随机驱动分形凝胶中的训练与记忆
Training and memory in a randomly driven fractal gel
- Johns Hopkins University（约翰斯·霍普金斯大学）
- University of Ottawa（渥太华大学）
- Brookhaven National Laboratory（布鲁克海文国家实验室）
- McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过X射线光子相关光谱法，比较了确定性循环剪切与随机剪切对分形纳米颗粒凝胶的训练效果，发现两者均能诱导微结构可逆性，且随机驱动的记忆可被标准协议读出并量化。
AI中文摘要：
多种无序材料，包括堵塞的颗粒系统和皱折的纸张，可以被训练以展示对循环应变幅度的记忆。然而，直到最近，关于随机驱动是否能施加类似的训练的问题才出现。在这里，我们使用X射线光子相关光谱法研究通过确定性循环剪切或由应变幅度$-\u03b3_t$和$+\u03b3_t$限定的随机剪切训练的分形纳米颗粒凝胶。两种类型的训练都导致微结构可逆性，随机协议的训练更慢且更不规则。在两种情况下，剪切诱导凝胶内部应力的重新分布，并伴随相应的不可逆应变位移，其幅度在训练过程中减小。最后，我们展示了随机驱动的记忆可以使用标准协议读出并量化。
英文摘要：
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.
