Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic...

Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents a novel approach by developing two probes, A1 and A2, based on 4-diethylaminosalicyladehyde, 2-hydroxy-1-naphthaldehyde, and 1,2-diaminoanthraquinone. These probes are highly sensitive and selective for detecting arsenite (As(III)) and arsenate (As(V)) in water, food samples, and homeopathic medicine with limits of detection in the nanomolar range. To elaborate our contribution to on-site arsenic detection, we introduce a convolutional neural network-based image recognition system. This system interprets images of the probes' colorimetric response, effectively categorizing different ranges of arsenic concentrations in parts per million (ppm). Our approach offers a real-time, cost-effective, and user-friendly solution for arsenic detection, extending its applicability from scientific laboratories to in-field conditions and even household monitoring. The findings fill critical research gaps in real-time detection methods, potentially revolutionizing the way we monitor environmental contaminants like arsenic.