Hu Xichen, Liu Xianhu, Xu Quan, Ikkala Olli, Peng Bo
Department of Applied Physics, Aalto University, P.O. Box 15100, FI 02150 Espoo, Finland.
Center of Excellence in Life-Inspired Hybrid Materials (LIBER), Aalto University, P.O. Box 16100, 00076 Aalto, Finland.
ACS Mater Lett. 2025 Feb 4;7(3):862-868. doi: 10.1021/acsmaterialslett.4c02021. eCollection 2025 Mar 3.
Inspired by biological sensors that characteristically adapt to varying stimulus ranges, efficiently detecting stimulus changes sooner than the absolute stimulus values, we propose a mechanosensing concept in which the resolution can be adapted by magnetic field () gating to detect small pressure-changes under a wide range of compressive stimuli. This is realized with resistive sensing by pillared -driven assemblies of soft ferromagnetic electrically conducting particles between planar electrodes under a voltage bias. By modulation of , the pillars respond with mechanically adaptable sensitivity. Higher enhances current resolution, while it increases scatter among repeating measurements due to increased magnetic structural jamming between colloids in their assembly. To manage the trade-off between electrical resolution and scatter, machine learning is introduced for searching optimum gatings, thus facilitating efficient pressure prediction. This approach suggests bioinspired pathways for developing adaptive stimulus-responsive mechanosensors, detecting subtle changes across varying stimuli levels with enhanced effectiveness through machine learning.
受生物传感器的启发，这些传感器能够典型地适应不同的刺激范围，比绝对刺激值更快地有效检测刺激变化，我们提出了一种机械传感概念，其中分辨率可以通过磁场（）门控进行调整，以在广泛的压缩刺激下检测微小的压力变化。这是通过在电压偏置下，由平面电极之间的软铁磁导电颗粒的柱状驱动组件进行电阻传感来实现的。通过调制，柱状物以机械适应性灵敏度做出响应。更高的增强了电流分辨率，但由于组装中胶体之间的磁性结构干扰增加，它会增加重复测量之间的散射。为了管理电分辨率和散射之间的权衡，引入了机器学习来搜索最佳门控，从而促进高效的压力预测。这种方法为开发自适应刺激响应机械传感器提出了受生物启发的途径，通过机器学习以更高的效率检测不同刺激水平下的细微变化。
