out again, in later stages of the process, before full equilibrium is reached. This is in similarity with the saturation process that we considered earlier.
[2] This is more than a thought-experiment; a test just like this was conducted by Karnland et al. (2005). Here is the recorded pressure response of a Na-montmorillonite sample (dry density 1.4 g/cm3) as it is contacted with NaCl solutions of increasing concentration
[3] As a side note, is the region near the surface supposed to be called “diffuse layer”, “electrical double layer”, or “electrostatic (diffuse double) layer”?
[5] This is not a gradient in the mathematical sense, but is defined as \( \left (c_\mathrm{target} – c_\mathrm{source} \right)/L\), where \(L\) is sample length.
[6] Hydraulic conductivity is often also measured
using a saline solution, which is commented on below.
[7] Which
still is an a amazingly small hydraulic conductivity, considering
the the water content.
[9] Mathematically, the statement “equal infinity” is
mostly nonsense, but I am trying to convey that a there is a tracer
flux even without any external tracer concentration difference.