Training allostery-inspired mechanical response in disordered elastic networks

Disordered elastic networks are a model material system in which it is possible to achieve tunable and trainable functions. This work investigates the modification of local mechanical properties in disordered networks inspired by allosteric interactions in proteins: applying strain locally to a set of source nodes triggers a strain response at a distant set of target nodes. This is demonstrated first by using directed aging to modify the existing mechanical coupling between pairs of distant source and target nodes, and later as a means for inducing coupling between formerly isolated source-target pairs. The experimental results are compared with those predicted by simulations.

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Publication Details

Journal
Soft Matter
Published
2025-01-01
DOI
https://doi.org/10.1039/d4sm01340a
Citations
1
Primary Topic
Advanced Materials and Mechanics
Type
article
Field-Weighted Citation Impact
0.71

Funders

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article

Training allostery-inspired mechanical response in disordered elastic networks

Savannah Gowen
1 citations
Soft Matter
Advanced Materials and Mechanics
0.71
article

Training allostery-inspired mechanical response in disordered elastic networks

Savannah Gowen
article en
1 citations

Abstract

Disordered elastic networks are a model material system in which it is possible to achieve tunable and trainable functions. This work investigates the modification of local mechanical properties in disordered networks inspired by allosteric interactions in proteins: applying strain locally to a set of source nodes triggers a strain response at a distant set of target nodes. This is demonstrated first by using directed aging to modify the existing mechanical coupling between pairs of distant source and target nodes, and later as a means for inducing coupling between formerly isolated source-target pairs. The experimental results are compared with those predicted by simulations.

Soft MatterVol. 21(18)
University of Chicago (US)
National Science Foundation, Materials Research Science and Engineering Center, Harvard University, Division of Materials Research
Openalex Percentile: Top 34%
Advanced Materials and Mechanics
0.71
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