{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:20:07Z","timestamp":1784031607032,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes with closely spaced half-wave potentials, such as Cd2+ and Cu2+, remains a challenge, especially for non-expert users. In this work, we present a novel methodology that integrates advanced artificial intelligence (AI) models with ITIES-based sensing to automate and enhance metal ion detection. Our approach first employed a convolutional neural network to classify CVs as either ideal or faulty with an accuracy exceeding 95 percent. Ideal CVs were then further analyzed for metal ion identification, achieving a classification accuracy of 99.15 percent between Cd2+ and Cu2+ responses. Following classification, an artificial neural network was used to quantitatively predict metal ion concentrations, yielding low mean absolute errors of 0.0158 for Cd2+ and 0.0127 for Cu2+. This integrated AI\u2013ITIES system not only provides a scientific methodology for differentiating analyte responses based on electrochemical signatures but also substantially lowers the expertise barrier for sensor signal interpretation. To our knowledge, this is the first report of the AI-assisted differentiation and quantification of metal ions from ITIES-based CVs, establishing a robust framework for the future development of user-friendly, automated electrochemical sensing platforms for environmental and biological applications.<\/jats:p>","DOI":"10.3390\/jsan14040070","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T13:44:19Z","timestamp":1752241459000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI-Driven Differentiation and Quantification of Metal Ions Using ITIES Electrochemical Sensors"],"prefix":"10.3390","volume":"14","author":[{"given":"Muzammil M. N.","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Chemistry and Chemical Engineering, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4270-5733","authenticated-orcid":false,"given":"Parth","family":"Ganeriwala","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthi","family":"Savvidou","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Chemical Engineering, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Breen","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Chemical Engineering, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddhartha","family":"Bhattacharyya","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9222-9109","authenticated-orcid":false,"given":"Pavithra","family":"Pathirathna","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Chemical Engineering, Florida Institute of Technology, 150 W. University Blvd, Melbourne, FL 32901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s10534-010-9328-y","article-title":"Heavy metal poisoning: The effects of cadmium on the kidney","volume":"23","author":"Johri","year":"2010","journal-title":"BioMetals"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8803","DOI":"10.1039\/D3RA00789H","article-title":"A review on arsenic in the environment: Contamination, mobility, sources, and exposure","volume":"13","author":"Patel","year":"2023","journal-title":"RSC Adv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100650","DOI":"10.1016\/j.cdc.2021.100650","article-title":"Data on the detection of essential and toxic metals in soil and corn and barley grains by atomic absorption spectrophotometry and their effect on human health","volume":"32","author":"Custodio","year":"2021","journal-title":"Chem. 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