Mechanosensingof Stimuli Changes with MagneticallyGated Adaptive Sensitivity

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 (<i>H</i>) gating to detect small pressure-changes under a wide range of compressive stimuli. This is realized with resistive sensing by pillared <i>H</i>-driven assemblies of soft ferromagnetic electrically conducting particles between planar electrodes under a voltage bias. By modulation of <i>H</i>, the pillars respond with mechanically adaptable sensitivity. Higher <i>H</i> 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 <i>H</i> 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.