文章基本信息

标题：Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities
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作者：Kevin Cruse ; amalie trewartha ; Sanghoon Lee 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-12
DOI：10.1038/s41597-022-01321-6
语种：English
出版社：Nature Publishing Group
摘要：Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can ofer insight to help guide understanding of these underlying mechanisms, so long as sufcient synthesis data are available . To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codifed gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques . This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, fltered from a database of 4,973,165 publications . Each record contains codifed synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs .