宽量程高线性度柔性压力传感器的逆向设计

       线性输出可有效降低传感器系统的复杂性与信息处理难度，进而降低生产与使用成本。然而线性输出在新兴的柔性传感领域仍存在技术空白。当前柔性传感器研究的输出特性主要由材料与微结构控制，现行研究往往凭主观经验对上述因素进行调节或匹配以实现线性输出，以上正向设计思路不仅耗时耗力，且结果难以预测，成功实现案例极少。
       为突破正向设计思路的局限性，本工作以基于双电层模型的离子-电子传感机制为例，结合宽量程内高线性度的最终目标，有指向性地进行逆向预测，通过基于代理模型的两步机器学习策略，有效降低了运算复杂度，只需一周的迭代运算，即可获取十二组线性拟合确定系数R² 在0.999 以上的高价值微结构模型，效率显著优于动辄数月的正向设计方法。随后借助高精度3D 打印技术与微结构翻模技术，将设计模型阵列构建到传感器传感层，并总结出从制备到质检的关键工艺流程。为减小模型验证的额外干扰，本工作对测试条件与环境因素进行探讨与排除，对离子-电子基材的物化特性提出新的要求。同时发展出一种基于电化学蚀刻的微结构-电极界面面积的高保真快速测量方法，有效增强了运算预测和实验反馈的联系。本工作所提出的微结构模型具有材料普适性，在不同的基材下均可实现0.995 以上的高线性，特别在聚乙烯醇基材上可实现1.6 MPa 超宽范围的高线性输出（R² = 0.9996）。
      该工作为突破传统设计的低效问题，提出一种基于机器学习的逆向设计思路，结合高精 度3D 打印与微结构翻模技术，实现宽量程高线性度柔性压力传感器的高效设计，具有开发周期短、普适性强的优点，为高性能柔性压力传感器的研制提供了新思路。

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