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AI-Augmented Analysis of FT-IR Spectroscopy Data for Nanomaterials Characterization: A Review of Current Applications and Future Directions
Summary
AI and machine learning techniques — from unsupervised clustering to deep learning — are increasingly used to automate the interpretation of FTIR spectroscopy data for nanomaterial characterization, including nanoplastic pollutants. This matters because high-throughput AI-assisted spectral analysis could significantly accelerate the identification and classification of microplastics and nanoplastics in environmental samples.
The characterization of nanomaterials by Fourier-transform infrared (FTIR) spectroscopy generates complex, high-dimensional datasets that present significant challenges for traditional manual interpretation methods. Overlapping spectral bands, subtle chemical variations, and the need for high-throughput analysis have driven the integration of artificial intelligence (AI) and machine learning (ML) into spectroscopic workflows. This paper reviews the current state of AI applications in the analysis of FTIR data for nanomaterials. We explore how ML techniques—ranging from unsupervised learning for cluster analysis to supervised deep learning for property prediction—are being deployed to overcome the limitations of conventional chemometrics. Key applications discussed include the automated classification of graphene oxide derivatives, the analysis of nanoplastic pollutants, and the extraction of physical properties from nanoscale infrared spectra. The review also addresses critical challenges such as model interpretability, data standardization, and generalization. Finally, we outline future directions, including physics-informed neural networks, foundation models, and the development of autonomous, self-driving spectroscopic laboratories [6,7].