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, 2025, 4(2): 025302. DOI: 10.1088/2752-5724/adccdf
Citation: 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, 2025, 4(2): 025302. DOI: 10.1088/2752-5724/adccdf

MOF-MoS2 nanosheets doped PEDOT: PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction

  • Organic electrochemical transistors (OECTs) are regarded as a promising platform for chemical and biological sensing due to their biocompatibility, cost-effectiveness and flexibility. However, maintaining long-term stability of OECTs while achieving high sensitivity remains a challenge for their practical applications. One of the main reasons is the relatively low electronic and ionic conductivity of the channel material. Herein, we present a p-type OECT fabricated by incorporating metal-organic framework (MOF)-MoS2 hybrid nanosheets into the PEDOT:PSS channel via solution-based processes. The strategy significantly improves the sensitivity of OECT, with the transconductance of the device increasing by ~threefold to 19.34 mS. The higher transconductance is attributed to the hybrid MOF-MoS2 dopant, which not only enhances the electronic conductivity, but also strengthens ion transport and capacitance of the PEDOT:PSS film due to the synergistic effects from high electron mobility of MoS2 and MOF porous structure with large surface area. The fabricated OECT demonstrates high selectivity and sensitivity as a glucose biosensor across a wide concentration range in saliva. Finally, we illustrate the merits of integration machine learning algorithms to construct predictive models using the extensive datasets produced by our sensors for both classification and quantification tasks. These findings highlight the great potential of OECTs incorporating MOF-MoS2 hybrid, as a promising candidate for ultra-sensitive biological detections, and broaden the applications of our OECT biosensors for non-invasive health monitoring and wearable electronics.
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