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 pra...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Materials Futures |
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| Online Access: | https://doi.org/10.1088/2752-5724/adccdf |
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| author | Yali Sun Yun Li Yang Zhou Ting Cai Yuxuan Chen Chao Zou Han Song Shenghuang Lin Shenghua Liu |
| author_facet | Yali Sun Yun Li Yang Zhou Ting Cai Yuxuan Chen Chao Zou Han Song Shenghuang Lin Shenghua Liu |
| author_sort | Yali Sun |
| collection | DOAJ |
| description | 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)-MoS _2 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-MoS _2 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 MoS _2 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-MoS _2 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. |
| format | Article |
| id | doaj-art-54e0971d8b084126bc55d3c19a4f10e7 |
| institution | OA Journals |
| issn | 2752-5724 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Futures |
| spelling | doaj-art-54e0971d8b084126bc55d3c19a4f10e72025-08-20T02:11:29ZengIOP PublishingMaterials Futures2752-57242025-01-014202530210.1088/2752-5724/adccdfMOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration predictionYali Sun0https://orcid.org/0000-0002-5348-3160Yun Li1Yang Zhou2Ting Cai3Yuxuan Chen4Chao Zou5Han Song6Shenghuang Lin7https://orcid.org/0000-0001-9552-4680Shenghua Liu8School of Materials, Shenzhen Campus of Sun Yat-Sen UniversityShenzhen , Guangdong 518107, People’s Republic of ChinaSongshan Lake Materials Laboratory , Dongguan 523808, People’s Republic of China; Institute of Physics, Chinese Academy of Science , Beijing 100190, People’s Republic of ChinaSongshan Lake Materials Laboratory , Dongguan 523808, People’s Republic of ChinaSchool of Materials, Shenzhen Campus of Sun Yat-Sen UniversityShenzhen , Guangdong 518107, People’s Republic of ChinaSchool of Materials, Shenzhen Campus of Sun Yat-Sen UniversityShenzhen , Guangdong 518107, People’s Republic of ChinaSongshan Lake Materials Laboratory , Dongguan 523808, People’s Republic of ChinaSongshan Lake Materials Laboratory , Dongguan 523808, People’s Republic of ChinaSongshan Lake Materials Laboratory , Dongguan 523808, People’s Republic of ChinaSchool of Materials, Shenzhen Campus of Sun Yat-Sen UniversityShenzhen , Guangdong 518107, People’s Republic of ChinaOrganic 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)-MoS _2 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-MoS _2 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 MoS _2 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-MoS _2 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.https://doi.org/10.1088/2752-5724/adccdforganic electrochemical transistorsMOF-MoS2 nanosheetsglucose sensingmachine learningtransconductance |
| spellingShingle | 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 Materials Futures organic electrochemical transistors MOF-MoS2 nanosheets glucose sensing machine learning transconductance |
| title | MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction |
| title_full | MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction |
| title_fullStr | MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction |
| title_full_unstemmed | MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction |
| title_short | MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction |
| title_sort | mof mos2 nanosheets doped pedot pss for organic electrochemical transistors in enhanced glucose sensing and machine learning based concentration prediction |
| topic | organic electrochemical transistors MOF-MoS2 nanosheets glucose sensing machine learning transconductance |
| url | https://doi.org/10.1088/2752-5724/adccdf |
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