Kavli Affiliate: Paul Alivisatos
| First 5 Authors: Samuel P. Gleason, Jakob C. Dahl, Mahmoud Elzouka, Xingzhi Wang, Dana O. Byrne
| Summary:
The development of a colloidal synthesis procedure to produce nanomaterials
of a specific size with high shape and size purity is often a time consuming,
iterative process. This is often due to the time, resource and expertise
intensive characterization methods required for quantitative determination of
nanomaterial size and shape. Absorption spectroscopy is often the easiest
method of colloidal nanomaterial characterization, however, due to the lack of
a reliable method to extract nanoparticle shapes from absorption spectroscopy,
it is generally treated as a more qualitative measure for metal nanoparticles.
This work demonstrates a gold nanorod (AuNR) spectral morphology analysis (SMA)
tool, AuNR-SMA, which is a fast and accurate method to extract quantitative
information about an AuNR sample’s structural parameters from its absorption
spectra. We apply AuNR-SMA in three distinct applications. First, we
demonstrate its utility as an automated analysis tool in a high throughput AuNR
synthesis procedure by generating quantitative size information from optical
spectra. Second, we use the predictions generated by this model to train a
machine learning model capable of predicting the resulting AuNR size
distributions from the reaction conditions used to synthesize them. Third, we
turn this model to spectra extracted from the literature where no size
distributions are reported to impute unreported quantitative information of
AuNR synthesis. This approach can potentially be extended to any other
nanocrystal system where the absorption spectra are size dependent and accurate
numerical simulation of the absorption spectra is possible. In addition, this
pipeline could be integrated into automated synthesis apparatuses to provide
interpretable data from simple measurements and help explore the synthesis
science of nanoparticles in a rational manner or facilitate closed-loop
workflows.
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