Automated\nGold Nanorod Spectral Morphology Analysis\nPipeline

The development of\na colloidal synthesis procedure to produce nanomaterials\nwith high shape and size purity is often a time-consuming, iterative\nprocess. This is often due to quantitative uncertainties in the required\nreaction conditions and the time, resources, and expertise intensive\ncharacterization methods required for quantitative determination of\nnanomaterial size and shape. Absorption spectroscopy is often the\neasiest method for colloidal nanomaterial characterization. However,\ndue to the lack of a reliable method to extract nanoparticle shapes\nfrom absorption spectroscopy, it is generally treated as a more qualitative\nmeasure for metal nanoparticles. This work demonstrates a gold nanorod\n(AuNR) spectral morphology analysis tool, called AuNR-SMA, which is\na fast and accurate method to extract quantitative structural information\nfrom colloidal AuNR absorption spectra. To demonstrate the practical\nutility of this model, we apply it to three distinct applications.\nFirst, we demonstrate this model’s utility as an automated\nanalysis tool in a high-throughput AuNR synthesis procedure by generating\nquantitative size information from optical spectra. Second, we use\nthe predictions generated by this model to train a machine learning\nmodel to predict the resulting AuNR size distributions under specified\nreaction conditions. Third, we apply this model to spectra extracted\nfrom the literature where no size distributions are reported and impute\nunreported quantitative information on AuNR synthesis. This approach\ncan potentially be extended to any other nanocrystal system where\nabsorption spectra are size dependent, and accurate numerical simulation\nof absorption spectra is possible. In addition, this pipeline could\nbe integrated into automated synthesis apparatuses to provide interpretable\ndata from simple measurements, help explore the synthesis science\nof nanoparticles in a rational manner, or facilitate closed-loop workflows.