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AI-integrated chemosensing platform for sequential nanomolar detection of Fe3+ and arsenic species via smartphone-assisted machine learning toward rapid portable environmental monitoring.

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Τίτλος: AI-integrated chemosensing platform for sequential nanomolar detection of Fe3+ and arsenic species via smartphone-assisted machine learning toward rapid portable environmental monitoring.
Συγγραφείς: Gandra UR; Department of Aerospace Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates., Vallapu Reddy HR; Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA., Managutti PB; Chemical Crystallography Laboratory, Research Laboratories, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates., Samad YA; Department of Aerospace Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. Electronic address: yarjan.abdulsamad@ku.ac.ae., Hasan SW; Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Center for Membranes and Advanced Water Technology (CMAT), Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. Electronic address: shadi.hasan@ku.ac.ae.
Πηγή: Chemosphere [Chemosphere] 2026 Sep; Vol. 411, pp. 145078. Date of Electronic Publication: 2026 Sep 04.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Science Ltd Country of Publication: England NLM ID: 0320657 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1298 (Electronic) Linking ISSN: 00456535 NLM ISO Abbreviation: Chemosphere Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Elsevier Science Ltd
Original Publication: Oxford, New York, : Pergamon Press.
Ιατρικοί όροι (MeSH): Environmental Monitoring*/methods , Environmental Monitoring*/instrumentation , Arsenic*/analysis , Water Pollutants, Chemical*/analysis , Iron*/analysis , Ferric Compounds*/analysis , Smartphone* , Machine Learning*, Colorimetry/methods ; Rhodamines/chemistry ; Arsenites/analysis ; Artificial Intelligence ; Limit of Detection
Περίληψη: The integration of molecular colorimetric sensors with smartphone-assisted image analysis and machine learning has emerged as a next-generation sensing paradigm for environmental diagnostics. Herein, we report an artificial intelligence (AI)-integrated chemosensing platform based on a norbornene-appended rhodamine derivative (RhB-NBE) for the sequential monitoring of Fe3+ and arsenic species [As(V)/As(III))] in aqueous media. The probe was synthesized through a Schiff base condensation reaction and fully characterized using NMR, X-ray, and other spectroscopic techniques. RhB-NBE exhibited highly selective and instantaneous recognition of Fe3+ over competing metal ions, including Fe2+, through a spirolactam ring-opening mechanism that generated a discrete colorimetric response. The resulting RhB-NBE + Fe3+ ensemble served as a secondary sensing platform for arsenite and arsenate via a cation-displacement process, producing a visible color transition from pink to colorless with a low detection limit of 2 nM (below the World Health Organization critical limit). Practical applicability was demonstrated using portable paper-based sensing strips, and successful quantification of arsenic species in environmental water samples with excellent recovery values. Despite the rapid advancement of AI-assisted chemical sensing, integration of machine learning with colorimetric arsenic detection remains largely unexplored due to challenges in sensor reproducibility, image standardization, and reliable concentration classification. Herein, we combine a selective colorimetric chemosensor with AI-based image analytics for rapid field monitoring. Smartphone images were analyzed using GNB, SVM, and CNN models trained on 1000 RGB datasets; SVM achieved 97.0% accuracy for equipment-free arsenic screening.
(Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Arsenic detection; Artificial intelligence; Chemosensing; Portable environmental monitoring; Smartphone colorimetric analysis; Support vector machine (SVM)
Substance Nomenclature: N712M78A8G (Arsenic)
0 (Water Pollutants, Chemical)
E1UOL152H7 (Iron)
0 (Rhodamines)
0 (Arsenites)
0 (Ferric Compounds)
Entry Date(s): Date Created: 20260904 Date Completed: 20260919 Latest Revision: 20260919
Update Code: 20260920
DOI: 10.1016/j.chemosphere.2026.145078
PMID: 42696917
Βάση Δεδομένων: MEDLINE
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  Data: AI-integrated chemosensing platform for sequential nanomolar detection of Fe<superscript>3+</superscript> and arsenic species via smartphone-assisted machine learning toward rapid portable environmental monitoring.
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  Data: <searchLink fieldCode="AU" term="%22Gandra+UR%22">Gandra UR</searchLink>; Department of Aerospace Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.<br /><searchLink fieldCode="AU" term="%22Vallapu+Reddy+HR%22">Vallapu Reddy HR</searchLink>; Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA.<br /><searchLink fieldCode="AU" term="%22Managutti+PB%22">Managutti PB</searchLink>; Chemical Crystallography Laboratory, Research Laboratories, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.<br /><searchLink fieldCode="AU" term="%22Samad+YA%22">Samad YA</searchLink>; Department of Aerospace Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. Electronic address: yarjan.abdulsamad@ku.ac.ae.<br /><searchLink fieldCode="AU" term="%22Hasan+SW%22">Hasan SW</searchLink>; Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Center for Membranes and Advanced Water Technology (CMAT), Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. Electronic address: shadi.hasan@ku.ac.ae.
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  Data: The integration of molecular colorimetric sensors with smartphone-assisted image analysis and machine learning has emerged as a next-generation sensing paradigm for environmental diagnostics. Herein, we report an artificial intelligence (AI)-integrated chemosensing platform based on a norbornene-appended rhodamine derivative (RhB-NBE) for the sequential monitoring of Fe<superscript>3+</superscript> and arsenic species [As(V)/As(III))] in aqueous media. The probe was synthesized through a Schiff base condensation reaction and fully characterized using NMR, X-ray, and other spectroscopic techniques. RhB-NBE exhibited highly selective and instantaneous recognition of Fe<superscript>3+</superscript> over competing metal ions, including Fe<superscript>2+</superscript>, through a spirolactam ring-opening mechanism that generated a discrete colorimetric response. The resulting RhB-NBE + Fe<superscript>3+</superscript> ensemble served as a secondary sensing platform for arsenite and arsenate via a cation-displacement process, producing a visible color transition from pink to colorless with a low detection limit of 2 nM (below the World Health Organization critical limit). Practical applicability was demonstrated using portable paper-based sensing strips, and successful quantification of arsenic species in environmental water samples with excellent recovery values. Despite the rapid advancement of AI-assisted chemical sensing, integration of machine learning with colorimetric arsenic detection remains largely unexplored due to challenges in sensor reproducibility, image standardization, and reliable concentration classification. Herein, we combine a selective colorimetric chemosensor with AI-based image analytics for rapid field monitoring. Smartphone images were analyzed using GNB, SVM, and CNN models trained on 1000 RGB datasets; SVM achieved 97.0% accuracy for equipment-free arsenic screening.<br /> (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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