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REVIEW 4 major objections 6 minor 70 references

Navigating the Complexities of Multiple Redox State Interactions in Aqueous Systems

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that a single measurable rate constant, applied to the summed concentration of all complexes in an oxidation state, can predict where and when Fe(II)/Fe(III) and Mn(II)/Mn(III) species form, move, and precipitate in…

desk verdict Genuinely new decoupling idea for redox kinetics, but the printed governing equations are internally inconsistent and neither case study validates the framework. read the letter →

arxiv 2501.11536 v1 pith:3GTJ5LZB submitted 2025-01-20 cond-mat.mtrl-sci

Shishir Mundra , Mohit Pundir , Barbara Lothenbach , David S. Kammer , Ueli M. Angst This is my paper · ORCID
classification cond-mat.mtrl-sci
keywords redoxreactionsreactivetransportoxidationstatesironspeciationmanganeseoperatorsplittingGibbsfreeenergyminimisationcorrosioninporousmedia
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a reactive-transport framework in which conversion of an element between oxidation states is a transient, kinetically controlled process, fully separated from thermodynamic speciation and precipitation. The central device is a pair of auxiliary variables, $[M^{a}]$ and $[M^{b}]$, each holding the summed concentration of all aqueous complexes of the element in one oxidation state, converted at a single measurable first-order rate constant $\kappa$ instead of species-by-species constants. Each oxidation state then undergoes its own Gibbs free energy minimisation, coupled through an iterated pH and shared background chemistry, so that transport and kinetics set the concentrations while thermodynamics settles speciation and solids. On this basis the authors simulate corroding steel in porous media, predicting where goethite forms relative to the metal surface under aerobic and anaerobic conditions, and a 30-m water column where rising $p\mathrm{CO}_2$ shrinks the mobile Mn(III) pool by slowing Mn(II) oxidation and accelerating Mn(III) reduction. The authors note the current scope assumes instant goethite precipitation, instantaneous CaCO$_3$ dissolution, and excludes Mn(IV) and mixed-valence solids, while framing the method as a foundation for more mechanistic corrosion and water-quality models.

What carries the argument

The load-bearing object is the pair of lumped oxidation-state concentrations $[M^{a}]$ and $[M^{b}]$, each the sum of the concentrations of all aqueous complexes of element M in a given oxidation state. Equations (2)--(5) couple Fickian diffusion with the redox conversion at net rates $\kappa_{\mathrm{ox}}$ and $\kappa_{\mathrm{red}}$; the product then feeds separate Gibbs free energy minimisations (equation (1)) for each oxidation state, iterated so the two systems converge to a common pH while exchanging background species. This machinery converts the unavailable species-resolved kinetic constants into a single measurable bulk rate constant while still resolving full speciation and precipitation per oxidation state.

What would settle it

A column experiment with carbonated-concrete pore solution: measure Fe(II) and Fe(III) profiles along the transport path at fixed pH, pO2, and temperature while the dominant Fe(II) species shifts from FeCO3(aq) to Fe2+, then check whether the inferred lumped kappa changes; if it does, species-resolved kinetics are required and the decoupled-systems prediction would misplace the goethite front.

Watch

Extended reading notes

Core claim

The paper's claim, stated for a fair reader, is that the fate of a multi-redox element in an aqueous system can be predicted from overall redox rate constants and standard thermodynamic databases, without resolving the kinetics of every complex. The auxiliary variables $[M^{a}]$ and $[M^{b}]$ are advanced by diffusion and by lumped pseudo-first-order conversion, and then the resulting totals are handed, one oxidation state at a time, to separate Gibbs minimisations that are iterated until they agree on pH and exchange background species to preserve mass balance. Applied to iron, this reproduces the experimentally observed result that anaerobic corrosion pushes Fe(III) (hydr)oxide precipitation away from the steel surface while aerobic corrosion keeps it near the interface. Applied to manganese, it yields the prediction that elevated CO$_2$ decreases the mobile Mn(III) pool in near-neutral natural waters. The authors present the framework as a methodological foundation, not as a finished predictive model.

Load-bearing premise

The method rests on the premise that a single measurable rate constant applied to the total concentration of all complexes in an oxidation state faithfully describes redox conversion even as that state's speciation, pH, and ionic strength shift.

Editorial extensions

If this is right

  • Fe(II)/Fe(III) distributions at a corroding steel surface in carbonated concrete become computable from bulk rate data, showing porosity, CaCO$_3$ content, and aeration jointly set where goethite precipitates.
  • In anaerobic media Fe(II) travels farther and precipitates as goethite further from the metal surface, matching experimental observations, whereas aerobic conditions keep Fe(III) solids near the interface.
  • Elevated atmospheric CO$_2$ in natural waters lowers pH and thereby reduces the mobile Mn(III) fraction, shifting Mn(II) speciation from Mn$^{2+}$ toward MnCO$_3$(aq).
  • The decoupling allows metastable and stable oxidation states to coexist in the same simulation, which the paper argues enables mechanistic treatment of corrosion-product formation and of redox-sensitive nutrient and toxicant cycling.
  • Extensions to sorption, solid-phase transformations, mixed-valence solids such as Fe$_3$O$_4$ and MnO$_x$, and porosity changes are direct corollaries of the same split, provided rate laws are available.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The lumped-state construction is generic, so Cr(III)/Cr(VI) and As(III)/As(V) systems are natural next targets once reliable overall rate laws and thermodynamic data exist for them.
  • Because the lumped $\kappa$ is really a speciation-weighted average, the model is least trustworthy precisely where speciation within an oxidation state changes fastest; a speciation-dependent $\kappa$ would be the sharpest available upgrade and a direct test of the approximation.
  • Assuming instantaneous goethite precipitation probably shifts predicted Fe(III) deposits outward; including ferrihydrite and its slow transformation would likely pull the solid front closer to the corroding surface than the present model shows.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes a reactive-transport framework for elements that coexist in multiple redox states in aqueous systems. The central idea is to decouple transient processes (diffusion and redox kinetics) from equilibrium processes (speciation and precipitation). Total concentrations of each oxidation state, denoted [M^a] and [M^b], are transported and interconverted using overall kinetic constants κ_ox and κ_red; then, at each spatial point, equilibrium speciation and precipitation are computed separately for each oxidation state with a Gibbs energy minimizer (Reaktoro), iterating until the pH in the two sub-systems converges. The framework is demonstrated on two case studies: electrochemically dissolved iron in porous media (Case I) and manganese speciation in a water column (Case II). The authors claim this decoupling permits the use of measurable overall rate constants and standard thermodynamic databases, avoiding the need for species-resolved kinetic constants.

Significance. If the framework is correct, it would be a practical addition to reactive-transport modeling of redox-sensitive environments, since it avoids per-species kinetic constants and leverages established thermodynamic solvers. The conceptual separation of kinetically controlled redox from equilibrium speciation is appealing, and the authors provide a detailed thermodynamic database, a time-step sensitivity analysis, and an explicit list of limitations. The two case studies address relevant problems (steel corrosion in carbonated concrete, Mn cycling in natural waters). However, the central governing equations are internally inconsistent as printed, and neither case study is quantitatively validated against experimental or field data. As a result, the predictive claims in the abstract cannot be assessed from the manuscript in its current form.

major comments (4)
  1. [Section 5.3, Eqs. (2)–(5)] The governing equations are internally inconsistent as printed. Since [M^a] is defined as the sum of all aqueous complexes in oxidation state a (Section 2, Fig. 1a) and [M^b] as the sum for state b, Eq. (2) and Eq. (5) prescribe different dynamics for the same quantity: Eq. (2) has no κ_red source term and uses per-species diffusion coefficients, whereas Eq. (5) includes +κ_red Σ_j M_j^b and a total diffusion coefficient. The same conflict exists between Eqs. (3) and (4). In the batch (zero-flux) limit, summing Eqs. (2) and (3) gives d/dt([M^a]+[M^b]) = -κ_ox[M^a] - κ_red[M^b], which violates total metal conservation, while Eqs. (4)+(5) conserve total metal exactly. The paper does not state which set of equations is actually solved, nor how per-species concentrations M_i^a and M_j^b are recovered from the transported auxiliary variables. This makes the model not well-posed as printed and prevents reproduction of the reported simulations.
  2. [Sections 3.1 and 3.2] Neither case study is quantitatively validated against experimental or field data. In Case I, the only comparison is the statement that goethite precipitation occurs further from the metal surface under anaerobic conditions (Fig. 3k,n) and the phrase 'in agreement with experimental observations [35]', without any quantitative metrics or uncertainty quantification. In Case II, no measured Mn(II)/Mn(III) profiles or rate data are compared with the simulations. Given the abstract's claim that the framework 'significantly enhances the modelling of a diverse range of redox-sensitive environments', a quantitative validation against at least one dataset is needed to support the predictive capability asserted.
  3. [Section 5.1] The central assumption that a single overall rate constant κ = f(κ_1,...,κ_n) is 'representative of the combined formation rates of each M^b complex' and can be taken from literature values is load-bearing for both case studies. The manuscript uses κ values from Refs. [2] and [11,12] but does not test the sensitivity of the results to the uncertainty or speciation dependence of these constants. If κ varies with pH, ionic strength, or the speciation distribution in ways not captured by the simple parameterizations used, the predicted tempo-spatial patterns could change substantially. A sensitivity analysis over the reported ranges of κ is required to assess the robustness of the framework.
  4. [Supplementary Note 5.5] The assumption that Fe(III) formed by oxidation instantly precipitates as goethite, with aqueous Fe(III) dictated by goethite solubility, means that aqueous Fe(III) speciation in Case I is purely thermodynamic rather than kinetically controlled. This is in tension with the abstract's statement that 'ion concentrations are governed by non-equilibrium rate laws' and weakens the claim that the framework predicts the tempo-spatial distribution of Fe(III) speciation; Figures 3d and 3h essentially reflect goethite solubility, not kinetic control. The text should clarify this distinction and either temper the claim or introduce a precipitation/dissolution kinetic law for Fe(III) phases.
minor comments (6)
  1. [Section 5.3] The typeset dot notation in Eqs. (2)–(5) (e.g., '.M_i^a') is nonstandard and appears to be a placeholder; the conventional ∂M/∂t notation should be used.
  2. [Supplementary Note 4] The sentence 'we hose the time step value for which the results are converged' contains a typo; it should read 'we chose the time step value...'.
  3. [Section 1] The phrase 'we uniquely treat redox kinetics along with transport as transient phenomena' may overstate the novelty, since sequential operator-split reactive transport models (e.g., PHREEQC-based) already allow kinetic redox reactions in a time-stepping manner; suggest softening this phrasing.
  4. [Section 3.1] In the description of the isolated-pore scenario, the text states 'we assume D to be 0 in Equations 3 − 5'; presumably this refers to the auxiliary variable equations (4)–(5), but the numbering should be explicit to avoid ambiguity.
  5. [Figures SI 1 and SI 2] The font sizes of axes labels and legends in the supplementary figures are very small; they should be increased for readability.
  6. [Section 5.2] The text says 'We employ the ideal activity model to calculate the coefficients of aqueous activity and gaseous activity', but Eq. (1) includes activity coefficients γ_i; please clarify which specific activity model (e.g., Davies, Debye-Hückel) is used and over what ionic-strength range.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: redox kinetics and thermodynamic inputs are external, and the predicted speciation/precipitation is a forward-model output.

full rationale

The derivation chain is not circular. The inputs to the framework are (i) experimentally determined overall redox rate constants (Fe: Mundra et al. 2023, cited as [2]; Mn: Klewicki 1996 and Morgan 2005, cited as [11,12]), (ii) standard thermodynamic datasets for aqueous and solid Fe/Mn species, and (iii) imposed initial/boundary conditions such as the corrosion flux, porosity, pO2 profile, and pCO2. The claimed outputs—spatial and temporal pH, total [Ma]/[Mb] in each oxidation state, and the equilibrium speciation/precipitation within each oxidation state—are obtained by integrating the transport-reaction equations and then performing Gibbs energy minimization in the operator-split equilibrium stage. Nothing in the output is used to define the inputs, and no parameter is fitted to the target predictions. The self-citations supply independent experimental input data, not the framework's conclusions, and the cited kinetic and thermodynamic data are externally measurable and not derived from the present model. The abstract's claim of 'predicting the tempo-spatial distribution of speciation and precipitation of species across oxidation states' is therefore a forward prediction conditional on those external inputs, not a restatement of them. The apparent inconsistency between Eqs. (2)-(3) and Eqs. (4)-(5) for the same total variables is a well-posedness/reproducibility concern, but it is a correctness issue rather than a circularity issue and does not raise the circularity score.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

No parameters are fitted to the target outputs; kinetic constants, thermodynamic data, corrosion current, pCO2, pO2 profiles, and initial concentrations are external inputs. The load-bearing modeling choices are the lumped overall rate constants, per-oxidation-state equilibrium closure, instantaneous goethite precipitation, the exclusion of Mn(IV), and instantaneous congruent CaCO3 dissolution. No new physical entities are postulated.

assumptions (6)
  • domain assumption The redox conversion between oxidation states can be described by a single overall pseudo-first-order rate constant kappa acting on the total concentration of all complexes in an oxidation state.
    Section 5.1; the paper argues kappa is measurable in experiments while individual complex rate constants are not. This is the load-bearing modeling step that makes the decoupling possible.
  • domain assumption Within each time step, speciation and precipitation of each oxidation state reach thermodynamic equilibrium via Gibbs free energy minimization.
    Sections 5.1 and 5.2; this operator-split closure requires intra-oxidation-state equilibration to be fast relative to redox kinetics and transport.
  • ad hoc to paper Fe(III) formed by oxidation instantly precipitates as goethite, and aqueous Fe(III) is set by goethite solubility.
    Supplementary Note 5.5 states this assumption; it fixes the Fe(III) 'prediction' in Case I and is not a general property of the framework.
  • ad hoc to paper Mn(IV) can be ignored in Case II due to missing thermodynamic and kinetic data.
    Section 3.2; the abstract's Mn speciation claim therefore only covers Mn(II) and Mn(III), not the thermodynamically favored Mn(IV) state.
  • ad hoc to paper CaCO3 dissolves congruently and instantaneously, with no dissolution kinetics.
    Supplementary Note 5.2; this controls pH buffering in Case I and directly shapes the reported pH evolution.
  • ad hoc to paper Corrosion releases Fe(II) at a constant rate of 1 uA/cm2 independent of evolving interfacial chemistry.
    Section 5.4 and Supplementary Note 5.5; acknowledged by the authors as a limitation, it restricts the predictive claims for Case I.

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Pith. "Pith review of Navigating the Complexities of Multiple Redox State Interactions in Aqueous Systems." pith.science (2026). https://pith.science/paper/3GTJ5LZB

@misc{pith2026250111536,
  author       = {Pith},
  title        = {Pith review of: Navigating the Complexities of Multiple Redox State Interactions in Aqueous Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3GTJ5LZB}},
  note         = {Machine review of arXiv:2501.11536}
}
read the original abstract

Numerous aqueous systems host elements in multiple redox states, with wide ranging implications such as their influence on the formation/dissolution of minerals, water toxicity, and nutrient cycling. To uncover governing mechanisms and complex chemical interactions in aqueous systems, reactive-transport models have increasingly gained importance. However, their predictive capabilities remain limited because existing approaches struggle to accurately account for the full complexities of redox reactions. Here, we develop a reactive-transport framework that leverages recent advancements in thermodynamic modelling, speciation chemistry, and redox kinetics. Distinct from traditional models, we uniquely treat redox kinetics along with transport as transient phenomena, decoupled from Gibbs free energy minimisation. Ensuring ion concentrations are governed by non-equilibrium rate laws, this approach allows predicting the tempo-spatial distribution of speciation and precipitation of species across oxidation states. We illustrate the versatility of our framework through two case studies: manganese speciation in natural waters and the fate of dissolved iron in aqueous/porous media. Our framework significantly enhances the modelling of a diverse range of redox-sensitive environments.

Figures

Figures reproduced from arXiv: 2501.11536 by the authors.

Figure 1
Figure 1. Conceptual modelling approach for solving the spatial and temporal evolution of multi-redox species in the proposed reactive transport framework: (a) Schematic of the complex interplay between various kinetically controlled processes such as speciation, redox reactions, precipitation and transport, governing the fate of element (M) in multiple redox states (Ma and Mb , where a > 0, b > a, and a and b are positive in… view at source ↗
Figure 2
Figure 2. Application examples of the proposed reactive transport framework: (a) Case study I: On the fate of electrochemically dissolved iron in porous media. The interface between iron or steel and the porous medium is a multi-component system comprising of reactive phases, CaCO3 (gray), non-reactive phases (brown), and aqueous phases such as pores saturated with moisture (blue). (b) Case study II: On the speciation of Mn i… view at source ↗
Figure 3
Figure 3. Simulations of electrochemically dissolved iron in reactive porous media, revealing the temporal and spatial changes in pH, [Fe] as well as Fe speciation and precipitation as a function of the porosity, [CaCO3] and aeration condition: (a-d) Modelled evolution of interfacial pH, [FeII], speciation of FeII, and speciation of FeIII, respectively, over time for three different average porosities of the porous medium (0.… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Influence of elevated levels of CO2 concentration on Mn speciation in natural waters: Distribution of MnII (red) and MnIII (blue) along the depth of the water column for three different pCO2 (a) 10−3.5 atm, (b) 10−2.5 atm and (c) 10−1.5 atm at 10 days, 100 days and 340…

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.