Innovative integration of machine learning and colorimetry for precise potential of hydrogen monitoring in printed hydrogel sensors

Abstract

Proper potential of hydrogen (pH) monitoring finds wide applications in environmental monitoring, clinical diagnostics, and a variety of industrial processes. However, traditional pH sensors normally present several challenges related to adaptability, portability, and environmental compatibility. In addition, the recently developed hydrogel-based sensors have manifested several advantages due to the flexibility and biocompatibility of the material in a wide variety of applications. While much advancement has been made in integration techniques, further advances need improvement in precision and reliability. The present work describes a novel methodology of pH sensing through integration of hydrogel-based sensors with machine learning algorithms. pH-sensitive dye-impregnated hydrogel sensors have been fabricated using three-Dimensional (3D) printing technology, whereby colorimetric data analysis is combined with five machine learning models, namely Decision Trees, eXtreme Gradient Boosting, K-Nearest Neighbours, Random Forests, and Neural Networks, in the classification of pH based on Red, Green, Blue (RGB) data. The sensor designed can detect pH between 4 and 10 pH with high speed, stability, and reversibility. With precision, recall, and F1-scores all above 99%, this shows how efficient the classification approach is based on RGB and gives weight to the potential of the developed sensors for real-time applications in monitoring and diagnostics, hence making a big contribution to the evolution of pH sensing and paving the way for smarter, more adaptable sensor solutions.