A. Paul Alivisatos’s research while affiliated with University of Chicago and other places

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.

Gold nanoparticles (AuNPs) are widely used functional nanomaterials that exhibit adjustable properties depending on their shapes and sizes. Creating a comprehensive dataset of AuNP syntheses is useful for understanding how to control their morphology and size. Here, we employed search-based algorithms and fine-tuned the Llama-2 large language model to extract 492 multi-sourced seed-mediated AuNP synthesis recipes from the literature. With this dataset which we share online, we verified that the type of seed capping agent such as CTAB or citrate plays a crucial role in determining the morphology of the AuNPs, aligning with established findings in the field. We also observe a weak correlation between the final AuNR aspect ratio and silver concentration, although a large variance reduces the significance of this relationship. Overall, our work demonstrates the value of literature-based datasets in advancing knowledge in the field of nanomaterial synthesis for further exploration and better reproducibility.

Lead-halide perovskite nanocrystals have recently emerged as desirable optical materials for applications such as coherent quantum light emitters and solid-state laser cooling due to their short radiative lifetime and near-unity photoluminescence quantum yield. Here, we investigate the effect of CsPbBr3 nanocrystal size on the radiative lifetime under ambient conditions. High-quality nanocrystals, with monoexponential time-resolved photoluminescence decay behaviors, unveil a non-monotonic trend in radiative lifetime. This non-monotonicity appears to reflect a behavior common among II-VI (CdSe) and perovskites semiconducting nanocrystals. We find that large nanocrystals in the weak quantum confinement regime exhibit long radiative lifetimes due to a thermally accessible population of dim states. Small nanocrystals within the strong quantum confinement regime, surprisingly, also show long radiative lifetimes, due however to a substantial reduction in oscillator strength. Nanocrystals in the intermediate quantum confinement regime displays the shortest radiative lifetime, as their oscillator strength is enhanced relative to particles in the strong confinement regime, but do not have sufficient low-lying dim states like the large particles to counteract this affect. These findings shed light on the impact of nanocrystal size on radiative lifetime and pave the way for tailored optical materials in various optical applications.

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.