Kavli Affiliate: Paul Alivisatos
| First 5 Authors: Akshay Subramanian, Kevin Cruse, Amalie Trewartha, Xingzhi Wang, A. Paul Alivisatos
| Summary:
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 images across a
variety of experimental conditions is expensive and infeasible. Published
scientific literature, which provides a vast source of high-quality figures
including SEM/TEM images, can provide a large amount of data at a lower cost if
effectively mined. In this work, we develop an automated pipeline to retrieve
and analyse microscopy images from gold nanoparticle literature and provide a
dataset of 4361 SEM/TEM images of gold nanoparticles along with automatically
extracted size and morphology information. The dataset can be queried to obtain
information about the physical attributes of gold nanoparticles and their
statistical distributions.
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