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Updated: Sep 16, 2025

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

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用微针刺穿软基板阵列:实验,模拟和机器学习预测

Nan Hu¹, Junjie Liu¹, Qifang Zhang¹

¹School of Mechanics and Aerospace Engineering, Sichuan Province Key Laboratory of Advanced Structural Materials Mechanical Behavior and Service Safety, Southwest Jiaotong University, Chengdu, Sichuan, 611756, China.

Journal of the mechanical behavior of biomedical materials

|July 6, 2025

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这项研究揭示了微针的几何如何影响插入力和深度. 经过实验和模拟的优化设计,提高了医疗应用中的微针阵列性能.

科学领域:

生物医学工程生物医学工程
材料科学材料科学材料科学
纳米技术纳米技术

背景情况:

微针阵列是用于药物输送,组织粘附和神经信号记录的先进医疗工具.
优化微针阵列设计需要了解几何特征对软组织穿孔性能的影响.

研究的目的:

为了研究个别微针几何形状 (直径,尖角) 对穿孔力学的影响.
分析数组级别的几何因素 (高度差异,间距,尖端角度,直径) 如何影响平面,箭头形和波形微针数组的穿孔力和效率.
使用人工神经网络开发微针阵列穿孔力的预测模型.

主要方法:

对单个微针和三种类型的微针阵列 (平面,箭头形,波形) 进行了穿孔实验.
用有限元模拟来补充实验数据.
一个人工神经网络 (ANN) 使用 152 个模拟结果进行训练,以根据几何参数预测穿孔力.

主要成果:

减少微针的直径和尖端角度降低了关键穿孔力和深度.
微针之间的间距增加提高了所有阵列类型的穿孔效率和峰值力.
高度变化在波形阵列中显著影响了峰值穿孔力;较大的尖端角度降低了效率,但在阵列和波形设计中可以减轻这一问题.
较小的直径降低了峰值穿孔力,并提高了所有测试阵列的穿孔效率.

关键词:

机器学习机器学习微针阵列是一个微针阵列.最佳设计的最佳设计.软材料的穿孔软材料的穿孔.穿孔模拟的穿孔模拟

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结论:

微针阵列几何学,包括间距和单个针尺寸,极大地影响穿孔性能.
开发的ANN模型准确地预测了微针阵列穿孔力,为设计优化提供了宝贵的工具.
研究结果为设计更有效的微针系统提供了关键的见解,用于各种生物医学应用.