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Toward a Multi-Analyte Smartphone Platform for Scalable Global Water Intelligence: Beyond Microplastics/Nanoplastics
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Researchers propose a modular smartphone platform initially designed for microplastic and nanoplastic detection that can expand to monitor turbidity, heavy metals, PFAS, and other contaminants through a shared hardware and data infrastructure, arguing that MP detection — among the most analytically challenging field targets — proves the concept's scalability to broader water quality monitoring.
Water testing has historically been fragmented, with different tools, workflows, and laboratories required for different contaminants or quality indicators. This model can be effective for specialized investigations but is difficult to scale for routine, geographically dense, and repeat monitoring across households, communities, schools, utilities, NGOs, and field teams. A more practical long-term approach may be modular environmental intelligence platforms that share common hardware, software, and data infrastructure across multiple analytes. This paper outlines a smartphone-based framework initially developed for microplastic and nanoplastic detection and expandable to additional targets including turbidity, copper, lead, arsenic, surfactants, PFAS, and future analytes. The central concept is a reusable backbone: sample collection, reagent interaction (when needed), smartphone imaging, computational analysis, and geospatial data integration. Microplastics and nanoplastics serve as a flagship proof-of-capability because they represent one of the most challenging categories for field detection. If a platform can address that challenge, expansion to additional targets becomes increasingly practical.
More Papers Like This
Toward a Multi-Analyte Smartphone Platform for Scalable Global Water Intelligence: Beyond Microplastics/Nanoplastics
AI summary Read the abstract
Researchers propose a modular smartphone-based platform initially built for microplastic and nanoplastic detection that can expand to screen multiple water contaminants — turbidity, heavy metals, PFAS, and surfactants — sharing common hardware, software, and geospatial data infrastructure to enable scalable, distributed water quality monitoring.
Designing Field-Deployable, AI Smartphone-Based Diagnostics for Global Scalable Water Intelligence: The EcoExposure Platform
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Researchers describe the EcoExposure platform, a smartphone-native water quality testing system designed around real-world field constraints rather than miniaturized lab instruments, with microplastics and nanoplastics as the flagship analyte and extensibility planned for PFAS, heavy metals, and other contaminants.
Why Scalable Water Intelligence Requires Field Methods: The Limitations of Centralized Laboratories
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Researchers argue that centralized laboratory workflows are structurally ill-suited for routine, geographically dense water quality monitoring, and present a smartphone-based biodegradable-reagent assay platform as a practical field-first alternative capable of detecting microplastics and nanoplastics at household and community scale.
Why Scalable Water Intelligence Requires Field Methods: The Limitations of Centralized Laboratories
AI summary Read the abstract
Researchers argue that centralized laboratory workflows are structurally ill-suited for routine, geographically dense water quality monitoring, and present a smartphone-based biodegradable-reagent assay platform as a practical field-first alternative capable of detecting microplastics and nanoplastics at household and community scale.
Designing Field-Deployable, AI Smartphone-Based Diagnostics for Global Scalable Water Intelligence: The EcoExposure Platform
AI summary Read the abstract
Researchers designed EcoExposure, a smartphone-based AI diagnostic platform for field-deployable water quality monitoring, arguing that existing centralized lab workflows create critical data gaps for microplastic and nanoplastic surveillance across underserved communities and low-resource environments.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.