2021
DOI: 10.48550/arxiv.2112.01689
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Dataset of gold nanoparticle sizes and morphologies extracted from literature-mined microscopy images

Abstract: The factors controlling the size and morphology of nanoparticles have so far been poorly understood. Data-driven techniques are an exciting avenue to explore this field through the identification of trends and correlations in data. However, for these techniques to be utilized, large datasets annotated with the structural attributes of nanoparticles are required. While experimental SEM/TEM images collected from controlled experiments are reliable sources of this information, large-scale collection of these imag… Show more

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“…[229] A similar algorithm was adopted by Subramanian et al but within a larger corpus of articles, that is, Elsevier, The Royal Society of Chemistry, Nature Publishing Group, and Springer, focusing on TEM and SEM images to improve the segmentation and morphology classification and size measurement of the Au nanoparticles. [230] Cruse et al even started from millions of publications from material science journals to understand the underlying mechanisms controlling their size and shape of Au nanoparticles. [231] As the advancements of NLP and LLM, state-of-the-art AI models such as ChemDataExtractor, [232] OSCAR4, [233] and GPT [234,235] have been harnessed to better extract the information from the literatures and understand the chemical principles.…”
Section: Ai-instructed Synthesis Substrates and Reportersmentioning
confidence: 99%
“…[229] A similar algorithm was adopted by Subramanian et al but within a larger corpus of articles, that is, Elsevier, The Royal Society of Chemistry, Nature Publishing Group, and Springer, focusing on TEM and SEM images to improve the segmentation and morphology classification and size measurement of the Au nanoparticles. [230] Cruse et al even started from millions of publications from material science journals to understand the underlying mechanisms controlling their size and shape of Au nanoparticles. [231] As the advancements of NLP and LLM, state-of-the-art AI models such as ChemDataExtractor, [232] OSCAR4, [233] and GPT [234,235] have been harnessed to better extract the information from the literatures and understand the chemical principles.…”
Section: Ai-instructed Synthesis Substrates and Reportersmentioning
confidence: 99%