Uncovering and Analyzing the Scientific Impact of LXCat in Low-Temperature Plasma (LTP) Research: An NLP and Data-Driven Approach

The modeling of LTPs and interpretation of LTP experiments relies heavily on accurate electron and ion collision data, swarm parameters etc., much of which is hosted on LXCat, a community-driven, open-access platform central to the LTP research ecosystem. Despite LXCat’s wide usage, its evolving influence on the scientific landscape of LTP research has not been systematically quantified. In this study, we investigate the scholarly impact of the LXCat platform over the past decade using a NLP framework applied to a curated corpus of approximately 500 full-text research papers that cite three foundational LXCat publications [1,2,3]. We present a comprehensive pipeline that includes data collection, cleaning, and processing, followed by the application of state-of-the-art NLP techniques informed by domain expertise. Our analysis identifies dominant gas species and links their usage trends to specific LXCat databases through entity extraction, citation context mining, and tf-idf scoring. Temporal trends are extracted to show evolving usage patterns of gases and corresponding LXCat databases. Topic modeling reveals thematic trajectories across subfields. This work also highlights the utility of NLP and citation data-analysis in quantifying how open-access platforms like LXCat contribute to shaping research directions and accelerating discovery in the LTP community.

[1] Plasma Process. Polym. (2017) 14, 1600098.

[2] Chem. Phys. (2012) 398, 148–153.

[3] Atoms (2021) 9 (1), 16.