The Polymer Society of Korea

Abstract

Corona phases on single-walled carbon nanotubes generate rich near-infrared (nIR) fluorescence signals that encode molecular interactions. By tailoring these soft-matter interfaces, we produces multidimensional spectral and spatial responses beyond conventional receptor-based sensing. Recent advances in our group integrate artificial intelligence with the corona phase engineering, where machine learning decodes hidden multispectral features within nIR spectra to reveal subtle fingerprints that classical analysis cannot capture. In parallel, deep learning applied to nIR imaging performs pixel-level feature extraction and object detection, allowing high-throughput mapping of cellular heterogeneity and dynamic chemical process. In this talk, I will highlight how corona phase engineering, combined with AI-driven interpretation of nIR spectral and imaging data, opens a route to multidimensional precision chemical information transfer for diagnostics and process analytics.