A scientometric analysis and unsupervised topic modeling of triboelectric nanogenerator research

Triboelectric nanogenerators (TENGs) have emerged as a promising technology for energy harvesting and self-powered sensing applications. As TENG research is expanding rapidly, it is essential to systematically map its evolution to understand emerging trends, thematic developments, and collaborative patterns. We conducted a comprehensive analysis of the TENG research landscape using bibliometric techniques and unsupervised latent Dirichlet Allocation topic modeling approaches from the 8,556 scientific documents indexed in Scopus from 1999 to 2024. We observed the upward growth of publications and citations over time, with a notable increase after 2018. Frequently used keywords included triboelectric nanogenerators, energy harvesting, and self-powered sensors. Polydimethylsiloxane emerged as the widely used material, followed by paper, cellulose, and graphene. Sensor technologies dominated applications, followed by energy harvesting and wearable electronics. Topic modeling revealed six themes: wearable electronics, sensing technologies, low-power energy-harvesting systems, TENG output enhancement, material innovation, and highly gravitated research about the large-scale power generation. The results show a shift from low-power devices to large-scale energy production and a greater focus on biomedical and health applications. These approaches and findings offer a reference for future researchers and support policymakers and stakeholders in understanding the evolving TENG research.