Harnessing small-molecule analyte detection in complex media : combining molecularly imprinted polymers, electrolytic transistors, and machine learning
Gabrielle Coelho Lelis, Wilson Tiago Fonseca, Alessandro Henrique de Lima, Anderson Kenji Okazaki, Eduardo Costa Figueiredo, Antonio Riul Jr, Gabriel Ravanhani Schleder, Paolo Samorì, Rafael Furlan de Oliveira
ARTIGO
Inglês
Agradecimentos: The authors acknowledge financial support from the São Paulo Research Foundation FAPESP (Grant number 2021/06238-5, 2019/14949-9, 2023/03501-2), CNPq (301465/2022-3), and SisNANO Programme. We thank Pedro H. S. A. Mamud for the assistance with the thermal reduction of GO. G.R.S....
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Agradecimentos: The authors acknowledge financial support from the São Paulo Research Foundation FAPESP (Grant number 2021/06238-5, 2019/14949-9, 2023/03501-2), CNPq (301465/2022-3), and SisNANO Programme. We thank Pedro H. S. A. Mamud for the assistance with the thermal reduction of GO. G.R.S. acknowledges support from CNPq - INCT (National Institute of Science and Technology on Materials Informatics, grant n. 371610/2023-0). R.F.O. acknowledges further support from INCT/INEO and the facilities at LNNano for AFM and XPS measurements
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Abstract: Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a...
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Abstract: Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a major challenge. Here, we introduce a novel approach that combines molecularly imprinted polymers (MIPs), electrolyte-gated transistors (EGTs) based on 2D materials, and machine learning (ML) to detect hippuric acid (HA) in artificial urine, being a critical marker for toluene intoxication, parasitic infections, and kidney and bowel inflammation. Reduced graphene oxide (rGO) was used as the sensory material and molecularly imprinted polymer (MIP) as supramolecular receptors. Employing supervised ML techniques based on symbolic regression and compressive sensing enabled us to comprehensively analyze the EGT transfer curves, eliminating the need for arbitrary signal selection and allowing a multivariate analysis during HA detection. The resulting device displayed simultaneously low operating voltages (<0.5 V), rapid response times (<= 10 s), operation across a wide range of HA concentrations (from 0.05 to 200 nmol L-1), and a low limit of detection (LoD) of 39 pmol L-1. Thanks to the ML multivariate analysis, we achieved a 2.5-fold increase in the device sensitivity (1.007 mu A/nmol L-1) with respect to the human data analysis (0.388 mu A/nmol L-1). Our method represents a major advance in PoC technologies, by enabling the accurate determination of small-molecule markers in complex media via the combination of ML analysis, supramolecular analyte recognition, and electrolytic transistors
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FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
2019/14949-9; 2021/06238-5; 2023/03501-2
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
301465/2022-3; 371610/2023-0
Fechado
DOI: https://doi.org/10.1021/acsami.3c16699
Texto completo: https://pubs.acs.org/doi/10.1021/acsami.3c16699
Harnessing small-molecule analyte detection in complex media : combining molecularly imprinted polymers, electrolytic transistors, and machine learning
Gabrielle Coelho Lelis, Wilson Tiago Fonseca, Alessandro Henrique de Lima, Anderson Kenji Okazaki, Eduardo Costa Figueiredo, Antonio Riul Jr, Gabriel Ravanhani Schleder, Paolo Samorì, Rafael Furlan de Oliveira
Harnessing small-molecule analyte detection in complex media : combining molecularly imprinted polymers, electrolytic transistors, and machine learning
Gabrielle Coelho Lelis, Wilson Tiago Fonseca, Alessandro Henrique de Lima, Anderson Kenji Okazaki, Eduardo Costa Figueiredo, Antonio Riul Jr, Gabriel Ravanhani Schleder, Paolo Samorì, Rafael Furlan de Oliveira
Fontes
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ACS applied materials and interfaces p. 1-11, Dec. 2023 |