Graphene biosensor platform for quantifying pesticide drift and runoff in agricultural and urban watersheds

Overview

This solution offers an inexpensive, field-deployable biosensor system designed to quantify pesticide spray drift and runoff across agricultural and urban watersheds. By combining disposable graphene-based microfluidic test strips with machine learning algorithms and a smartphone application, the platform enables rapid, on-site detection of multiple pesticide classes from a single water sample. The technology addresses the critical need for accessible environmental monitoring tools that can replace expensive laboratory analysis, allowing researchers and decision-makers to map pesticide distribution at landscape scale and evaluate ecological impacts on sensitive species such as mosquitoes.

Technical specifications

• Graphene-based open microfluidics divide a single water sample across multiple biosensors, enabling simultaneous detection of several pesticide classes from one test strip
• Biosensor detection capabilities include organophosphates (detection limit of 0.6 nM), neonicotinoids (detection limit of 340 nM), and preliminary detection of herbicides such as atrazine and glyphosate in the nM regime
• Sensitivity below regulatory thresholds — detection limits are lower than target drinking water limits for monitored compounds
• Low-cost, disposable test strips designed for high-volume deployment across large watersheds
• Smartphone application integration for real-time data capture and visualization in the field
• Artificial intelligence algorithms to filter and interpret multiple streams of biosensor data
• Mosquito phenotypic analysis to correlate pesticide exposure with outcomes such as fecundity, development, and insecticide resistance in species like Culex pipiens

Technology readiness level

The underlying graphene-based open microfluidics and individual biosensors have already been developed and published. Detection limits for organophosphates and neonicotinoids have been validated below drinking water regulatory thresholds, and preliminary data supports herbicide detection in the nM range. Baseline mosquito insecticide resistance characterization is currently underway in Iowa. Future development will focus on expanding the biosensor repertoire to cover the full range of pesticides found in field samples, integrating AI for multi-analyte data interpretation, and conducting field validation studies linking pesticide exposure to mosquito phenotypic outcomes to inform regulatory tolerance levels and application practices.