ArticleEntropy (Basel, Switzerland)2023
Narrow Pore Crossing of Active Particles under Stochastic Resetting.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- On the Stochastic Motion Induced by Magnetic Fields in Random Environments.Entropy (Basel, Switzerland) · 2025Article
- Ratcheting by Stochastic Resetting With Fat-Tailed Time Distributions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2024Article
- Optimal escapes in active matter.The European physical journal. E, Soft matter · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose a two-dimensional model of biochemical activation process, whereby self-propelling particles of finite correlation times are injected at the center of a circular cavity with constant rate equal to the inverse of their lifetime; activation is triggered when one such particle hits a receptor on the cavity boundary, modeled as a narrow pore. We numerically investigated this process by computing the particle mean-first exit times through the cavity pore as a function of the correlation and injection time constants. Due to the breach of the circular symmetry associated with the positioning of the receptor, the exit times may depend on the orientation of the self-propelling velocity at injection. Stochastic resetting appears to favor activation for large particle correlation times, where most of the underlying diffusion process occurs at the cavity boundary.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.