论文标题
超级电容器的纳米材料：通过无监督的机器学习发现研究主题
Nanomaterials for Supercapacitors: Uncovering Research Themes with Unsupervised Machine Learning
论文作者
论文摘要
文本中重要主题的识别可以促进知识策划，发现主题趋势并预测未来的方向。在本文中，我们旨在定量检测新兴的超级电容器研究领域中最常见的研究主题，并通过提出的无监督的机器学习方法来总结其趋势和特征。我们已经从Scopus数据库中检索了2004年至2021年所有原始研究文章的文章摘要的完整参考条目。摘要是通过自然语言处理管道处理的，并通过潜在的Dirichlet分配主题分配算法进行分析，以实现无处可比性的主题发现。通过主题字关联，主题间距离图和特定于主题的单词云进一步研究了九个主要主题。我们观察到对性能指标（28.2％），柔性电子设备（8％）和基于石墨烯的纳米复合材料（10.9％）的最重要性。该分析还指出了对生物衍生的碳纳米材料（例如RGO）和灵活的超级电容器的至关重要的研究方向。
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.
