Two Decades of Progress in Numerical Simulation for Nanoparticle Preparation: A Bibliometric Analysis [2004-2024]

Introduction:: This study aims to map the evolution and global collaboration patterns of numerical simulation for nanoparticle (NP) preparation between 2004 and 2024. It also seeks to address gaps in our understanding of the interdisciplinary synergies between computational modeling and NP engineering. Methods: Leveraging data from the Web of Science, we utilized Bibliometrix, CiteSpace, and VOSviewer to construct co-citation networks, detect keyword bursts, and perform temporal clustering. CiteSpace’s advanced tools, such as timeline visualization and betweenness centrality, enabled us to identify emerging research frontiers. Results: Annual publications grew exponentially (31-fold from 2004 to 2022), with China leading global contributions (35.19%). Four dominant research clusters were identified: molecular dynamics for self-assembly; surface-enhanced Raman spectroscopy (SERS)-based characterization; computational fluid dynamics; and AI-driven synthesis optimization. Notably, “performance” (burst strength: 12.59) and green synthesis represent the shift towards AI-driven synthesis and sustainable methodologies. Discussion: The exponential growth reflects the synergies between computational modeling and nanotechnology engineering. The integration of AI and policy investments has accelerated these synergies. However, challenges in model reliability and cross-scale integration necessitate the use of AI-enhanced multiphysics frameworks. Conclusion: This study provides a strategic framework for policymakers and researchers to prioritize funding, foster cross-institutional collaboration, and integrate artificial intelligence with traditional simulation. The findings highlight the transformative role of numerical modeling in advancing precision nanomedicine, offering actionable insights for industrial process optimization.