Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

TL;DR

This paper presents a large, publicly available dataset of gold nanoparticle synthesis procedures and outcomes, extracted from scientific literature using NLP, to aid data-driven understanding of nanoparticle morphology control.

Contribution

The authors created and shared a comprehensive dataset of over 5,000 gold nanoparticle synthesis records extracted via text-mining from nearly 5 million publications, enabling data-driven research.

Findings

Dataset contains 5,154 records with synthesis protocols and morphological data.

Extracted data from 4,973,165 publications using NLP techniques.

Provides a resource to explore mechanisms of nanoparticle size and shape control.

Abstract

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 offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified 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, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.