Impact of Nitro Substituents on Dopamine Sensing and Nanostructure Morphology: A Machine Learning Approach for PANI:2-and 3-Nitro-1H-Pyrrole Nanocomposite Sensors

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dc.authoridYildiz, Dilber Esra/0000-0003-2212-199X
dc.authoridKARAKUS, SELCAN/0000-0002-8368-4609
dc.contributor.authorGursu, Gamze
dc.contributor.authorYildiz, Dilber Esra
dc.contributor.authorTasaltin, Nevin
dc.contributor.authorBaytemir, Gulsen
dc.contributor.authorKarakus, Selcan
dc.contributor.authorKaraca, Bahriye
dc.contributor.authorAkarsu, Canan Hazal
dc.date.accessioned2025-03-20T09:50:46Z
dc.date.available2025-03-20T09:50:46Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractIn this study, we explore the effects of nitro substituents on the morphology and dopamine (DOP) sensing performance of polyaniline (PANI) nanocomposites (NCs). The novelty of the study is the unique integration of 2-nitro-1H-pyrrole (D9A) and 3-nitro-1H-pyrrole (D9B) into PANI to develop advanced non-enzymatic voltammetric sensors, combined with machine learning for DOP sensitivity and morphology analysis. Structural and morphological insights were obtained through comprehensive characterization techniques including H-1 NMR, 13C NMR, Fourier transform infrared spectroscopy, scanning electron microscopy, and artificial intelligence-enhanced SEM analysis. The PANI: D9B NCs sensor demonstrated superior DOP detection in the range of 0.625-5 mu M, with exceptional sensitivity (329.72 mu A mu M-1 cm-2) and an ultra-low limit of detection of 0.078 mu M. Its rapid sensing capability within 1 min indicates potential for use in biomedical diagnostics. In contrast, the PANI NCs sensor exhibited lower sensitivity, which was linked to higher Zreel values and space charge effects. To further enhance DOP prediction accuracy, we employed machine learning (ML) models-ANN, SVM, XGBoost, and Linear Regression-to analyze sensor outputs, with a focus on feature extraction and multivariate data analysis. Our combined approach provides a robust framework for optimizing nitro-substituted PANI NCs for high-performance sensing applications.
dc.description.sponsorshipTrkiye Bilimsel ve Teknolojik Arastirma Kurumu https://doi.org/10.13039/501100004410 [122N962]; Scientific and Technological Research Council of Turkey (TUBITAK)
dc.description.sponsorshipThe authors gratefully acknowledge the Scientific and Technological Research Council of Turkey (TUBITAK) due to the financial support for the Project 122N962. Conceptualization, Method and Analysis, G. G., Conceptualization, Writing- review & editing, Writing- original draft, D.E.Y. and N. T., Conceptualization, Writing-original draft, Writing-editing, Supervision, G. B., Method, Analysis, B.K., Writing- review & editing, analysis and interpretation of results, S. K., Method, Analysis, Writing- review & editing S. B., Writing- original draft, Method, analysis and interpretation of results, C.H.A. The authors declare that they have no conflict of interest.
dc.identifier.doi10.1149/1945-7111/ad9ccb
dc.identifier.issn0013-4651
dc.identifier.issn1945-7111
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85213024711
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1149/1945-7111/ad9ccb
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2304
dc.identifier.volume171
dc.identifier.wosWOS:001381146300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElectrochemical Soc Inc
dc.relation.ispartofJournal of The Electrochemical Society
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectnanocomposite
dc.subjectelectrochemical sensor
dc.subjectdopamine
dc.subject3-nitro-1H-pyrrole (3): D9B
dc.subjectpolyaniline
dc.subjectmachine learning
dc.titleImpact of Nitro Substituents on Dopamine Sensing and Nanostructure Morphology: A Machine Learning Approach for PANI:2-and 3-Nitro-1H-Pyrrole Nanocomposite Sensors
dc.typeArticle

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