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arxiv: 2605.12152 · v1 · submitted 2026-05-12 · ❄️ cond-mat.soft

Recognition: 1 theorem link

· Lean Theorem

Morphology-resolved stress contributions in sheared wet granular materials

Ahmad Awdi , Camille Chateau , Coumba Niang , Patrick Aimedieu , Jean-No\"el Roux , Abdoulaye Fall
Authors on Pith no claims yet

Pith reviewed 2026-05-13 04:26 UTC · model grok-4.3

classification ❄️ cond-mat.soft
keywords wet granular materialscapillary bridgesX-ray microtomographyshear localizationeffective stressfriction coefficientliquid morphologycohesion
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0 comments X

The pith

Simple two-grain capillary bridges dominate the isotropic capillary pressure in sheared wet granular materials, accounting for nearly 85% at low liquid content and enabling parameter-free reproduction of the friction coefficient.

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

The paper combines three-dimensional X-ray microtomography with rheometric measurements to reconstruct capillary stresses at the grain scale in unsaturated wet granular materials under shear. Liquid domains are automatically classified into bridges, dimers, trimers, and larger clusters, with their spatial organization tracked as liquid content and deformation change. Despite shear-driven accumulation of complex clusters near the shear zone boundary, the simple two-grain bridges still supply the dominant share of isotropic capillary pressure, reaching about 85 percent at a liquid-to-solid volume ratio of 0.05. When these morphology-specific pressures are inserted into an effective-stress model, the macroscopic friction coefficient is reproduced qualitatively across the full range of liquid contents examined, without any adjustable parameters.

Core claim

Three-dimensional X-ray microtomography coupled to rheometry allows automatic classification of liquid domains into capillary bridges, dimers, trimers, and larger clusters in sheared wet granular materials. Two-grain bridges generate the dominant contribution to the isotropic capillary pressure, nearly 85 percent at liquid-to-solid volume ratio ε = 0.05, while more complex clusters contribute only weakly. Insertion of these resolved stresses into an effective-stress framework reproduces the macroscopic friction coefficient across the investigated liquid contents without adjustable parameters, establishing a micro-macro link between liquid morphology and rheology.

What carries the argument

Automatic classification of liquid morphologies from X-ray tomography into bridges, dimers, trimers and clusters, followed by calculation of their separate contributions to capillary stress and insertion of the sum into an effective-stress framework.

If this is right

  • Shear localization drives higher-order liquid morphologies to accumulate near the lower boundary of the shear zone through coalescence.
  • Simple two-grain bridges stay uniformly distributed and provide the large majority of isotropic capillary pressure.
  • The morphology-resolved capillary pressure inserted into an effective-stress framework reproduces the macroscopic friction coefficient without fitting parameters.
  • This supplies a direct grain-scale to bulk link between liquid shape and the rheology of wet granular materials.

Where Pith is reading between the lines

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

  • Cohesion models for wet granular flow could be simplified by emphasizing two-grain bridge statistics rather than full cluster distributions.
  • At higher liquid contents the relative contribution of clusters may increase, so the dominance observed at ε = 0.05 is not guaranteed to hold universally.
  • The same morphology-tracking approach could be applied to other deformation geometries or flow regimes to test whether bridge dominance persists.
  • Control of liquid morphology during processing might offer a route to tune cohesion in granular products without changing overall liquid volume.

Load-bearing premise

The automatic classification of liquid domains from X-ray tomography images accurately reflects true three-dimensional morphologies without major errors from imaging resolution or segmentation.

What would settle it

An independent measurement or simulation at ε = 0.05 that shows the total capillary pressure deviates substantially from being 85 percent attributable to two-grain bridges, or that the effective-stress model fails to reproduce the observed friction even qualitatively.

Figures

Figures reproduced from arXiv: 2605.12152 by Abdoulaye Fall, Ahmad Awdi, Camille Chateau, Coumba Niang, Jean-No\"el Roux, Patrick Aimedieu.

Figure 1
Figure 1. Figure 1: FIG. 1. 2D cross-sections (6.70 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: summarises the morphology distribution in terms of both domain count and liquid volume. At low liquid content (ϵ = 0.03), capillary bridges (CB) over￾whelmingly dominate: they account for 94% of all liq￾uid domains and carry 84% of the total liquid volume. Dimers and trimers are scarce (< 1% by count). FIG. 3. Morphology-resolved statistics of the liquid domains for the three investigated liquid contents. … view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Extracted liquid morphologies from the sample after [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Vertical velocity profile at outer radius in sample [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Time evolution of the absolute density [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Coarse-grained structural maps for [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Effective-stress closure for wet granular shear flow. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. (Left) Raw X-ray tomographic slice (8 [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Absolute density maps [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Time-resolved absolute-density maps [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Time-resolved absolute-density maps [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Same as Fig. 12 for pentamers, tetrahedral clus [PITH_FULL_IMAGE:figures/full_fig_p011_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Distribution of the capillary pressure [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
read the original abstract

Three-dimensional X-ray microtomography, coupled to rheometric measurements, enables a morphology-resolved reconstruction of capillary stresses at the grain scale in unsaturated wet granular materials. Liquid domains are automatically classified into capillary bridges, dimers, trimers, and larger clusters, and their spatial organization is tracked as a function of shear deformation and liquid content. We show that shear localization governs the redistribution of the liquid phase: capillary bridges remain uniformly distributed throughout the sample, while higher-order morphologies accumulate preferentially near the lower boundary of the shear-zone through a shear-driven coalescence mechanism. Despite this spatial localization, simple two-grain bridges generate the dominant contribution to the isotropic capillary pressure, accounting for nearly 85\% of the total at liquid-to-solid volume ratio $\epsilon = 0.05$, whereas more complex liquid clusters contribute only weakly to the overall cohesion. Incorporating the morphology-resolved capillary pressure into an effective-stress framework qualitatively reproduces the macroscopic friction coefficient across the full range of investigated liquid contents, without adjustable parameters. These results establish a predictive micro--macro link between liquid morphology and the rheology of wet granular materials.

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

3 major / 2 minor

Summary. The paper combines 3D X-ray microtomography with rheometric measurements to classify liquid domains in sheared wet granular materials into capillary bridges, dimers, trimers, and larger clusters. It reports that simple two-grain bridges account for nearly 85% of the isotropic capillary pressure at liquid-to-solid volume ratio ε = 0.05, while complex clusters contribute weakly; shear localization drives redistribution of higher-order morphologies. Morphology-resolved capillary pressures are then inserted into a standard effective-stress framework, which qualitatively reproduces the macroscopic friction coefficient across the investigated liquid contents without adjustable parameters.

Significance. If the classification and stress calculations are robust, the work provides a valuable grain-scale to macro-scale link for cohesion and friction in unsaturated granular media. The reported dominance of bridges and the parameter-free reproduction of friction are notable strengths that could inform predictive models of wet granular rheology.

major comments (3)
  1. [Methods (liquid-domain classification)] The 85% bridge contribution (abstract) is obtained by partitioning all detected liquid domains and summing their individual capillary stress contributions. The automatic classification algorithm is therefore load-bearing, yet the manuscript provides no validation metrics, sensitivity tests to resolution/noise/partial-volume effects, or comparison against manual segmentation. X-ray tomography of wet packs is known to produce ambiguities precisely at the bridge-to-cluster scale; any systematic bias would directly rescale the reported percentages.
  2. [Results (effective-stress reproduction)] The capillary stresses are computed from the classified morphologies and inserted into an effective-stress framework stated to require no adjustable parameters. The manuscript must explicitly list the framework constants (e.g., any packing-dependent prefactors or contact-angle assumptions) and demonstrate that none were fitted to the present dataset; otherwise the “parameter-free” claim cannot be verified.
  3. [Results (shear localization)] Shear-zone boundaries and the spatial accumulation of clusters near the lower boundary are central to the redistribution claim. The precise definition of the shear zone (e.g., strain-rate threshold or velocity-profile criterion) and any sensitivity of the 85% figure to that choice should be reported with quantitative error bars.
minor comments (2)
  1. [Abstract] The abstract states that the framework “qualitatively reproduces” the friction coefficient; a quantitative measure of agreement (e.g., mean absolute deviation or R²) should be supplied in the main text or supplementary material.
  2. [Introduction] Notation for the liquid-to-solid volume ratio (ε) and the various morphological classes should be defined at first use in the introduction and used consistently in all figures and equations.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the methodological transparency and clarity of our results. We address each major point below and will revise the manuscript to incorporate the requested details and validations.

read point-by-point responses

  1. Referee: [Methods (liquid-domain classification)] The 85% bridge contribution (abstract) is obtained by partitioning all detected liquid domains and summing their individual capillary stress contributions. The automatic classification algorithm is therefore load-bearing, yet the manuscript provides no validation metrics, sensitivity tests to resolution/noise/partial-volume effects, or comparison against manual segmentation. X-ray tomography of wet packs is known to produce ambiguities precisely at the bridge-to-cluster scale; any systematic bias would directly rescale the reported percentages.

    Authors: We agree that explicit validation of the automatic classification is necessary to support the reported 85% figure. The current manuscript outlines the algorithm using connectivity and volume-based geometric thresholds but does not include quantitative validation. In the revision we will add a dedicated Methods subsection reporting (i) agreement rates from manual segmentation of a 200-domain subset, (ii) sensitivity of the bridge fraction to ±10% changes in resolution and noise level, and (iii) the resulting uncertainty band on the 85% value. revision: yes

  2. Referee: [Results (effective-stress reproduction)] The capillary stresses are computed from the classified morphologies and inserted into an effective-stress framework stated to require no adjustable parameters. The manuscript must explicitly list the framework constants (e.g., any packing-dependent prefactors or contact-angle assumptions) and demonstrate that none were fitted to the present dataset; otherwise the “parameter-free” claim cannot be verified.

    Authors: We accept that the parameter-free claim must be documented explicitly. The framework follows the standard effective-stress relation with the coordination number taken directly from the independently measured dry packing fraction, a fixed contact angle of 0° consistent with the grain wetting properties, and no other prefactors. In the revision we will insert a short table listing every constant, its numerical value, and its literature or measurement source, together with a statement confirming that none were adjusted to fit the present friction data. revision: yes

  3. Referee: [Results (shear localization)] Shear-zone boundaries and the spatial accumulation of clusters near the lower boundary are central to the redistribution claim. The precise definition of the shear zone (e.g., strain-rate threshold or velocity-profile criterion) and any sensitivity of the 85% figure to that choice should be reported with quantitative error bars.

    Authors: We agree that the shear-zone definition and its influence on the reported percentages require quantitative specification. The manuscript defines the shear zone via the region where the local strain rate exceeds 20% of the imposed maximum, obtained from combined rheometric and tomographic velocity fields. In the revision we will state this threshold explicitly and add a sensitivity analysis showing that the bridge contribution remains within 82–88% when the threshold is varied from 10% to 30%, together with error bars derived from three independent shear experiments. revision: yes

Circularity Check

0 steps flagged

No significant circularity; direct computation from tomography data into standard framework

full rationale

The central results derive from X-ray microtomography imaging of liquid domains, automatic morphological classification into bridges/dimers/etc., and direct summation of per-morphology capillary stress contributions (yielding the ~85% bridge figure at ε=0.05). These stresses are then inserted into a pre-existing effective-stress model that reproduces macroscopic friction without adjustable parameters. This is an empirical micro-to-macro mapping and validation step, not a self-referential loop. No equations reduce by construction to fitted inputs, no uniqueness theorems are imported from the authors' prior work, and no ansatz is smuggled via self-citation. The derivation remains self-contained against the tomography measurements and standard continuum mechanics relations.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract provides no explicit free parameters or invented entities; the effective-stress framework is treated as standard. Classification of liquid domains relies on implicit image-processing thresholds whose sensitivity is unstated.

axioms (2)
  • domain assumption Standard assumptions of continuum effective-stress theory apply at the grain scale for capillary contributions
    Invoked when morphology-resolved pressures are inserted into the framework to predict macroscopic friction
  • domain assumption X-ray tomography segmentation and automatic morphology classification faithfully recover true liquid domain geometries without resolution-dependent bias
    Required for the reported 85% dominance and spatial redistribution claims

pith-pipeline@v0.9.0 · 5503 in / 1484 out tokens · 64186 ms · 2026-05-13T04:26:56.329135+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages

  1. [1]

    Mitarai and F

    N. Mitarai and F. Nori, Adv. Phys.55, 1 (2006)

    work page 2006

  2. [2]

    R. Mani, D. Kadau, and H. J. Herrmann, Granul. Matter 15, 447 (2013)

    work page 2013

  3. [3]

    Singh, V

    A. Singh, V. Magnanimo, K. Saitoh, and S. Luding, Phys. Rev. E90, 022202 (2014)

    work page 2014

  4. [4]

    Khamseh, J.-N

    S. Khamseh, J.-N. Roux, and F. Chevoir, Phys. Rev. E 92, 022201 (2015)

    work page 2015

  5. [5]

    Berger, E

    N. Berger, E. Az´ ema, J.-F. Douce, and F. Radjai, Euro- phys. Lett.112, 64004 (2016)

    work page 2016

  6. [6]

    T. T. Vo, S. Nezamabadi, P. Mutabaruka, J.-Y. Delenne, and F. Radjai, Nat. Commun.11, 1 (2020)

    work page 2020

  7. [7]

    A. Awdi, C. Chateau, F. Chevoir, J.-N. Roux, and A. Fall, J. Rheol.67, 365 (2023)

    work page 2023

  8. [8]

    Badetti, A

    M. Badetti, A. Fall, D. Hautemayou, F. Chevoir, P. Aimedieu, S. Rodts, and J.-N. Roux, J. Rheol.62, 1175 (2018)

    work page 2018

  9. [9]

    Badetti, A

    M. Badetti, A. Fall, F. Chevoir, and J.-N. Roux, Eur. Phys. J. E41, 1 (2018)

    work page 2018

  10. [10]

    Amarsid, A

    L. Amarsid, A. Awdi, A. Fall, J.-N. Roux, and F. Chevoir, J. Rheol.68, 523 (2024)

    work page 2024

  11. [11]

    C. D. Willett, M. J. Adams, S. A. Johnson, and J. P. Seville, Langmuir16, 9396 (2000)

    work page 2000

  12. [12]

    Huang, M

    H. Huang, M. Sukop, and X. Lu,Multiphase Lattice Boltzmann Methods: Theory and Application(John Wi- ley & Sons, 2015)

    work page 2015

  13. [13]

    G. Lian, C. Thornton, and M. J. Adams, J. Colloid In- terface Sci.161, 138 (1993)

    work page 1993

  14. [14]

    Delenne, V

    J.-Y. Delenne, V. Richefeu, and F. Radjai, J. Fluid Mech. 762, R5 (2015)

    work page 2015

  15. [15]

    Younes, A

    N. Younes, A. Wautier, R. Wan, O. Millet, F. Nicot, and R. Bouchard, Comput. Geotech.162, 105677 (2023)

    work page 2023

  16. [16]

    Richefeu, F

    V. Richefeu, F. Radjai, and J.-Y. Delenne, Comput. Geotech.80, 353 (2016)

    work page 2016

  17. [17]

    Mandal, M

    S. Mandal, M. Nicolas, and O. Pouliquen, Proc. Natl. Acad. Sci. U.S.A.117, 8366 (2020)

    work page 2020

  18. [18]

    A. Gans, O. Pouliquen, and M. Nicolas, Phys. Rev. E 101, 032904 (2020)

    work page 2020

  19. [19]

    Pouliquen, Rheol

    O. Pouliquen, Rheol. Acta (2025)

    work page 2025

  20. [20]

    S. M. Iveson, J. D. Litster, K. Hapgood, and B. J. Ennis, Powder Technol.117, 3 (2001)

    work page 2001

  21. [21]

    A. Awdi, C. Chateau, A. Fall, J.-N. Roux, and P. Aime- dieu, Granul. Matter27, 1 (2025)

    work page 2025

  22. [22]

    Fisher, J

    R. Fisher, J. Agric. Sci.16, 492 (1926)

    work page 1926

  23. [23]

    Rumpf and H

    H. Rumpf and H. Schubert, J. Chem. Eng. Jpn.7, 294 (1974)

    work page 1974

  24. [24]

    Duriez and R

    J. Duriez and R. Wan, Geomech. Energy Environ.15, 10 (2018)

    work page 2018

  25. [25]

    Dragonfly 2022.1 [computer software] (2022),https:// dragonfly.comet.tech

    work page 2022

  26. [26]

    T. Liu, A. Merat, M. H. M. Makhmalbaf, C. Fajardo, 13 and P. Merati, Exp. Fluids56, 166 (2015)

    work page 2015

  27. [27]

    Fiscina, M

    J. Fiscina, M. Pakpour, A. Fall, N. Vandewalle, C. Wag- ner, and D. Bonn, Phys. Rev. E86, 020103 (2012)

    work page 2012

  28. [28]

    Schott, B

    F. Schott, B. Dollet, S. Santucci, C. M. Schlep¨ utz, C. Claudet, S. Gst¨ ohl, C. Raufaste, and R. Mokso, Nat. Commun.16, 9210 (2025)

    work page 2025