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REVIEW 1 major objections 2 minor 54 references

Filament flexibility optimizes transport in disordered obstacle arrays at intermediate values but favors semiflexible filaments in ordered arrays.

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2026-06-26 06:01 UTC pith:67QX72P5

load-bearing objection Simulations map three flexibility-dependent transport regimes for active filaments in ordered versus disordered obstacles, but the long-time diffusion assumption needs direct checks. the 1 major comments →

arxiv 2606.23921 v1 pith:67QX72P5 submitted 2026-06-22 cond-mat.soft

Flexibility Controls Active-Filament Transport in Crowded Landscapes

Qingyi Di , Mohammad Fazelzadeh , Sara Jabbari-Farouji This is my paper
classification cond-mat.soft
keywords active filamentscrowded environmentsfilament flexibilitytransport regimesBrownian dynamicsobstacle arraysdiffusion in porous mediaactive matter
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper maps how active filament flexibility interacts with obstacle arrangement to set long-time diffusion rates. Brownian dynamics simulations of tangentially driven polymers show that disordered media produce peak mobility at intermediate flexibility, while dense ordered arrays favor semiflexible filaments that follow periodic channels. Three regimes emerge: tortuosity-controlled trapping-and-hopping for very flexible filaments, confinement-assisted enhancement for moderate flexibility, and persistence-controlled directed motion for stiffer ones. Long-time behavior traces to how confinement reshapes filament conformation and reorientation. The results supply a predictive framework for deformable active agents navigating porous landscapes.

Core claim

Active filaments moving through obstacle arrays exhibit three distinct transport regimes determined by their flexibility. Highly flexible filaments undergo tortuosity-controlled diffusion via trapping-and-hopping. Moderately flexible ones benefit from confinement-assisted transport that enhances diffusion in dense media. Semiflexible filaments show persistence-controlled transport that aids diffusion in ordered arrays but hinders it in disordered ones. Long-time diffusion is governed by confinement-induced changes in filament conformation and reorientation dynamics.

What carries the argument

Three transport regimes (tortuosity-controlled, confinement-assisted, persistence-controlled) extracted from simulations of tangentially driven active polymers in ordered versus disordered obstacle arrays.

Load-bearing premise

The Brownian dynamics model with tangential driving and the chosen obstacle interactions produces long-time diffusion statistics representative of real active filaments without major finite-time or finite-size artifacts.

What would settle it

Measuring that semiflexible filaments do not exhibit faster long-time diffusion than intermediate-flexibility ones in dense ordered arrays, or that highly flexible filaments lack trapping-and-hopping dynamics, would falsify the regime distinctions.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • In disordered environments transport peaks at intermediate filament flexibility.
  • In dense ordered arrays semiflexible filaments gain mobility through directed motion along periodic channels.
  • Long-time diffusion is set by confinement-induced shifts in filament shape and turning rates.
  • The three regimes organize behavior across the full range of flexibility and obstacle density.

Where Pith is reading between the lines

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

  • The regime map could guide design of synthetic filaments or microrobots for targeted navigation through specific porous materials.
  • Similar flexibility-medium interactions may govern transport of other deformable active objects such as cells or worms in heterogeneous settings.
  • Varying driving forces or obstacle interaction rules in follow-up simulations could expose additional regimes or crossovers.
  • Direct comparison of the simulated regimes against experiments on bacterial filaments or microtubules in fabricated obstacle arrays would test the predicted optima.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 2 minor

Summary. The manuscript uses large-scale Brownian dynamics simulations of tangentially driven active polymers in ordered and disordered obstacle arrays to map long-time diffusion coefficients versus filament flexibility and obstacle density. It reports that flexibility can enhance or hinder transport depending on medium structure, with non-monotonic behavior in disordered media and enhanced mobility for semiflexible filaments in dense ordered arrays. Three regimes are identified—tortuosity-controlled (highly flexible), confinement-assisted (moderately flexible), and persistence-controlled (semiflexible)—and linked via theory to confinement-induced changes in conformation and reorientation dynamics.

Significance. If the long-time diffusive regime is confirmed across parameters, the work provides a predictive framework for deformable active agents in heterogeneous porous media, with relevance to biological systems such as motor-driven filaments. Strengths include the systematic exploration of flexibility and ordering effects and the combination of simulation with theoretical interpretation of microscopic mechanisms.

major comments (1)
  1. [Results / Simulation Methods] The identification of three distinct transport regimes and the reported non-monotonic dependence of diffusion on flexibility rest on the assumption that mean-squared displacements have entered the asymptotic linear regime for every combination of bending rigidity, obstacle density, and ordering. The manuscript should provide explicit validation (e.g., time-dependent effective diffusion coefficients or MSD plots spanning multiple decades) in the Results or Methods section to rule out finite-time artifacts from trapping or channeling.
minor comments (2)
  1. [Abstract] The abstract states that 'long-time diffusion' is mapped but does not specify the observation times, system sizes, or convergence criteria used; adding a brief statement on these would improve clarity.
  2. [Introduction / Model] Notation for the bending rigidity and driving force should be defined consistently when first introduced in the main text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. The single major comment raises a valid methodological point about confirming the long-time diffusive regime, which we address below by agreeing to add explicit validation.

read point-by-point responses

  1. Referee: [Results / Simulation Methods] The identification of three distinct transport regimes and the reported non-monotonic dependence of diffusion on flexibility rest on the assumption that mean-squared displacements have entered the asymptotic linear regime for every combination of bending rigidity, obstacle density, and ordering. The manuscript should provide explicit validation (e.g., time-dependent effective diffusion coefficients or MSD plots spanning multiple decades) in the Results or Methods section to rule out finite-time artifacts from trapping or channeling.

    Authors: We agree that explicit confirmation of the asymptotic regime strengthens the claims. Although our Brownian dynamics runs were extended until the effective diffusion coefficient D_eff(t) = MSD(t)/(4t) plateaued for all reported parameter sets (with total simulation times exceeding 10^4 persistence times in the densest cases), we did not include these diagnostic plots. In the revised manuscript we will add, in a new subsection of the Methods and representative panels in the Results, time-dependent D_eff(t) curves and log-log MSD plots spanning at least three decades for representative combinations of bending rigidity, obstacle density, and ordering. These will demonstrate that the quoted long-time diffusivities are free of transient trapping or channeling artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: results are direct simulation outputs

full rationale

The manuscript reports long-time diffusion coefficients and transport regimes obtained from explicit Brownian dynamics simulations of tangentially driven polymers interacting with obstacle arrays. No load-bearing step reduces a reported quantity (e.g., D or regime boundaries) to an input parameter by construction, nor does any equation or self-citation chain equate a prediction to its own fitted value. The three regimes are classified post hoc from measured MSD, conformation, and reorientation statistics; the mapping itself is not tautological. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is necessarily incomplete. The central claim rests on the validity of the Brownian-dynamics model for tangentially driven polymers and on the assumption that long-time diffusion is governed by confinement-induced conformation and reorientation changes.

axioms (1)
  • domain assumption Brownian dynamics simulations with tangential driving accurately reproduce the long-time diffusive behavior of active filaments in obstacle arrays
    Invoked by the choice of simulation method described in the abstract

pith-pipeline@v0.9.1-grok · 5783 in / 1345 out tokens · 34049 ms · 2026-06-26T06:01:26.945514+00:00 · methodology

0 comments
read the original abstract

Active filaments, ranging from motor-driven biopolymers to elongated bacteria and worms, are paradigmatic examples of deformable active matter. How filament flexibility interacts with environmental heterogeneity to control their transport in crowded environments, however, remains poorly understood. Here, we perform large-scale Brownian dynamics simulations of tangentially driven active polymers moving through ordered and disordered obstacle arrays to map the long-time diffusion as a function of obstacle density and filament flexibility. We find that flexibility can either enhance or hinder transport depending on the structure of the medium. In disordered environments, transport is optimized at intermediate filament flexibility, whereas both highly flexible and semiflexible filaments diffuse more slowly. In contrast, dense ordered arrays enhance the mobility of semiflexible filaments by promoting directed motion along periodic channels. We identify three distinct transport regimes: (i) tortuosity-controlled diffusion of highly flexible filaments, characterized by trapping-and-hopping dynamics; (ii) confinement-assisted transport of moderately flexible filaments, which enhances diffusion in dense media; and (iii) persistence-controlled transport of semiflexible filaments, which facilitates diffusion in dense ordered media, but suppresses it in disordered media. Combining theory and simulations, we show that long-time diffusion is governed by confinement-induced changes in filament conformation and reorientation dynamics. Our work uncovers general transport principles for deformable active agents in heterogeneous environments and provides a predictive framework for active-filament navigation in complex porous landscapes.

Figures

Figures reproduced from arXiv: 2606.23921 by Mohammad Fazelzadeh, Qingyi Di, Sara Jabbari-Farouji.

Figure 1
Figure 1. Figure 1: FIG. 1. Schematic of a tangentially driven active polymer in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Pore-space length scales for ordered and disordered [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: shows Tdiff as a function of obstacle packing fraction ϕ for both random and square-lattice media. For square lattices, we distinguish between the tortuosity av￾eraged over all directions and the tortuosity measured along the primitive lattice axes. These values are similar at low packing fractions, but for ϕ >∼ 0.4 the axial tor￾tuosity becomes noticeably smaller than the directional average, reflecting t… view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Colour map of the normalised long-time diffusion [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Normalised long-time diffusion coefficient [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Representative simulation snapshots of 2D phantom [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Probability density function of end-to-end distance [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Normalised time autocorrelation function of the [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Parameters of the compressed-/stretched [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Normalised mean relaxation time of the polymer [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Normalised long-time diffusion coefficient [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Normalised long-time diffusion coefficient of poly [PITH_FULL_IMAGE:figures/full_fig_p011_13.png] view at source ↗
Figure 16
Figure 16. Figure 16: In the semiflexible regime, ℓ 0 p/L ∼ 1, transport is governed by the interplay between polymer persistent motion over length scales comparable to the polymer contour length and pore geometry. Rather than being strongly compactified, filaments retain extended confor￾mations and navigate the porous medium through per￾sistent motion along interconnected curvilinear channels. Consequently, the architecture o… view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15. Mean squared displacement of active polymer cen [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16. Properties of active polymers in disordered arrays [PITH_FULL_IMAGE:figures/full_fig_p014_16.png] view at source ↗

discussion (0)

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