Nanomaterials for Supercapacitors: Uncovering Research Themes with Unsupervised Machine Learning

TL;DR

This study uses unsupervised machine learning to identify and analyze key research themes in supercapacitor literature from 2004 to 2021, revealing dominant topics and future directions.

Contribution

It introduces a novel application of NLP and topic modeling to systematically uncover research themes and trends in supercapacitor studies.

Findings

Performance metrics are the most emphasized topic (28.2%)

Flexible electronics constitute 8% of research focus

Graphene-based nanocomposites are a significant theme (10.9%)

Abstract

Identification of important topics in a text can facilitate knowledge curation, discover thematic trends, and predict future directions. In this paper, we aim to quantitatively detect the most common research themes in the emerging supercapacitor research area, and summarize their trends and characteristics through the proposed unsupervised, machine learning approach. We have retrieved the complete reference entries of article abstracts from Scopus database for all original research articles from 2004 to 2021. Abstracts were processed through a natural language processing pipeline and analyzed by a latent Dirichlet allocation topic modeling algorithm for unsupervised topic discovery. Nine major topics were further examined through topic-word associations, Inter-topic distance map and topic-specific word cloud. We observed the greatest importance is being given to performance metrics (28.2%), flexible electronics (8%), and graphene-based nanocomposites (10.9%). The analysis also points out crucial future research directions towards bio-derived carbon nanomaterials (such as RGO) and flexible supercapacitors.