Automated Gold Nanorod Spectral Morphology Analysis Pipeline

Journal Article · 12 December 2024 · ACS Nano

DOI: https://doi.org/10.1021/acsnano.4c09753 · OSTI ID:2497290

Gleason, Samuel P.  ^[1]; Dahl, Jakob C.  ^[1]; Elzouka, Mahmoud ^[2]; Wang, Xingzhi ^[1]; Byrne, Dana O. ^[1]; Cho, Hannah ^[3]; Gababa, Mumtaz ^[3]; Prasher, Ravi S.  ^[2]; Lubner, Sean ^[2]; Chan, Emory M.  ^[4]; Alivisatos, A. Paul  ^[5]

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1. University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
3. University of California, Berkeley, CA (United States)
4. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Molecular Foundry
5. University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States)

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for 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 tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's 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 to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2497290
• Journal Information: ACS Nano, Journal Name: ACS Nano Journal Issue: 51 Vol. 18; ISSN 1936-0851
• Publisher: American Chemical Society (ACS)Copyright Statement
• Country of Publication: United States
• Language: English