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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Marine & Wildlife Policy & Risk Sign in to save

Can Smart Electrochemical Sensors Sustainably Tackle PFAS-@Microplastics?

ECS Sensors Plus 2025 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 53 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Justin Sanchez-Almirola, Andrea Lee, Ajeet Kaushik R. J. Orr, R. J. Orr, Justin Sanchez-Almirola, Ajeet Kaushik Ajeet Kaushik Jasmina Casals‐Terré, Jasmina Casals‐Terré, Jasmina Casals‐Terré, Jasmina Casals‐Terré, Ajeet Kaushik Jasmina Casals‐Terré, Ajeet Kaushik Ajeet Kaushik Ajeet Kaushik

Summary

This perspective explores the potential for smart electrochemical sensors to detect both microplastics and per- and polyfluoroalkyl substances in water systems. While existing sensors can detect each pollutant separately at very low concentrations, simultaneous detection of PFAS bound to microplastics has not yet been achieved. The authors suggest that integrating internet-of-things connectivity and artificial intelligence modeling could advance real-time environmental monitoring of these combined contaminants.

Microplastics (MPs, long-lasting pollutants) can carry per- and polyfluoroalkyl substances (PFAS, forever chemicals) in aquatic systems. PFAS@MP complexation is a threat to the human cycle, and tackling this issue is crucial for sustainability. Electrochemical sensors detect MPs (ppb) and PFAS (ppt) selectively, but simultaneous detection of both in the PFAS@MP complexation has not been demonstrated. To handle these issues, the internet-of-things-based sensing technology should be tuned to perform MPs/PFAS sensing at point-of-care applications. Furthermore, incorporating artificial intelligence (AI)-based modeling can aid in risk assessments and decision-making to facilitate efficient management and monitoring of a sustainable environment.

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