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用于灵敏度增强触觉系统的由马兰戈尼效应驱动的较软空心微结构的确定性形成。

Marangoni-driven deterministic formation of softer, hollow microstructures for sensitivity-enhanced tactile system.

作者信息

Xiong Wennan, Zhang Fan, Qu Shiyuan, Yin Liting, Li Kan, Huang YongAn

机构信息

State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China.

Flexible Electronics Research Center, Huazhong University of Science and Technology, Wuhan, 430074, P.R. China.

出版信息

Nat Commun. 2024 Jul 3;15(1):5596. doi: 10.1038/s41467-024-49864-z.

Abstract

Microengineering the dielectric layers with three-dimensional microstructures has proven effective in enhancing the sensitivity of flexible pressure sensors. However, the widely employed geometrical designs of solid microstructures exhibit limited sensitivity over a wide range of pressures due to their inherent but undesired structural compressibility. Here, a Marangoni-driven deterministic formation approach is proposed for fabricating hollow microstructures, allowing for greater deformation while retarding structural stiffening during compression. Fluid convective deposition enables solute particles to reassemble in template microstructures, controlling the interior cavity with a void ratio exceeding 90%. The hollow micro-pyramid sensor exhibits a 10-fold sensitivity improvement across wider pressure ranges over the pressure sensor utilizing solid micro-pyramids, and an ultra-low detect limit of 0.21 Pa. With the advantages of facilitation, scalability, and large-area compatibility, such an approach for hollow microstructures can be expanded to other sensor types for superior performance and has considerable potential in robotic tactile and epidermal devices.

摘要

用三维微结构对介电层进行微工程已被证明能有效提高柔性压力传感器的灵敏度。然而,由于其固有的但不理想的结构可压缩性,固体微结构广泛采用的几何设计在很宽的压力范围内灵敏度有限。在此,提出了一种由马兰戈尼驱动的确定性形成方法来制造中空微结构,在压缩过程中允许更大的变形,同时延缓结构硬化。流体对流沉积使溶质颗粒在模板微结构中重新组装,控制内部空腔的孔隙率超过90%。与使用固体微金字塔的压力传感器相比,中空微金字塔传感器在更宽的压力范围内灵敏度提高了10倍,检测极限低至0.21 Pa。由于具有简便、可扩展和大面积兼容性的优点,这种中空微结构的方法可扩展到其他传感器类型以实现卓越性能,在机器人触觉和表皮装置中具有相当大的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3fd/11222500/f2de8c537ea0/41467_2024_49864_Fig1_HTML.jpg

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