Training and memory in a randomly driven fractal gel

Abstract

A variety of disordered materials, including jammed particulate systems and crumpled paper, can be trained to exhibit memory of a cyclic strain amplitude. Only recently, however, have questions emerged around the whether random driving can impart similar training. Here, we employ x-ray photon correlation spectroscopy to study a fractal nanoparticle gel trained via either deterministic cyclic shear or random shear bounded by strain amplitudes −γ_t and +γ_t. Both types of training lead to microstructural reversibility, with a slower and more irregular training for the random protocol. In both cases, the shear induces redistribution of internal stress in the gel with corresponding irreversible strain displacements whose magnitudes decrease during training. Finally, we show that memory of the random driving can be read out and quantified using a standard protocol.

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Cite

BibTeX RIS

BibTeX

@misc{lindeman2026training,
  title = {{Training and memory in a randomly driven fractal gel}},
  author = {Chloe W. Lindeman and Joshua D. Clugston and Justin C. Goodrich and Mark Sutton and James L. Harden and Robert L. Leheny},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.40010},
  eprint = {2609.40010},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.40010},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Training and memory in a randomly driven fractal gel
AU  - Chloe W. Lindeman
AU  - Joshua D. Clugston
AU  - Justin C. Goodrich
AU  - Mark Sutton
AU  - James L. Harden
AU  - Robert L. Leheny
PY  - 2026
DA  - 2026/09/30
PB  - arXiv
DO  - 10.48550/arxiv.2609.40010
UR  - https://arxiv.org/abs/2609.40010
AB  - A variety of disordered materials, including jammed particulate systems and crumpled paper, can be trained to exhibit memory of a cyclic strain amplitude. Only recently, however, have questions emerged around the whether random driving can impart similar training. Here, we employ x-ray photon correlation spectroscopy to study a fractal nanoparticle gel trained via either deterministic cyclic shear or random shear bounded by strain amplitudes −γ_t and +γ_t. Both types of training lead to microstructural reversibility, with a slower and more irregular training for the random protocol. In both cases, the shear induces redistribution of internal stress in the gel with corresponding irreversible strain displacements whose magnitudes decrease during training. Finally, we show that memory of the random driving can be read out and quantified using a standard protocol.
ER  -

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