材料科学
晶体管
气体分析呼吸
超短脉冲
检出限
纳米技术
丙酮
场效应晶体管
光电子学
半导体
响应时间
CMOS芯片
灵敏度(控制系统)
计算机科学
氧化物
吸附
挥发性有机化合物
毒品检测
极限(数学)
制作
作者
Jiaxin Liu,Zhongyu Wang,Chengxu Lin,Zhizhi Wang,Linlin Hou,Chunhua He,Qingyuan Wang,Lei Ma,Guanglan Liao,Zirong Tang,Tielin Shi,Hu Long
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2026-02-05
卷期号:11 (2): 1557-1569
被引量:1
标识
DOI:10.1021/acssensors.5c03910
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI