Facile "Pick-up" experiments and Monte Carlo simulations for the entanglement of tunable staple-like particles
Youhan Sohn, Saeed Pezeshki, and Francois Barthelat

TL;DR
This paper introduces simple experimental and computational methods to measure and predict the entanglement of staple-like particles, revealing how geometry influences entanglement strength and enabling the design of advanced tunable materials.
Contribution
It presents a novel pick-up test, a geometrical entanglement model, and a Monte Carlo simulation to analyze and optimize entanglement in staple-like particles.
Findings
Entanglement is highly sensitive to backbone thickness.
The model accurately predicts entanglement density.
Designs can be optimized for maximum entanglement.
Abstract
Entangled matter provides intriguing perspectives in terms of deformation mechanisms, mechanical properties, assembly and disassembly. However, collective entanglement mechanisms are complex, occur over multiple length scales, and they are not fully understood to this day. In this report, we propose a simple pick-up test to measure the entanglement in staple-like particles with various leg lengths, crown-leg angles, and backbone thickness. We also present a new "throw-bounce-tangle" model based on a 3D geometrical entanglement criterion between two staples, and a Monte Carlo approach to predict the probabilities of entanglement in a bundle of staples. This relatively simple model is computationally efficient and it predicts an average density of entanglement which is consistent with the entanglement strength measured experimentally. Entanglement is very sensitive to the thickness of the…
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Taxonomy
TopicsMachine Learning in Materials Science · Electron and X-Ray Spectroscopy Techniques · Quantum Dots Synthesis And Properties
