Determination of Iodide and pH by Monitoring the Fluorescence Lifetime of Fluorescein and a Deep Learning-Based Model

The effective treatment of iodine-containing wastewater is essential for environmental protection, as elevated iodide concentrations can adversely affect ecosystems and human health. Traditional methods for iodide monitoring and pH adjustment often involve time consuming multi step procedures, limiting their efficiency in real time wastewater management. In this study, we introduce a deep learning assisted analytical strategy that enables the simultaneous determination of iodide concentration and pH from a single fluorescence lifetime decay curve. The approach is based on the dual sensitivity of fluorescein sodium, which exhibits distinct fluorescence responses in different environments: protonation occurs under acidic conditions, deprotonation under alkaline conditions, and dynamic fluorescence quenching is triggered by iodides. By integrating time correlated single-photon counting (TCSPC) with a hybrid convolutional neural network–residual network–random forest regression (CNN-ResNet-RF) model, we achieve rapid and accurate prediction of both parameters without chemical separation or additional reagents. The proposed model not only overcomes the nonlinear coupling between pH and iodide effects on fluorescence lifetime but also demonstrates strong generalization performance in simulated wastewater matrices. This work provides a robust, reagent-free, and high-throughput analytical platform for real-time monitoring and process control in iodine-containing wastewater treatment.