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Lelis GC, Fonseca WT, de Lima AH, Okazaki AK, Figueiredo EC, Riul A, Schleder GR, Samorì P, de Oliveira RF. Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 38134415 DOI: 10.1021/acsami.3c16699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
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 μA/nmol L-1) with respect to the human data analysis (0.388 μA/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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Affiliation(s)
- Gabrielle Coelho Lelis
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
| | - Wilson Tiago Fonseca
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
| | - Alessandro Henrique de Lima
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
| | - Anderson Kenji Okazaki
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
| | - Eduardo Costa Figueiredo
- Faculty of Pharmaceutical Sciences, Federal University of Alfenas, Alfenas, MG 37130-001, Brazil
| | - Antonio Riul
- Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin, Campinas, SP 13083-859, Brazil
| | - Gabriel Ravanhani Schleder
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Paolo Samorì
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg 67000, France
| | - Rafael Furlan de Oliveira
- Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, SP 13083-100, Brazil
- Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin, Campinas, SP 13083-859, Brazil
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