image: MOF-MoS2 hybrid incorporated PEDOT:PSS as channel layer for OECT biosensors.
Credit: Yali Sun and Shenghua Liu from the University of Sun Yat-sen.
Organic electrochemical transistor (OECT), a powerful tool for chemical and biological sensing, can operate directly in aqueous environment at low voltages, which makes it ideal for wearable and biocompatible applications. However, the most used material in OECTs, called PEDOT:PSS, has long faced a performance barrier due to its limited ability to conduct electricity and ions. This shortcoming has prevented it from achieving the sensitivity required for detecting small changes in glucose levels, especially in real-time applications.
To overcome this, the researchers created a new blend by adding hybrid nanosheets made of metal-organic frameworks (MOFs) and MoS₂ into the PEDOT:PSS channel layer. This combination brings together the high conductivity of MoS₂ and the porous, high-surface-area structure of MOFs, resulting in a more efficient movement of both electrons and ions. As a result, the performance of the device improved significantly, with transconductance increasing nearly threefold—from 6.5 to 19.34 millisiemens. This directly translated into more accurate and sensitive glucose detection, even across a wide range of concentrations from as low as 30 nanomolar up to 1 millimolar. Moreover, by pairing the upgraded sensor with machine learning, the team was able to train the device to accurately predict glucose concentrations from saliva samples. This addresses a common challenge in sensor variability and reduces the need for repeated manual calibration—bringing us a step closer to truly user-friendly, non-invasive glucose testing.
Looking ahead, the researchers see great potential in using similar hybrid nanomaterials to build smarter and flexible biosensors based on OECT. Such sensors could revolutionize not only diabetes care but also other areas of personal health monitoring.
The research has been recently published in the online edition of Materials Futures, a prominent international journal in the field of interdisciplinary materials science research.
Reference: Yali Sun, Yun Li, Yang Zhou, Ting Cai, Yuxuan Chen, Chao Zou, Han Song, Shenghuang Lin, Shenghua Liu. MOF-MoS2 Nanosheets Doped PEDOT:PSS for Organic Electrochemical Transistors in Enhanced Glucose Sensing and Machine Learning-based Concentration Prediction[J]. Materials Futures. DOI: 10.1088/2752-5724/adccdf
Journal
Materials Futures
Article Title
MOF-MoS₂ Nanosheets Doped PEDOT:PSS for Organic Electrochemical Transistors in Enhanced Glucose Sensing and Machine Learning-based Concentration Prediction