Detecting the environmental impact of nanoparticles using plant-based biosensors

The increased manufacturing of nanoparticles for use in cosmetics, foods and clothing, necessitates the need for an effective system to evaluate the potential side-effects (e.g., toxicity) of nanoparticles to the environment. A sensitive detection system would serve as a sentinel to characterize and understand the impact, and monitor the toxicity level for any potential health or safety concerns. In this paper, we proposed a plant biosensor for characterizing, monitoring, and understanding the environmental impact of both naturally occurring and man-made nanoparticles. The health of the plant sensor was monitored using an automated camera system. A machine learning-based approach was used to train the sensor to increase accuracy and adaptability with various plant sensors. The biosensor has been tested and validated in vitro. The plant sensor was proposed because it is cost-effective, and can be easily embedded into the environment. Moreover, it can detect not only exposure of water and soil, but also air. Since environmental toxicity can be measured by monitoring the health of the plant, the plant sensor can be used to monitor any potential hazard. In this study, we have demonstrated a proof-of-concept bio-sensor system for effectively detecting environmental exposure from nanoparticles. The preliminary experimental results have demonstrated the effectiveness of the approach, and the adaptability of the proposed system.