Academic Journal
AI-integrated chemosensing platform for sequential nanomolar detection of Fe3+ and arsenic species via smartphone-assisted machine learning toward rapid portable environmental monitoring.
| 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 |
καταχωρήστε σχόλιο πρώτοι!