A chlorella-derived carbon dots-based integrated sensing-removal platform for silver ion monitoring and removal via dual-mode optical detection and smartphone-assisted deep learning.

Chlorella-derived fluorescent carbon dots (CDs) were synthesized via a solvothermal route and a dual-mode colorimetric/fluorescent platform was constructed for Ag⁺ sensing based on coordination-induced visible color change. To enable portable analysis, the CDs were immobilized on filter paper to fabricate portable test strips. Smartphone-based image acquisition, coupled with a convolutional neural network (CNN) model running on a portable laptop, allowed intelligent extraction and quantitative interpretation of color features for Ag⁺ determination. Notably, the coordination/aggregation interaction between CDs and Ag+ generated a reddish-brown precipitate, which could be readily removed by centrifugation, achieving an Ag⁺ removal efficiency of over 86.7% and thereby coupling rapid detection with post-detection removal in a two-stage integrated platform. Under optimized conditions, the limits of detection for the fluorescent and colorimetric modes were 0.13 and 0.49 µM, respectively, while the CNN-assisted test-strip assay achieved a limit of detection of 0.88 µM. The recoveries for Ag⁺ in real samples (milk and tap water) ranged from 97.8% to 101.9%, in good agreement with inductively coupled plasma-mass spectrometry (ICP-MS) results. This work presents a functionally integrated, portable, and AI-assisted strategy for rapid Ag⁺ monitoring and removal in water and food-related matrices.