Passive Smart Dust for Detecting and Classifying Fuel Spills: Drone-Based Colorimetric Imaging Using Solvatochromic Paper Sensors

Rapid detection and localization of liquid fuel spills is critical for first responders assessing fire and health hazards, yet current methods require ground-based sampling or specialized instrumentation, limiting their practicality for wide-area emergency response. We present a drone-based passive colorimetric sensor system using test strips impregnated with Nile red, similar to colored confetti. Nile red is a solvatochromic dye that undergoes distinct visible color transitions upon exposure to different liquids. The dye is embedded within a polymer matrix that minimizes leaching while providing high optical contrast between dry, water-exposed, and fuel-exposed states. The sensor strips exhibit solvent-specific colorimetric responses within one minute of exposure, readily detectable by standard RGB cameras mounted on unmanned aerial vehicles (UAV) at altitudes up to 50 m. Automated classification was validated at 20 m altitude, enabling remote surveillance of contaminated surfaces without specialized equipment. Color-corrected image analysis using Calibrite ColorChecker calibration ensures reliable interpretation under variable field illumination (625–77,000 lux). Systematic laboratory evaluation of twelve fossil and bio-derived fuels revealed characteristic hue shifts that clearly discriminate ethanol-containing gasoline blends from diesel-range fuels. Field validation confirmed localization and classification of fuel-exposed sensors, achieving F1 scores of 0.94 for gasoline and 0.98 for diesel detection with no false positives in the tested scenarios. This cost-effective and scalable approach provides actionable information on both contamination location and fuel type, crucial for rapid hazard assessment in emergency response scenarios.