Collaborative Internal Cavity Effect and Interfacial Modulation Mechanism for Boosting Deep Learning-Powered Immunochromatographic Pathogen Detection.

Abstract

Nanoenabled immunochromatographic assay (ICA) emerges as a powerful tool for pathogen diagnosis, yet current nanotechnologies are still constrained by inadequate light-matter interaction efficiency, sluggish nanomaterial flow dynamics, and inefficient immunorecognition. Herein, we present a deep learning-enhanced immunoassay synergistically leveraging the internal cavity effect of hollow carbon nanospheres (h-CNSs) and interfacial antibody orientation modulation for the ultrasensitive detection of S. typhimurium. The h-CNSs exhibit significantly enhanced light absorption (molar extinction coefficients 5.4 × 1011 vs. 3.7 × 1011 L mol-1 cm-1 for counterpart) and photothermal conversion efficiency (66.78% vs. 43.37%) due to internal light reflection within the hollow cavity, while the reduced density (0.05 g mL-1) optimizes lateral flow kinetics. Further interfacial modification with 3,5-dicarboxybenzeneboronic acid enables directional antibody immobilization through boronate affinity, improving antibody binding affinity by 83-fold (Ka = 2.95 × 109 vs. 3.55 × 107 M-1). Integrated into an ICA, D-h-CNSs achieve visual detection limits of 500 CFU mL-1 (colorimetric) and 100 CFU mL-1 (photothermal), surpassing conventional ICA (104 CFU mL-1) and demonstrating high specificity, robust stability, and reliable performance in spiked milk and lettuce. By integration with a convolutional neural network (CNN), the developed nanoplatform achieves 100% accuracy for S. typhimurium detection with augmented training, providing a paradigm for amplifying biosensing signals through nanomaterial design and intelligent data analysis.