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arxiv: 2605.19845 · v1 · pith:72EJYKAOnew · submitted 2026-05-19 · ❄️ cond-mat.soft

Percolation of a cohesive fine particle in a static bed

Jizhi Zhang , Qiong Zhang , Julio M. Ottino , Paul B. Umbanhowar , Richard M. Lueptow This is my paper

Pith reviewed 2026-05-20 01:55 UTC · model grok-4.3

classification ❄️ cond-mat.soft
keywords percolationcohesive finesdiscrete element methodgranular materialsadhesiontrappingfrictionparticle interactions
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0 comments X

The pith

Cohesive fine particles trap in a bed of larger ones even when geometry allows passage.

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

The paper uses discrete element simulations to study gravity-driven percolation of cohesive fines through a static bed of larger particles where the size ratio of 7 would geometrically permit free passage for non-cohesive cases. It shows that sufficiently large cohesion and friction cause trapping because fines fail to rebound after collisions due to adhesion, low restitution, and low impact speed, with any later rolling or sliding too weak to detach them. This sequence of collision, adhesion, and post-contact motion determines the particle's fate instead of pore size alone. Industrial and natural processes that rely on fines moving or staying put in granular beds would behave differently under these interaction rules than geometry-based models predict.

Core claim

Sufficiently large cohesion and friction lead to non-geometric trapping of fine particles. Fines become trapped when they fail to rebound after a collision owing to large cohesion, low restitution, and low collision velocity, after which any rolling or sliding proves insufficient for detachment. This establishes that a sequence of local interactions—collision, adhesion, and post-contact motion—governs the ultimate fate of a fine particle. A collisional model with trapping probability per collision and collision frequency predicts trapping distance in the relevant regime, while a static force-balance condition predicts whether the particle remains stationary after contact.

What carries the argument

The sequence of collision followed by adhesion and post-contact motion that overrides geometric pore accessibility and decides whether the fine particle passes or sticks.

If this is right

  • Trapping distance follows directly from a simple model using measured trapping probability per collision and the local collision rate.
  • In non-rebounding collisions, cohesion amplifies frictional resistance enough to block later detachment.
  • A force-balance equilibrium check after contact can forecast whether a fine particle stays put.
  • Percolation of cohesive fines is set by particle-scale dynamics in addition to geometric accessibility.

Where Pith is reading between the lines

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

  • In dense fines flows, particle-particle contacts among the fines themselves could shorten or lengthen the predicted trapping distances.
  • The same adhesion-after-collision rule may control retention in aerosol filters or powder mixing equipment when cohesion is high.
  • Varying bed motion or flow speed would test whether the static-bed trapping mechanism persists or gives way to shear-induced release.

Load-bearing premise

Fine particles interact only with the larger bed particles and not with each other, while the static randomly packed bed and chosen contact models fully capture the physics without collective or multi-body effects altering single-particle outcomes.

What would settle it

An experiment that measures the average distance cohesive fines travel before trapping in a packed bed of known size ratio and cohesion strength, then checks whether those distances match the collisional model's predictions for varying restitution and friction.

Figures

Figures reproduced from arXiv: 2605.19845 by Jizhi Zhang, Julio M. Ottino, Paul B. Umbanhowar, Qiong Zhang, Richard M. Lueptow.

Figure 1
Figure 1. Figure 1: FIG. 1. (a) Example of a pore throat containing a fine par [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Comparison of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Positions of 10 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Positions of trapped fine particles for different cohe [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Examples of trapped particle distributions (PDF) [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: correspond to equivalent trapping distances, which indicates similar trapping behavior for various parameter combinations. Hence, trapping behavior is not governed by cohesion alone; sliding friction also plays a key role, particularly for µ ⪅ 0.25. 2. Effects of particle stiffness Particle stiffness, as controlled by the Young’s Modu￾lus, E, can play a significant role in cohesive trapping be￾havior. Whil… view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Inverse trapping distance, [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Inverse trapping distances, [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Examples of the four possible types of post-impact [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Effective restitution coefficient, [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Critical velocity of normal collision vs Bond number [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16. Number of collisions, [PITH_FULL_IMAGE:figures/full_fig_p010_16.png] view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15. Trapping probability per collision based on [PITH_FULL_IMAGE:figures/full_fig_p010_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: FIG. 17. Correlation between the inverse trapping dis [PITH_FULL_IMAGE:figures/full_fig_p011_17.png] view at source ↗
Figure 19
Figure 19. Figure 19: FIG. 19. Examples of double contact trapping under gravity [PITH_FULL_IMAGE:figures/full_fig_p012_19.png] view at source ↗
Figure 18
Figure 18. Figure 18: FIG. 18. Inverse trapping distance, [PITH_FULL_IMAGE:figures/full_fig_p012_18.png] view at source ↗
Figure 20
Figure 20. Figure 20: FIG. 20. Phase diagram of single contact fraction showing [PITH_FULL_IMAGE:figures/full_fig_p013_20.png] view at source ↗
read the original abstract

Percolation of fine particles (fines) in a static bed of larger particles is central to many industrial and natural processes. Non-cohesive fines either pass through the bed or become trapped depending on multiple factors including particle sizes, friction and restitution coefficients, and size-polydispersity. Here we consider the additional factor of cohesion. We use the discrete element method to simulate gravity-driven percolation of cohesive fine particles through a static bed of randomly packed large particles; fines interact with bed particles but not with each other. A large-to-fine particle diameter ratio of 7 geometrically permits non-cohesive fines to pass the narrowest pore throats formed by the large particles so they can freely percolate. However, sufficiently large cohesion and friction lead to non-geometric trapping. Fines are trapped when they fail to rebound after a collision, due to large cohesion, low restitution, and low collision velocity, and any subsequent rolling or sliding is insufficient to cause detachment. This establishes a sequence of local interactions -- collision, adhesion, and post-contact motion -- that governs the ultimate fate of a fine particle. A collisional model that incorporates a trapping probability per collision and a collision frequency predicts the trapping distance in the regime dominated by collision-induced trapping. For non-rebounding collisions, frictional effects are enhanced by cohesion and, when large enough, prevent the fine particle from subsequently detaching. A static equilibrium condition based on force balance predicts whether a fine particle remains stationary after contact. These results show that percolation of cohesive fine particles is not determined by geometric accessibility alone, but also by particle-scale interaction dynamics that can override geometric expectations.

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 / 1 minor

Summary. The paper claims that percolation of cohesive fine particles through a static bed of larger particles (diameter ratio 7, which geometrically permits passage for non-cohesive fines) is governed by particle-scale dynamics rather than geometry alone. Using DEM simulations in which fines interact only with bed particles (not each other) in a fixed random packing, it shows that sufficiently large cohesion and friction produce non-geometric trapping: fines fail to rebound after collision due to cohesion, low restitution, and low velocity, after which rolling or sliding is insufficient for detachment. A collisional model incorporating a trapping probability per collision and collision frequency predicts the trapping distance in the collision-dominated regime; a force-balance equilibrium condition determines whether a particle remains stationary post-contact.

Significance. If the central claim holds, the work establishes that a specific sequence of local interactions (collision, adhesion, post-contact motion) can override geometric pore accessibility in cohesive granular percolation, with direct relevance to industrial and natural processes involving fine particles. The DEM results and the proposed collisional model supply a mechanistic account of how cohesion enhances frictional trapping in non-rebounding events, which is a clear strength when the simulations are reproducible and the model is independently validated.

major comments (2)
  1. [Abstract] Abstract: the collisional model is described as predicting trapping distance from a trapping probability per collision and collision frequency, yet the origin of the trapping probability (independent derivation/measurement versus extraction from the same DEM runs) is unspecified. If the latter, the prediction reduces to a re-expression of simulation outputs and does not constitute an independent test of the claimed mechanism.
  2. [Abstract] Abstract (simulation setup): the central claim that non-geometric trapping occurs via the stated collision-adhesion sequence rests on the assumptions that fines interact only with bed particles, the bed remains static and randomly packed, and no multi-body or collective effects alter single-particle outcomes. The manuscript provides no sensitivity tests or discussion of how these assumptions might fail at realistic fine concentrations or with bed mobility.
minor comments (1)
  1. [Abstract] Abstract: parameter choices for cohesion strength, friction coefficient, and restitution coefficient, as well as any validation against experiments or checks on sensitivity to random packing realizations, are not mentioned; adding a brief statement would improve clarity without altering the central claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful and constructive review of our manuscript. We respond to each major comment below and have revised the manuscript to improve clarity on the model and to discuss the simulation assumptions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the collisional model is described as predicting trapping distance from a trapping probability per collision and collision frequency, yet the origin of the trapping probability (independent derivation/measurement versus extraction from the same DEM runs) is unspecified. If the latter, the prediction reduces to a re-expression of simulation outputs and does not constitute an independent test of the claimed mechanism.

    Authors: We thank the referee for highlighting this point. The trapping probability is determined directly from the DEM simulations by measuring the fraction of collisions that lead to permanent trapping for given cohesion, friction, and restitution values. The collisional model then uses this probability together with a collision frequency derived from the particle velocity and the geometry of the fixed bed to estimate the mean trapping distance. This provides a mechanistic scaling relation that interprets the simulation outcomes and allows extrapolation to different bed depths. We will revise the abstract and main text to state explicitly that the trapping probability originates from the DEM data and to clarify the interpretive role of the model. revision: yes

  2. Referee: [Abstract] Abstract (simulation setup): the central claim that non-geometric trapping occurs via the stated collision-adhesion sequence rests on the assumptions that fines interact only with bed particles, the bed remains static and randomly packed, and no multi-body or collective effects alter single-particle outcomes. The manuscript provides no sensitivity tests or discussion of how these assumptions might fail at realistic fine concentrations or with bed mobility.

    Authors: We agree that the idealized single-particle setup isolates the collision-adhesion mechanism. The simulations deliberately exclude fine-fine interactions and keep the bed fixed to focus on the local interaction sequence. We will add a dedicated paragraph in the discussion section that addresses the limitations of these assumptions, including possible changes at higher fine concentrations where multi-body contacts or bed-particle mobility could influence trapping statistics. While new sensitivity simulations are beyond the scope of the present revision, the added text will outline the parameter regimes in which the reported mechanism is expected to remain dominant. revision: partial

Circularity Check

1 steps flagged

Collisional model re-expresses simulation-derived trapping probability as a prediction

specific steps
  1. fitted input called prediction [Abstract and collisional model section]
    "A collisional model that incorporates a trapping probability per collision and a collision frequency predicts the trapping distance in the regime dominated by collision-induced trapping."

    The trapping probability is obtained by counting non-rebounding collisions in the DEM runs; the model then uses that same probability (plus collision frequency also measured in the runs) to 'predict' the mean trapping distance observed in those runs. The output is therefore statistically forced by the input statistics extracted from the identical dataset.

full rationale

The paper's central prediction of trapping distance relies on a collisional model whose key input (trapping probability per collision) is extracted from the same DEM simulations used to measure the output distance. This reduces the 'prediction' to a re-expression of simulation statistics rather than an independent derivation. The force-balance equilibrium condition is independent but does not carry the main claim. No self-citation load-bearing or ansatz smuggling is evident from the provided text.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The central claim depends on discrete-element contact laws for cohesion and friction, the assumption of non-interacting fines, and a static random packing whose pore geometry is taken as given.

free parameters (3)
  • cohesion strength
    Controls whether a collision results in adhesion rather than rebound
  • friction coefficient
    Determines whether post-contact rolling or sliding leads to detachment
  • restitution coefficient
    Sets the rebound velocity after collision
axioms (2)
  • domain assumption Fines interact with bed particles but not with each other
    Stated explicitly to isolate single-fine-particle behavior
  • domain assumption Bed remains static and randomly packed throughout
    Required for the pore-throat geometry and collision frequency to be time-independent

pith-pipeline@v0.9.0 · 5842 in / 1416 out tokens · 41136 ms · 2026-05-20T01:55:23.131597+00:00 · methodology

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Reference graph

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