Huang Dake, Qi Jian, Gao Shuo, Yin Lukui, Qi Houjun, Zheng Shuxian
Tianjin High-end Intelligent CNC Machine Tool Engineering Research Center, Tianjin Key Laboratory of High Speed Cutting and Precision Processing, Tianjin University of Technology and Education, Tianjin 300222, China.
Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, School of Mechanical Engineering, Tianjin University, Tianjin 300354, China.
ACS Appl Mater Interfaces. 2025 Aug 20;17(33):47273-47289. doi: 10.1021/acsami.5c07889. Epub 2025 Aug 5.
Flexible hydrogel sensors have attracted significant attention in wearable applications due to their excellent flexibility and biocompatibility. However, challenges such as insufficient long-term stability, limited sensitivity range, and reliance on traditional molds for microstructure design urgently need to be addressed. This study constructs a dual-ion conductive hydrogel sensor with multilevel conic-pyramid microstructures via Digital Light Processing (DLP) 3D printing, breaking through existing technical bottlenecks. Using an acrylamide (AM)-poly(ethylene glycol) diacrylate (PEGDA) double-network matrix loaded with a Mg/Na ion system, combined with 30 wt % glycerol modification, the water retention rate of the hydrogel is increased to over 90%, solving the ion concentration fluctuation problem in traditional hydrogels caused by water loss. Simulations comparing six single microstructures show that the conic-pyramid structure, relying on a stepwise compression deformation mechanism (three-level structures sequentially contacting the electrode layer), achieves a sensitivity of 0.544 kPa in the 0-0.8 kPa pressure range, representing a 78% improvement over traditional pyramid structures. It features a response time of 30 ms, a recovery time of 40 ms, and a signal attenuation <4% after 10,000 cycle tests, with stability improved by 56% compared to single Na systems. The sensor enables real-time monitoring of finger joint bending (55% resistance variation at 90° bending) and wrist movements (64% resistance variation) through a 9 × 9 orthogonal electrode grid and achieves "handwriting fingerprint" recognition for different writers (signal differences >2.5%) using combined pressure-trajectory features. The high-resolution characteristics (7.8 μm precision, size error <9.13%) of DLP printing breaks through the limitations of traditional molds for complex structures, providing a new paradigm for rapid microstructure prototyping. Compared with existing flexible sensors, this study demonstrates significant improvements in the synergistic performance of sensitivity and stability. The conic-pyramid structure design principle and dual-ion regulation strategy proposed herein offer a universal solution to address sensor performance degradation in complex environments. The "handwriting fingerprint" technology shows broad application potential in identity authentication, medical monitoring, and intelligent anticounterfeiting fields.
柔性水凝胶传感器因其出色的柔韧性和生物相容性，在可穿戴应用中备受关注。然而，诸如长期稳定性不足、灵敏度范围有限以及在微观结构设计上依赖传统模具等挑战亟待解决。本研究通过数字光处理（DLP）3D打印构建了一种具有多级圆锥金字塔微观结构的双离子导电水凝胶传感器，突破了现有技术瓶颈。使用负载Mg/Na离子体系的丙烯酰胺（AM）-聚乙二醇二丙烯酸酯（PEGDA）双网络基质，并结合30 wt%甘油改性，水凝胶的保水率提高到90%以上，解决了传统水凝胶因水分流失导致的离子浓度波动问题。对六种单一微观结构的模拟表明，圆锥金字塔结构依靠逐步压缩变形机制（三级结构依次与电极层接触），在0-0.8 kPa压力范围内实现了0.544 kPa的灵敏度，比传统金字塔结构提高了78%。其响应时间为30 ms，恢复时间为40 ms，经过10000次循环测试后信号衰减<4%，与单一Na体系相比稳定性提高了56%。该传感器通过9×9正交电极网格能够实时监测手指关节弯曲（90°弯曲时电阻变化55%）和手腕运动（电阻变化64%），并利用压力-轨迹组合特征实现对不同书写者的“手写指纹”识别（信号差异>2.5%）。DLP打印的高分辨率特性（精度7.8μm，尺寸误差<9.13%）突破了传统模具对复杂结构的限制，为快速微观结构原型制作提供了新范例。与现有柔性传感器相比，本研究展示了在灵敏度和稳定性协同性能方面的显著提升。本文提出的圆锥金字塔结构设计原理和双离子调控策略为解决复杂环境下传感器性能退化提供了通用解决方案。“手写指纹”技术在身份认证、医疗监测和智能防伪领域显示出广阔的应用潜力。
