Automated Gold Nanorod Spectral Morphology Analysis Pipeline

Area of Science:

• Nanomaterial Science
• Spectroscopy
• Computational Chemistry

Background:

• Colloidal nanomaterial synthesis is often slow and iterative due to challenges in accurately determining size and shape.
• Absorption spectroscopy is a simple characterization method but lacks reliable quantitative shape extraction for metal nanoparticles.
• Existing methods for nanomaterial characterization are often time-consuming, resource-intensive, and require specialized expertise.

Purpose of the Study:

• To develop a fast and accurate method, AuNR-SMA, for extracting quantitative structural information from gold nanorod (AuNR) absorption spectra.
• To demonstrate the practical applications of AuNR-SMA in high-throughput synthesis, machine learning-based prediction, and literature data imputation.
• To provide a framework for extending spectral morphology analysis to other nanocrystal systems and integrating it into automated synthesis workflows.

Main Methods:

• Development of the AuNR spectral morphology analysis (AuNR-SMA) tool for quantitative analysis of colloidal AuNR absorption spectra.
• Application of AuNR-SMA for automated analysis in high-throughput synthesis, providing quantitative size information from optical spectra.
• Training a machine learning model using AuNR-SMA predictions to forecast AuNR size distributions based on reaction conditions.

Main Results:

• AuNR-SMA successfully extracts quantitative structural information from AuNR absorption spectra, overcoming limitations of qualitative analysis.
• The tool was effectively used to automate analysis in high-throughput synthesis and to impute missing size distribution data from literature.
• A machine learning model trained on AuNR-SMA predictions demonstrated the ability to predict AuNR size distributions under specific synthesis conditions.

Conclusions:

• AuNR-SMA offers a fast, accurate, and quantitative approach to characterize gold nanorods using absorption spectroscopy.
• This spectral morphology analysis method has broad applicability in accelerating nanomaterial synthesis, enabling data imputation, and facilitating rational design.
• The developed pipeline can be extended to other nanocrystal systems and integrated into automated platforms for closed-loop synthesis and exploration.