DL-assisted self-volume-calibrating colorimetric PAAHM sensors for water surveillance.

This work introduces a colorimetric sensing platform based on sodium polyacrylate hydrogel microspheres (PAAHM) integrated with a deep learning-assisted self-volume calibration strategy for the efficient and quantitative detection of NH4+, PO43-, and Fe2+ in water. The PAAHM are uniformly loaded with colorimetric indicators through a simple immersion process, significantly simplifying sensor fabrication and enabling rapid, consistent, large-scale production. Moreover, the PAAHM exhibits dual responsiveness-both colorimetric and volumetric-allowing for the simultaneous detection of analyte concentration changes and sample volume fluctuations. To address the limitations of conventional RGB analysis, a self-volume calibration method was implemented in conjunction with a Convolutional Neural Network (CNN) to automatically extract and model both colour and morphological features from sensor images. The results demonstrate that the CNN model achieves an R2 correlation coefficient of 0.999 for concentration prediction and 100% classification accuracy. This approach offers a convenient, low-cost, and highly accurate solution for on-site environmental monitoring, presenting significant potential for broad application.