Field effect transistor biosensors for healthcare monitoring

Abstract The burgeoning demand for healthcare monitoring has brought field effect transistor (FET) biosensors into the spotlight as a highly efficient detection technology. FET biosensors offer inherent advantages including high sensitivity, rapid response times, operational simplicity, integrability, and label‐free detection. These characteristics render them particularly well‐suited for detecting a diverse array of physiological parameters and biomarkers, thereby furnishing real‐time data crucial for personalized medicine and disease prevention. This review aims to elucidate recent advancements in FET biosensors within the realm of healthcare management encompassing several facets. Initially, this review systematically analyzes the device architecture, sensing mechanisms, and performance evaluation methods of FET biosensors to gain an in‐depth understanding of their operational principles and features. Subsequently, it focuses on the application of FET biosensors for detecting health‐related biomarkers encompassing nucleic acids, proteins, exosomes, viruses, etc. Lastly, the review presents engineered medical sensor prototypes predicated on FET biosensors, such as point‐of‐care testing devices, wearable sensors, and implantable sensors, underscoring their practical utility and potential in health management. In addition, the review addresses critical issues and prospects of using FET biosensors in healthcare monitoring, aiming to provide some references and insights for research and innovation in this field.

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Institutions

Publication Details

Journal
Interdisciplinary medicine
Published
2024-09-17
DOI
https://doi.org/10.1002/inmd.20240032
Citations
18
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
4.83

Funders

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article

Field effect transistor biosensors for healthcare monitoring

Xiaoding Lou, Jingjing Hu, Fan Xia, Jiajun Wu et al.
18 citations
Interdisciplinary medicine
Neuroscience and Neural Engineering
4.83
article

Field effect transistor biosensors for healthcare monitoring

Xiaoding Lou, Jingjing Hu, Fan Xia, Jiajun Wu, Zhicheng Zhang, Shijun Lin
article en
18 citations

Abstract

Abstract The burgeoning demand for healthcare monitoring has brought field effect transistor (FET) biosensors into the spotlight as a highly efficient detection technology. FET biosensors offer inherent advantages including high sensitivity, rapid response times, operational simplicity, integrability, and label‐free detection. These characteristics render them particularly well‐suited for detecting a diverse array of physiological parameters and biomarkers, thereby furnishing real‐time data crucial for personalized medicine and disease prevention. This review aims to elucidate recent advancements in FET biosensors within the realm of healthcare management encompassing several facets. Initially, this review systematically analyzes the device architecture, sensing mechanisms, and performance evaluation methods of FET biosensors to gain an in‐depth understanding of their operational principles and features. Subsequently, it focuses on the application of FET biosensors for detecting health‐related biomarkers encompassing nucleic acids, proteins, exosomes, viruses, etc. Lastly, the review presents engineered medical sensor prototypes predicated on FET biosensors, such as point‐of‐care testing devices, wearable sensors, and implantable sensors, underscoring their practical utility and potential in health management. In addition, the review addresses critical issues and prospects of using FET biosensors in healthcare monitoring, aiming to provide some references and insights for research and innovation in this field.

Interdisciplinary medicineVol. 2(4)
China University of Geosciences (CN)
Natural Science Foundation of Shenzhen City, National Natural Science Foundation of China, Natural Science Foundation of Hubei Province, Israel Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 4%
Neuroscience and Neural Engineering
4.83
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