Volume 5 Number 1 2026
Electrochemical Sensing of Heavy Metals in Water for Field Deployment
Eafaz Uddin Pantho 1*
Biosensors and Nanotheranostics 5 (1) 1-8 https://doi.org/10.25163/biosensors.5110952
Submitted: 13 August 2026 Revised: 01 October 2026 Accepted: 08 October 2026 Published: 10 October 2026
Heavy metals such as lead, cadmium, mercury, arsenic, chromium, copper and zinc do not degrade, and their slow accumulation in water continues to shape human health. Laboratory spectrometry remains the regulatory reference, yet it is slow, costly and fixed in place, so short-lived contamination events can pass unrecorded. Electrochemical sensing has become the most credible alternative, although whether it can leave the bench is still uncertain. This review examines literature published between January 2020 and early 2026, with deliberate emphasis on 2025 and 2026, across three connected fronts: nanomaterial interface engineering, the hardware and machine-learning stack that turns noisy field signals into defensible numbers, and the autonomous platforms that carry sensors into rivers, lagoons and mining catchments. Four databases (Scopus, Web of Science, PubMed and Google Scholar) were searched, records were screened against explicit eligibility criteria, and findings were synthesised narratively, with deployment studies appraised on a monitoring-readiness scale. Porous frameworks and bio-receptors reported the lowest detection limits, yet field-deployed systems leaned mostly on more modest bismuth and carbon composites. Instrumentation has consolidated from breadboard prototypes to single-chip systems. Machine-learning models often reported accuracies above 95%, but most were trained on small datasets and omitted uncertainty estimates. Most platforms appeared to sit at low readiness levels, validated for days in synthetic buffer rather than for months in authentic water. Taken together, dissolved organic matter, biofouling and drift, rather than sensitivity, now seem to limit translation. We outline four priorities: anti-fouling interfaces, built-in sample pretreatment, uncertainty-aware algorithms and harmonised reporting standards.
Keywords: Electrochemical sensing; Heavy metal detection; Nanomaterial electrodes; Stripping voltammetry; Machine learning calibration; Biofouling; Autonomous water monitoring
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