Smartphone-integrated paper sensor strips for rapid and on-site detection of heavy metal ions in environmental water samples
S. S. Malik, Joginder Singh, Ahmad Umar, Ahmed A. Ibrahim, Sotirios Baskoutas
Abstract
The detection of heavy metal ions is critical for environmental monitoring, public health, and industrial safety due to their toxicological and bioaccumulative properties. Traditional analytical techniques, such as inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS), offer high sensitivity and specificity but are limited by their reliance on expensive instrumentation, specialized expertise, and laboratory infrastructure, making them impractical for real-time, on-site applications. To address these limitations, this study presents a smartphone-assisted paper-based sensor strip (WFP-113 and WFP-4) for the simultaneous detection of copper (Cu(II)), chromium (Cr(VI)), zinc (Zn(II)), and manganese (Mn(II)) ions in water samples. The sensor employs chromogenic reagents that produce distinct colorimetric responses upon metal ion complexation, enabling visual and quantitative analysis via smartphone imaging and digital processing. Key analytical parameters, including linear detection range, limit of detection (LOD), limit of quantification (LOQ), reproducibility, and selectivity, were systematically optimized. The achieved LODs for WFP-113 and WFP-4 were 0.42, 0.32, 0.79, 0.30 mg L −1 and 0.30, 0.26, 0.72, 0.28 mg L −1 for Cu(II), Cr(VI), Zn(II), and Mn(II), respectively, meeting regulatory standards for water quality assessment. Validation using spiked water samples demonstrated high accuracy and reliability compared to AAS, confirming the sensor's applicability in field-based monitoring. The proposed system offers significant advantages, including low cost, rapid analysis, portability, and ease of fabrication, while smartphone integration bridges the gap between qualitative and quantitative analysis. This innovation holds substantial promise for environmental monitoring, point-of-care diagnostics, and resource-limited settings, providing a sustainable and scalable solution for heavy metal detection.