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arxiv: 2604.07743 · v1 · submitted 2026-04-09 · ⚛️ physics.flu-dyn · physics.data-an· physics.geo-ph

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

Christopher A. Allison , Ruotong Huang , Anindityo Patmonoaji , Lydia Knuefing , Anna L. Herring This is my paper

Pith reviewed 2026-05-10 18:33 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn physics.data-anphysics.geo-ph
keywords mass transferporous mediaX-ray microCTdissolutionhydrogenanalytical approachestime-lapse imaginginjection rate
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0 comments X

The pith

Three analytical methods for mass transfer in porous media from microCT images produce average coefficients within one order of magnitude.

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

This paper evaluates three ways to calculate interphase mass transfer rates from time-lapse X-ray images of dissolving hydrogen in porous rock. A volume-ratio filter is added to each method to focus on actual dissolution rather than moving gas clusters. At any given injection rate of the solvent, the Slice-Averaged Concentration, Non-Classified per-Cluster, and Classified per-Cluster methods give average mass transfer coefficients that differ by no more than a factor of ten. This agreement lets researchers pick the method that fits their computing budget and the level of detail they need for applications such as groundwater cleanup or underground energy storage.

Core claim

Our analysis finds that all three analytical approaches estimate average mass transfer coefficients within one order of magnitude of one another at the same solvent injection rate. However, the similarity between the estimates of each approach diverges when approximating more complex phenomena, such as aqueous solute concentration profiles. 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. Higher phenomenological resolution requires greater computational processing and refinement due to increased sensitivity to measurement and processing noise, as well as outlier events.

What carries the argument

The three analytical frameworks—Slice-Averaged Concentration (SAC), Non-Classified per-Cluster (NPC), and Classified per-Cluster (CPC)—combined with a volume-ratio filtering technique to isolate mass transfer events in microCT data.

If this is right

  • Researchers can choose simpler methods for average rates without significant loss in accuracy for basic estimates.
  • Methods requiring more detail demand more computation and are more prone to noise effects.
  • Estimates remain consistent across injection rates when using the filtering technique.
  • The approaches are suitable for matching computational resources to needed physical insight in mass transfer studies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar agreement might hold for other dissolving systems like CO2 in water or oil recovery processes.
  • Integrating these methods with larger-scale simulations could improve predictions for field applications.
  • Testing the filter on synthetic data with known mass transfer would validate its bias removal.
  • The divergence in complex profiles suggests room for hybrid approaches that combine strengths of each method.

Load-bearing premise

The volume-ratio filtering technique successfully removes biases from dissolution-driven cluster remobilization without discarding valid mass-transfer events or introducing new artifacts.

What would settle it

Running the three filtered approaches on additional microCT datasets at varying injection rates and finding that the average mass transfer coefficients differ by more than one order of magnitude would falsify the core comparability claim.

Figures

Figures reproduced from arXiv: 2604.07743 by Anindityo Patmonoaji, Anna L. Herring, Christopher A. Allison, Lydia Knuefing, Ruotong Huang.

Figure 1
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. 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. 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 … view at source ↗
Figure 4
Figure 4. Figure 4: Box and whisker plots of the mass transfer coe [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
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] view at source ↗
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 distributions. The K seq mean SAC values ob￾tained from Patmonoaji et al. (2023) are in purple. The estimated mass transfer coefficients ( K … view at source ↗
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] view at source ↗
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 Concentration (SAC: triangles), non-classified cluster point cloud (NPC: filled circles), a mix of the former approaches (pentagons; Hu et al. … view at source ↗
read the original 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 (microCT) provides critical in situ observations, analyzing mass transfer requires models and workflows compatible with the limited spatial and temporal resolution. Current literature presents three analytical frameworks for evaluating interphase mass transfer using microCT data: the Slice-Averaged Concentration (SAC) approach, the Non-Classified per-Cluster (NPC) approach, and the Classified per-Cluster (CPC) approach. This study evaluates the results of all three approaches across four sets of time-lapse tomography sequences that observe hydrogen dissolution at varying solvent injection rates. To mitigate biases arising from dissolution-driven cluster remobilization, we introduce a volume-ratio filtering technique to all workflows to ensure that estimates more accurately reflect true mass transfer events. Our analysis finds that all three analytical approaches estimate average mass transfer coefficients within one order of magnitude of one another at the same solvent injection rate. However, the similarity between the estimates of each approach diverges when approximating more complex phenomena, such as aqueous solute concentration profiles. 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. Higher phenomenological resolution requires greater computational processing and refinement due to increased sensitivity to measurement and processing noise, as well as outlier events. We anticipate that the findings will provide a framework for researchers to match analytical approaches to their available computational resources and desired level of physical detail.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper compares three analytical frameworks—Slice-Averaged Concentration (SAC), Non-Classified per-Cluster (NPC), and Classified per-Cluster (CPC)—for quantifying interphase mass transfer from time-lapse X-ray microCT images of hydrogen dissolution in porous media. It introduces a volume-ratio filtering technique to reduce biases from dissolution-driven cluster remobilization and reports that the three methods yield average mass transfer coefficients within one order of magnitude for the same solvent injection rates, while diverging in their ability to capture complex phenomena like solute concentration profiles. The work discusses trade-offs between phenomenological resolution and computational demands.

Significance. If the filtering approach is shown to be robust, the study supplies a practical decision framework for matching analysis methods to computational resources and desired physical detail in porous-media mass-transfer studies. Credit is given for the experimental time-lapse microCT sequences acquired at multiple injection rates and for the explicit side-by-side evaluation of three existing workflows.

major comments (2)
  1. [Methods section describing the volume-ratio filtering technique] The volume-ratio filtering technique (introduced to mitigate dissolution-driven cluster remobilization) is central to the reported order-of-magnitude agreement among SAC, NPC, and CPC, yet the manuscript supplies no sensitivity analysis, synthetic-data validation, or quantitative justification for the chosen ratio threshold. Different thresholds could systematically alter cluster retention and averaging differently across the three methods, undermining the claim that the agreement reflects true mass-transfer behavior rather than filter-induced convergence.
  2. [Abstract and Results] The abstract and results state that all three approaches produce average mass-transfer coefficients within one order of magnitude, but provide no numerical values, error bars, or explicit description of how noise and outliers were handled after filtering. This absence makes it impossible to evaluate whether the reported agreement is statistically meaningful or sensitive to processing choices.
minor comments (2)
  1. [Abstract] The abstract refers to “four sets of time-lapse tomography sequences” without stating the specific injection rates or the number of time points per sequence; adding these details would improve context for the comparative findings.
  2. [Introduction] Notation for the three approaches (SAC, NPC, CPC) is introduced without a concise table summarizing their key differences in cluster handling and averaging; such a table would aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment point-by-point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Methods section describing the volume-ratio filtering technique] The volume-ratio filtering technique (introduced to mitigate dissolution-driven cluster remobilization) is central to the reported order-of-magnitude agreement among SAC, NPC, and CPC, yet the manuscript supplies no sensitivity analysis, synthetic-data validation, or quantitative justification for the chosen ratio threshold. Different thresholds could systematically alter cluster retention and averaging differently across the three methods, undermining the claim that the agreement reflects true mass-transfer behavior rather than filter-induced convergence.

    Authors: We agree that the lack of a sensitivity analysis for the volume-ratio threshold represents a gap in the current manuscript. The threshold was selected based on preliminary examination of the time-lapse sequences to exclude clusters exhibiting clear remobilization while preserving those primarily undergoing dissolution. To address the referee's concern rigorously, we will add a dedicated sensitivity study to the revised Methods section. This will test a range of ratio thresholds, quantify their differential impact on cluster retention and mass-transfer estimates across the SAC, NPC, and CPC frameworks, and include synthetic data validation where feasible to demonstrate robustness. These additions will clarify that the reported agreement is not an artifact of the specific filter choice. revision: yes

  2. Referee: [Abstract and Results] The abstract and results state that all three approaches produce average mass-transfer coefficients within one order of magnitude, but provide no numerical values, error bars, or explicit description of how noise and outliers were handled after filtering. This absence makes it impossible to evaluate whether the reported agreement is statistically meaningful or sensitive to processing choices.

    Authors: We concur that providing explicit numerical values, error bars, and processing details is necessary for a complete evaluation. In the revised manuscript, we will update both the abstract and Results section to report the specific average mass-transfer coefficient values (including their orders of magnitude) for each method at the tested injection rates. We will also incorporate error bars reflecting temporal or replicate variability and add a clear description of post-filtering procedures, including quantitative criteria for identifying and handling noise from imaging artifacts as well as outliers (e.g., via deviation thresholds from expected dissolution behavior). This will allow readers to assess the statistical robustness of the agreement. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison of image-processing workflows on experimental data

full rationale

The manuscript is an experimental study that applies three existing data-analysis workflows (SAC, NPC, CPC) to time-lapse microCT image sequences and introduces a volume-ratio filter as a preprocessing step. The reported result—that the three workflows yield mass-transfer coefficients within one order of magnitude—is an observed numerical outcome from the processed experimental volumes, not a mathematical derivation, fitted parameter, or self-referential definition. No equations are presented whose outputs are forced by construction to equal their inputs, and no load-bearing premise rests on a self-citation chain. The filter choice affects which events are retained, but the agreement among methods remains a contingent empirical finding rather than a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the effectiveness of the newly introduced filter and on the assumption that microCT images capture true mass-transfer events at the stated resolution; no free parameters or invented physical entities are described.

axioms (2)
  • domain assumption X-ray microCT provides critical in situ observations of phase distributions and mass transfer at sufficient spatial and temporal resolution.
    Stated directly in the abstract when introducing the data source.
  • ad hoc to paper The volume-ratio filtering technique isolates true mass-transfer events by removing only dissolution-driven remobilization artifacts.
    The paper introduces this step specifically to mitigate bias, and the reported findings depend on its validity.

pith-pipeline@v0.9.0 · 5611 in / 1384 out tokens · 89199 ms · 2026-05-10T18:33:05.202826+00:00 · methodology

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Reference graph

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