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REVIEW 3 major objections 5 minor 39 references

Quantifying Injection-Driven Interphase Mass Transfer within Porous Media via Time-Elapsed X-ray micro-Computed Tomography

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read Three frameworks that estimate gas-dissolution mass transfer from time-lapsed X-ray micro-CT scans agree within one order of magnitude on mass-transfer coefficients at every injection rate, but diverge sharply on pore-scale concentration.

desk verdict The three-way SAC/NPC/CPC comparison is genuinely new and useful, but the SAC values appear to come from the original study rather than the same filtered, three-phase processing, so the 'same data' claim needs fixing before the central comparison can be trusted as stated. read the letter →

arxiv 2604.07743 v2 pith:TS23VLVV submitted 2026-04-09 physics.flu-dyn physics.data-anphysics.geo-ph

Christopher A. Allison , Ruotong Huang , Anindityo Patmonoaji , Lydia Knuefing , Anna L. Herring This is my paper · ORCID
classification physics.flu-dynphysics.data-anphysics.geo-ph PACS 47.56.+r
keywords X-raymicro-computedtomographyinterphasemasstransferporousmediagasdissolutionclusterremobilizationcoefficientslice-averagedconcentrationper-clusteranalysis
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

When trapped hydrogen dissolves into water flowing through the pore spaces of a granular packing, its mass-transfer rate can be inferred from time-lapsed X-ray micro-CT scans — but the inference depends on which analytical framework converts imaged cluster volume changes into a coefficient. This paper applies the three frameworks currently in the literature — slice-averaged concentration (SAC), non-classified per-cluster (NPC), and classified per-cluster (CPC) — to the same four hydrogen-dissolution sequences at injection rates from 0.10 to 1.00 mL/min. Its central finding is that the three frameworks estimate average mass-transfer coefficients within one order of magnitude of one another at every injection rate, despite very different underlying assumptions and computational costs. The frameworks diverge, however, when estimating pore-scale aqueous solute concentration: SAC dilutes concentrations by cross-sectional averaging, NPC violates the physical bounds 0 ≤ C_i/C_sol ≤ 1, and CPC yields negative values localized at the moving dissolution front. The practical upshot is that researchers can choose a framework by the level of detail they need — the cheap SAC for system-scale coefficients, the expensive CPC when front structure or remobilization matters — rather than by accuracy of the bulk rate.

What carries the argument

The load-bearing machinery is the thin-film relation dm_g/dt = -kA(C_sol - C_g), which all three frameworks solve for k from imaged cluster volume changes, differing in how they supply C_g and A and which clusters they count. SAC reduces the system to a 1-D advection equation and iteratively computes slice-averaged concentration, diluting dissolved mass over each cross-section. NPC applies k = -(ΔV_g/Δt)ρ_gas/(A·C_sol) to every cluster volume change under a maximum-gradient assumption; CPC applies the same formula only to clusters classified as completely or partially dissolved, presumed to sit at the dissolution front. The paper's new device is a mobilization filter excluding time-intervals

What would settle it

Measure the true dissolution flux directly during identical injection runs — via effluent solute concentration, gravimetric gas loss, or a mass balance over the imaged volume — and check whether the three frameworks' filtered coefficients bracket it; and, on the same datasets, recompute the sequence averages at mobilization thresholds of 0.1, 0.3, 0.5, and 1.0 to see whether the cross-approach agreement survives the choice of cutoff.

Watch

Extended reading notes

Core claim

At a given solvent injection rate, the SAC, NPC, and CPC frameworks estimate average mass-transfer coefficients within one order of magnitude of each other, and all three rise with injection rate. The same datasets yield divergent pore-scale solute concentrations: SAC dilutes them by assuming radial uniformity; NPC, applying the maximum-gradient equation to every cluster change, produces values outside physical bounds (0 ≤ C_i/C_sol ≤ 1); and CPC returns negative concentrations at the leading edge of the dissolution front, which the authors read as evidence that the sequence-average coefficient underestimates front mass transfer. System-scale agreement reflects large sample pools; pore-scale

Load-bearing premise

The load-bearing premise is that the volume-ratio filter (ΔV_gained/|ΔV_lost| ≤ 0.2) cleanly separates dissolution-dominated from remobilization-dominated time-intervals; the paper admits there is no established method for setting this threshold, it is chosen post hoc to 'ensure sufficient data remained' (Section 4.1), and Table 6 shows it changes final average mass-transfer coefficients by up to 54% (Seq 0.10 CPC -38%, Seq 0.25 NPC +54%).

Editorial extensions

If this is right

  • For system-scale mass-transfer coefficients, the least expensive approach (SAC) is sufficient: at each injection rate its estimate falls within an order of magnitude of both per-cluster approaches.
  • Pore-scale aqueous solute concentration cannot be treated as method-independent: SAC systematically dilutes, NPC exceeds physical bounds, and CPC alone localizes a moving, non-uniform dissolution front.
  • The front's leading edge is underpredicted by the sequence-average mass-transfer coefficient, as shown by CPC's negative concentrations clustering at that edge in the point-cloud visualization.
  • The mobilization filter is portable to all three approaches: excluding time-intervals with volume-gained-to-volume-lost ratio above 0.2 removes remobilization-biased early intervals and shifts final coefficients by -38% to +54% depending on sequence and method.
  • All three approaches agree that mass-transfer coefficients increase with injection rate, and in dimensionless Sherwood-Reynolds form the proportional gap between approaches narrows as injection rate rises.

Reading between the lines

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

  • The order-of-magnitude agreement is demonstrated for one gas (hydrogen), one packing, and four low-Reynolds-number rates; nothing in the data says it generalizes to other gases, pressures, or rock types — the cross-study comparison in the paper is too confounded by medium differences to test this.
  • Because the 0.2 mobilization threshold shifts coefficients by up to 54% (Table 6), the reported agreement may be partly a consequence of that particular cutoff; sweeping the threshold or weighting intervals continuously would test whether the agreement is a property of the physics or of the filter.
  • The CPC negative-concentration region could be repurposed as a quantitative diagnostic: fitting a spatially resolved (per-region) coefficient at the leading edge would measure how much front dissolution exceeds the bulk average, and could be validated against independent effluent-concentration measurements.
  • SAC's dilution error is itself informative: the gap between SAC and per-cluster concentration estimates measures how non-uniform the real dissolution field is, and mapping that gap across injection rates would quantify flow-rate effects on radial mixing.
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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

3 major / 5 minor

Summary. The paper applies three published micro-CT-based analytical frameworks — Slice-Averaged Concentration (SAC), Non-Classified per-Cluster (NPC), and Classified per-Cluster (CPC) — to the same time-lapsed H2 dissolution datasets from Patmonoaji et al. (2023) at four solvent injection rates. It introduces a volume-ratio mobilization filter to remove time intervals suspected of dissolution-driven cluster remobilization, then compares the resulting mass-transfer coefficients, concentration estimates, and Sherwood-Reynolds behavior. The central claims are that the three approaches estimate average mass-transfer coefficients within one order of magnitude of one another at each injection rate, that pore-scale concentration estimates diverge strongly (SAC dilutes, NPC violates 0–1 bounds, CPC yields negative values at the front), and that the practical choice among approaches should depend on the desired spatial detail versus computational cost.

Significance. If the claims hold, the paper provides a useful practical benchmark: it applies three published analytical frameworks side-by-side on common datasets, transparently reports data-retention statistics, and candidly acknowledges the absence of ground-truth measurements. The introduction of a mobilization filter and the systematic quantification of its effect (Table 6) is a useful contribution, as is the dimensionless Sherwood-Reynolds comparison. The paper is also appropriately hedged in several places, noting that previous studies are not directly comparable. However, the central comparison is currently undermined by an inconsistency in whether the SAC values are recomputed on the filtered, three-phase segmented data or taken from the original study, and the back-calculated concentration fields are algebraic functions of the very average they are used to interpret. These issues need to be resolved before the comparative claims can be taken as established.

major comments (3)
  1. [§4.1 and §4.4, Fig. 6] The basis of the SAC comparison is internally inconsistent. Section 4.1 states that 'the same post-filtration intervals were used to evaluate the SAC, and NPC approaches as well, to ensure data uniformity,' but the Figure 6 caption says 'The K_seq mean SAC values obtained from Patmonoaji et al. (2023) are in purple.' The original study used two-phase segmentation and did not apply the mobilization filter. Table 6 shows that this filter changes the per-cluster K_seq estimates by up to +54% (NPC, Seq. 0.25) and -38% (CPC, Seq. 0.10). If the SAC values are indeed taken from Patmonoaji et al. (2023), then the one-order-of-magnitude agreement is not a same-data comparison and the central claim is not supported as presented. If the SAC values were recomputed, the figure and methods must be corrected to state so explicitly and to report the recomputed values.
  2. [§3.4, Eq. (13) and §4.3] The normalized aqueous concentrations for the per-cluster approaches are not independent estimates: Eq. (13) back-calculates C_i/C_sol using the same K_seq_ave that the paper estimates from the cluster volume changes. For a shrinking cluster with S_tot = S_aff, Eq. (13) reduces, algebraically, to C_i/C_sol = 1 - k_i/K_seq_ave. Thus C_i/C_sol < 0 is equivalent to k_i > K_seq_ave, and C_i/C_sol > 1 is equivalent to a positive volume change. The statement in §4.3 that the CPC approach 'yields unexpected negative aqueous concentrations' and the interpretation of the negative front as a region where the dissolution front is 'increasingly underestimated' are therefore interpretations of residuals from the sequence average, not independent concentration measurements. This needs to be reframed as a diagnostic of mismatch between local and averaged k, or validated against an independent concentra
  3. [§4.1, Table 6] The mobilization filter threshold is selected post hoc: ΔV_gained/|ΔV_lost| ≤ 0.2 was chosen to 'ensure sufficient data remained.' Table 6 shows that the filter changes final K_seq estimates by tens of percent for some sequences. Because the central comparison is conducted on post-filter intervals, the sensitivity of the cross-approach agreement to this threshold should be quantified. If the one-order-of-magnitude agreement persists over a range of thresholds, the claim is robust; if not, the reported agreement may inherit a selection bias from the filter.
minor comments (5)
  1. [§3.3] Typo: 'The time fo each scan' should be 'The time of each scan.'
  2. [§3.4, Eq. (11)] Eq. (11) uses S_atot,i in the denominator, while Eq. (13) uses S_aff,i. The relationship between these areas for partially dissolved versus completely dissolved clusters should be stated explicitly, especially since the NPC implementation is described as using the 'average fluid-fluid interfacial area.'
  3. [§4.2 and Fig. 4] The notation for the SAC interval average alternates between K_int_mean and 'saturation average.' Also, Figure 4's caption says 'Seq. 1.00' while the text sometimes uses 'Seq. 1.0'; please standardize.
  4. [Table 6] The 'Difference [%]' column should specify whether the percentage is relative to the unfiltered value and how the confidence intervals propagate through the filtering step.
  5. [§2, Table 2] The H2 density and solubility are given without uncertainty or the pressure/temperature provenance beyond a single line; if available from Patmonoaji et al. (2023), a citation would be helpful.

Circularity Check

1 steps flagged · score 6.0 of 10

Per-cluster concentration estimates are back-calculated from the very K_seq_ave they are compared against; central mass-transfer comparison is independent.

  1. self definitional [Section 4.3, Eq. (13), Fig. 5 (and Eq. (11), Section 3.4)]
    "For both the NPC and the CPC approaches, the concentration is back-calculated using the previously estimated K_seq_ave value and Eq. (13). ... Ci/Csol<0 represent underestimated clusters (where k_i > K_seq_ave, for ΔV_i), and Ci/Csol>1 represent overestimated clusters (where k_i < K_seq_ave, for ΔV_i)."

    Eq. (13) is Eq. (11) algebraically rearranged with the sequence average K_seq_ave replacing the local k_i. From Eq. (11), k_i = -(ΔV_i/Δt)ρ_gas/(S_atot,i Csol), so Eq. (13) gives C_i/Csol = 1 - (S_atot,i/S_aff,i)(k_i/K_seq_ave). Thus C_i/Csol<0 holds exactly when k_i exceeds the weighted mean K_seq_ave (and C_i/Csol>1 when ΔV_i>0). The 'negative concentrations at the dissolution front' are therefore a restatement of the scatter of per-cluster k_i around its own weighted average, not an independent physical concentration measurement. The paper discloses the back-calculation but presents the out-of-bounds values as evidence that the per-cluster approaches diverge from SAC; that portion of the comparison is fixed by the defining equations.

full rationale

The central claim—that SAC, NPC, and CPC estimate average mass-transfer coefficients within one order of magnitude—is not circular: the K_seq_ave values are computed from imaged cluster volume changes and surface areas via Eq. (11) (CPC/NPC) and from saturation changes via Eq. (6) (SAC), not fitted to force agreement. The per-cluster concentration analysis, however, is self-referential: Eq. (13) defines C_i using K_seq_ave, so the finding that CPC/NPC produce out-of-bounds concentrations, and that CPC gives negative values at the leading edge, is algebraically equivalent to reporting that some k_i deviate from their own weighted average. This is a genuine but bounded circularity; it does not undermine the mass-transfer-coefficient comparison. A separate consistency concern (not scored as circularity) is that Section 4.1 says the same filtered intervals were used for SAC, while the Fig. 6 caption says SAC K_seq values were obtained from Patmonoaji et al. (2023); if those are unfiltered values from the original 2-phase processing, that is a comparability flaw rather than a definitional circularity. No load-bearing uniqueness theorems or self-citation chains are invoked; the methods from Huang et al. (2023) and Lv et al. (2024) are applied as published formulas. Score 6 reflects one self-definitional 'prediction' (the concentration divergence) while the primary mass-transfer result remains independently computed.

Assumptions & free parameters 5 free parameters · 9 assumptions · 0 invented entities

The central comparison rests on the shared thin-film model and on several per-approach modeling assumptions (1-D pseudo-steady flow for SAC; C_g=0 and complete-dissolution-for-front for per-cluster methods), plus a cluster of image-processing thresholds. The only genuinely novel degree of freedom introduced here is the mobilization filter threshold; the other thresholds are inherited from the cited methods.

free parameters (5)
  • Mobilization filter volume-ratio threshold = 0.2 (ΔV_gained/|ΔV_lost| ≤ 0.2)
    Chosen post hoc to flag intervals with suspected cluster remobilization. Table 6 shows that removing flagged intervals changes sequence-average K estimates by -38% to +54% depending on sequence and approach, so the choice is consequential.
  • Minimum cluster size cutoff = 50.40 µm (15 voxels, ~3 voxels equivalent spherical diameter)
    Clusters below the smallest pore size from a pore-network reconstruction of the dry scan are removed; this defines which objects count as gas clusters for all per-cluster statistics.
  • Cluster matching tolerances = center-of-geometry shift ≤ 4 voxels; volume change ≤ 99%
    Criteria for matching the same cluster between time-lapse scans; directly controls the labeling of dissolved, grown, snapped-off, and merging clusters.
  • Cluster classification volume-change thresholds = ±10% volume change
    Clusters shrinking >10% are 'partially dissolved', growing >10% are 'grown', and |ΔV|<10% are 'non-changed'; these labels determine the CPC sample pool.
  • Surface-area–volume linear best-fit relation = slope and intercept fit to cluster data
    Used instead of Huang et al. (2023)'s power-law to relate total surface area S_atot to volume; affects k_i values through Eq. (11).
assumptions (9)
  • domain assumption Thin-film/boundary-layer mass-transfer model
    Eqs. (2)–(3): dissolution rate is set by diffusion through a stagnant film of thickness δ, with D and δ lumped into k; this is the shared basis of all three approaches.
  • domain assumption Constant gas density
    Between Eqs. (2) and (3): ρ_g is constant for small temperature/pressure perturbations, so dmg/dt = ρ_g dVg/dt.
  • domain assumption SAC 1-D pseudo-steady advection-dominant flow
    Section 1.2.2: Eq. (4) reduced to Eq. (5) under pseudo-steady state, 1-D, advection-dominant, radial-uniformity, and C(x=0,t)=0; used to forward-calculate concentrations.
  • domain assumption NPC: all cluster volume changes are mass transfer and C_g = 0
    Section 1.2.3: Eq. (7) applied to every cluster change, assuming maximum dissolution gradient and that non-mass-transfer events are a small share that the law of large numbers averages out.
  • domain assumption CPC: completely dissolved clusters define the dissolution front
    Section 1.2.3: Eq. (7) applied only to completely dissolved clusters, assuming they occur where the dissolution gradient is maximum; also assumes their full surface contacts solvent over part of the interval.
  • domain assumption Linear volume change over time interval
    Section 3.4: cluster volume change is assumed linear over the observation interval when converting ΔV/Δt to a rate.
  • domain assumption Three-phase segmentation accuracy despite overlapping water/grain attenuation
    Section 3.2: wet scans are segmented into gas, water, and grain using supplementary criteria; errors here propagate into cluster volumes, areas, and classifications.
  • domain assumption Law of large numbers validity for NPC
    Section 4.2: NPC results assume true mass-transfer events significantly outnumber fictitious events in the measurement pool; if not, the NPC average is biased.
  • domain assumption Pore-network reconstruction of dry scan for pore-size distribution
    Section 3.2: used to set minimum cluster size; the reconstruction itself is not validated within this paper.

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Pith. "Pith review of Quantifying Injection-Driven Interphase Mass Transfer within Porous Media via Time-Elapsed X-ray micro-Computed Tomography." pith.science (2026). https://pith.science/paper/TS23VLVV

@misc{pith2026260407743,
  author       = {Pith},
  title        = {Pith review of: Quantifying Injection-Driven Interphase Mass Transfer within Porous Media via Time-Elapsed X-ray micro-Computed Tomography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TS23VLVV}},
  note         = {Machine review of arXiv:2604.07743}
}
abstract

Understanding interphase mass transfer is essential for a variety of applications in porous media, ranging from groundwater remediation to geologic energy storage. While X-ray micro-Computed Tomography ($\mu$CT) provides critical \textit{in situ} observations, its application in quantifying mass transfer phenomena requires models and workflows compatible with spatial and temporal constraints. Current literature presents three analytical frameworks for evaluating interphase mass transfer using time-lapsed sequences of $\mu$CT scans: the Slice-Averaged Concentration (SAC) approach, the Non-Classified per-Cluster (NPC) approach, and the Classified per-Cluster (CPC) approach. Comparing results with previous studies, we identify that further research is needed to understand how these approaches will vary with experimental conditions and how the physical implications of their calculation frameworks should affect the interpretation of the results, as there are often no ground-truth measurements to compare the estimates to. The current study systematically evaluates the frameworks and results of the three approaches as applied to several sequences of time-lapsed $\mu$CT scans, each observing hydrogen dissolution experiments at varying injection rates. For each observed advective injection rate, results indicate that system-scale properties, like mass transfer, appear robust to the selected approach. However, approach estimates diverged when approximating more complex, pore-scale phenomena, such as aqueous solute concentration. Ultimately, the utility of one approach over another is determined by the desired level of system detail, at the cost of the computational resources required to achieve it. Our results provide a framework for researchers to select analytical approaches based on available computational resources and the desired level of physical detail.

Figures

Figures reproduced from arXiv: 2604.07743 by the authors.

Figure 1
Figure 1. Volume weighted size distribution using the equivalent spherical di [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Time-lapsed reconstructions of cluster positions, from sequence 0.50 mL/ [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Bar charts of the ∆volume gained |∆volume lost| in each interval. ∆volume gained |∆volume lost| ≤ 0.2 cutoff are blue, interval values 0.2 ≤ ∆volume gained |∆volume lost| ≤ 0.5 are pink, and interval values ∆volume gained |∆volume lost| ≥ 0.5 are in red. The graph is truncated at 1; some values extend to orders of magnitude beyond 1. sampled does come with the inherent cost that the final estimate is more sensitive … view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Box and whisker plots of the mass transfer coe [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: The ranges of estimated concentrations from each approach, normalized by the solubility limit. The back-calculated concentrations around each cluster [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Comparison of the K seq for each of the approaches, at each injection rate. The K seq ave for the per-cluster approaches are shown in red (CPC) and blue (NPC). The error bars represent the 99% confidence interval for each estimated value, with respect to the weighted d…
Figure 7
Figure 7. Figure 7: plot of the Sherwood numbers (S h) versus the Reynolds numbers (Re) for each approach: CPC (red), NPC (blue), and the SAC (purple). as a function of the solvent injection rate (post-mobilization fil￾tering) are shown in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: The estimated mass transfer coefficients from various studies, against the interstitial velocity (left), and in dimensionless form (right) as the Sherwood number and the Reynolds number. The shape of the marker denotes the approach used for the data: Slice-Averaged Con…

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Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.