Integrating bibliometrics and deep learning to analyze emerging trends and predict future directions in photoelectrochemical water splitting for hydrogen production: A Scopus-database driven study

Abstract

Hydrogen production is gaining significant attention due to its high energy content, zero emissions, and availability of renewable sources. Photoelectrochemical (PEC) water splitting has gained significant attention recently because it utilizes solar energy for green and renewable hydrogen production and combines photoconversion and energy storage in a single system. In this study, a bibliometric assessment based on the Scopus database was carried out on the topic “photoelectrochemical water splitting for hydrogen production”. The progress of photoelectrochemical water splitting for hydrogen production was systematically analyzed based on articles, countries, authors, institutions, journals, and cited references. Additionally, burst words related to photoelectrochemical water splitting published from 1990 to 2024 were analyzed to identify research hotspots. In addition to bibliometric assessment, deep learning assessment is carried out to understand the homogeneity of the investigated dataset and forecast future research trends. This work represents an overview for beginners to understand the progress of research on photoelectrochemical water splitting worldwide and can help predict future developments in photoelectrochemical water splitting for hydrogen production.