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通过优化分子基压电传感器的微观结构来提高其性能。

Enhancing the performance of molecule-based piezoelectric sensors by optimizing their microstructures.

作者信息

Tang Zheng-Xiao, Wang Bin, Li Zhi-Rui, Huang Zhuo, Zhao Hai-Xia, Long La-Sheng, Zheng Lan-Sun

机构信息

State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University Xiamen Fujian 361005 China

出版信息

Chem Sci. 2024 Oct 7;15(43):18060-6. doi: 10.1039/d4sc05442c.

Abstract

By combining the rigidity of inorganic components with the flexibility of organic components, molecule-based ferroelectrics emerge as promising candidates for flexible, self-powered piezoelectric sensors. While it is well known that the performance of piezoelectric sensor devices depends not only on the materials' piezoelectric properties but also on the device architecture, research into enhancing molecule-based piezoelectric sensor performance through microstructure optimization has never been investigated. Here, we report the synthesis of a molecule-based ferroelectric, [(2-bromoethyl) trimethylammonium][GaBr] ([(CH)NCHCHBr][GaBr]) (1), which exhibits a piezoelectric coefficient ( ) of up to 331 pC N. Our investigation reveals that the power density of a composite piezoelectric sensor device made from 1@S-PDMS(800#) (with microstructures) is twelve times that of 1-Flat-PDMS (without microstructures), due to a synergistic combination of piezoelectric and triboelectric effects. Interestingly, this flexible piezoelectric sensor can effectively detect human physiological signals, such as finger bending, breathing, and speech recognition, without the need for an external power supply.

摘要

通过将无机组分的刚性与有机组分的柔韧性相结合,基于分子的铁电体成为柔性自供电压电传感器的有前途的候选材料。虽然众所周知压电传感器器件的性能不仅取决于材料的压电性能,还取决于器件架构,但通过微观结构优化来提高基于分子的压电传感器性能的研究从未被探讨过。在此,我们报道了一种基于分子的铁电体[(2-溴乙基)三甲基铵][GaBr]([(CH)NCHCHBr][GaBr])(1)的合成,其压电系数( )高达331 pC N。我们的研究表明,由1@S-PDMS(800#)(具有微观结构)制成的复合压电传感器器件的功率密度是1-Flat-PDMS(无微观结构)的12倍,这归因于压电效应和摩擦电效应的协同组合。有趣的是,这种柔性压电传感器无需外部电源就能有效地检测人体生理信号,如手指弯曲、呼吸和语音识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d947/11539518/abbb017476ab/d4sc05442c-f1.jpg

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