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Record W4415121652 · doi:10.1016/j.physa.2025.131044

Review of the percolation threshold for spherocylinder-based systems in a continuum model

2025· article· en· W4415121652 on OpenAlexaff
Meysam Khodaei, Mohsen Jafaraghaei, Ashkan Ajrian, Sina Giahkar

Bibliographic record

VenuePhysica A Statistical Mechanics and its Applications · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterpolation (computer graphics)Monte Carlo methodPercolation (cognitive psychology)Range (aeronautics)Focus (optics)Percolation thresholdStrengths and weaknesses

Abstract

fetched live from OpenAlex

This study presents a comprehensive review and comparative analysis of various methods for determining the percolation threshold in systems of spherocylinders—a critical parameter in the design of advanced composite materials. We evaluated a range of approaches, including analytical models based on excluded volume theory (soft- and hard-core), computational Monte Carlo simulations, and established experimental techniques. A central focus was reconciling the discrepancies between theoretical models, which often assume infinite aspect ratios, and experimental results from fillers with finite aspect ratios. Our analysis reveals that while traditional analytical bounds and hard-core models exhibit limited predictive accuracy, computational soft-core simulations for finite-sized fillers provide robust predictions that align well with experimental data. Moreover, empirical approximations fitted to numerical results demonstrate strong agreement across all aspect ratio regimes. The primary contribution of this work is a novel interpolation formula that unifies the distinct asymptotic behaviours observed at very low and very high aspect ratios. This formula shows excellent agreement with extensive simulation data and serves as a highly accurate, unified predictive tool. By clarifying the strengths and weaknesses of existing methods, this investigation provides a reliable framework for accurately predicting the percolation threshold in spherocylinder-based systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.268
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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