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Modular Design in Triboelectric Sensors: A Review on the Clinical Applications for Real-Time Diagnosis

Triboelectric nanogenerators (TENGs) have garnered considerable interest as a promising technology for energy harvesting and stimulus sensing. While TENGs facilitate the generation of electricity from micro-motions, the modular design of TENG-based modular sensing systems (TMSs) also offers signific...

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Detalles Bibliográficos
Autores principales: Zhao, Zequan, Lu, Yin, Mi, Yajun, Zhu, Qiliang, Meng, Jiajing, Wang, Xueqing, Cao, Xia, Wang, Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181202/
https://www.ncbi.nlm.nih.gov/pubmed/37177395
http://dx.doi.org/10.3390/s23094194
Descripción
Sumario:Triboelectric nanogenerators (TENGs) have garnered considerable interest as a promising technology for energy harvesting and stimulus sensing. While TENGs facilitate the generation of electricity from micro-motions, the modular design of TENG-based modular sensing systems (TMSs) also offers significant potential for powering biosensors and other medical devices, thus reducing dependence on external power sources and enabling biological processes to be monitored in real time. Moreover, TENGs can be customised and personalized to address individual patient needs while ensuring biocompatibility and safety, ultimately enhancing the efficiency and security of diagnosis and treatment. In this review, we concentrate on recent advancements in the modular design of TMSs for clinical applications with an emphasis on their potential for personalised real-time diagnosis. We also examine the design and fabrication of TMSs, their sensitivity and specificity, and their capabilities of detecting biomarkers for disease diagnosis and monitoring. Furthermore, we investigate the application of TENGs to energy harvesting and real-time monitoring in wearable and implantable medical devices, underscore the promising prospects of personalised and modular TMSs in advancing real-time diagnosis for clinical applications, and offer insights into the future direction of this burgeoning field.