Edge-Enriched Metal–Organic Framework Integrated with a Gate-Sensitive Field-Effect Transistor for Highly Sensitive and Ultrafast Detection of Breath Acetone

Non-invasive monitoring of exhaled acetone-a recognized biomarker of diabetes-has offered a promising and convenient diagnostic approach for diabetes. However, conventional optical and metal oxide semiconductor sensors suffer from bulky instrumentation, high power consumption, and poor portability. Metal-organic framework (MOF)-based sensors can overcome these drawbacks but still require improvements in response time and stability. Here, we develop a gate-sensitive field-effect transistor (GS-FET) gas sensor functionalized with a sensitive MOF for ultrafast and noninvasive acetone detection. The MOF serves as a chemical-sensitive gate, modulating the polysilicon channel current, while a solvent-modification strategy promotes the density of edge-unsaturated sites with enhanced adsorption activity, as confirmed by density functional theory. Benefiting from these optimizations, the GS-FET sensor achieves a sub-500 ppb detection limit toward acetone and enables real-time breath analysis when integrated into a portable mobile-linked device. To further improve the practicality and convenience of the gas sensor, we have proposed a data analysis algorithm to predict the concentration of acetone based on the initial response of the sensors within 5 s with high data reliability. This work demonstrates a practical pathway for leveraging MOF-based architectures in ultrafast, noninvasive diabetes diagnosis and provides new insights into the development of high-performance gas sensors.

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Publication Details

Journal
ACS Sensors
Published
2026-02-05
DOI
https://doi.org/10.1021/acssensors.5c03910
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
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article

Edge-Enriched Metal–Organic Framework Integrated with a Gate-Sensitive Field-Effect Transistor for Highly Sensitive and Ultrafast Detection of Breath Acetone

Guanglan Liao, Hu Long, Zirong Tang, Chunhua He et al.
ACS Sensors
Gas Sensing Nanomaterials and Sensors
article

Edge-Enriched Metal–Organic Framework Integrated with a Gate-Sensitive Field-Effect Transistor for Highly Sensitive and Ultrafast Detection of Breath Acetone

Guanglan Liao, Hu Long, Zirong Tang, Chunhua He, Zhongyu Wang, Haomin Wang, Zhizhi Wang, Chengxu Lin, Linlin Hou, Jiaxin Liu, Lei Ma, Tielin Shi
article en

Abstract

Non-invasive monitoring of exhaled acetone-a recognized biomarker of diabetes-has offered a promising and convenient diagnostic approach for diabetes. However, conventional optical and metal oxide semiconductor sensors suffer from bulky instrumentation, high power consumption, and poor portability. Metal-organic framework (MOF)-based sensors can overcome these drawbacks but still require improvements in response time and stability. Here, we develop a gate-sensitive field-effect transistor (GS-FET) gas sensor functionalized with a sensitive MOF for ultrafast and noninvasive acetone detection. The MOF serves as a chemical-sensitive gate, modulating the polysilicon channel current, while a solvent-modification strategy promotes the density of edge-unsaturated sites with enhanced adsorption activity, as confirmed by density functional theory. Benefiting from these optimizations, the GS-FET sensor achieves a sub-500 ppb detection limit toward acetone and enables real-time breath analysis when integrated into a portable mobile-linked device. To further improve the practicality and convenience of the gas sensor, we have proposed a data analysis algorithm to predict the concentration of acetone based on the initial response of the sensors within 5 s with high data reliability. This work demonstrates a practical pathway for leveraging MOF-based architectures in ultrafast, noninvasive diabetes diagnosis and provides new insights into the development of high-performance gas sensors.

ACS SensorsVol. 11(2)
Photronics (United States) (US), National Engineering Research Center of Electromagnetic Radiation Control Materials (CN), Intelligent Health (United Kingdom) (GB), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 66%
Gas Sensing Nanomaterials and Sensors
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