Dopamine-driven coloration technologies: a knowledge structuring built on Citavi and artificial intelligence

The interdisciplinary field of dopamine-mediated coloration, spanning materials science, biomimetics, and sustainable chemistry, has experienced rapid growth, making comprehensive literature synthesis increasingly challenging. This paper presents a novel methodology for transforming the reference management software Citavi into a structured knowledge system, enabling both manual and AI-augmented trend analysis. We curated and annotated over 1,000 publications, with particular focus on dopamine and polydopamine in textile functionalization and coloration. Structuring via custom taxonomies (e.g., substrate type, deposition method, durability metrics) enabled AI-driven synthesis using Qwen3-Max. Key insights include: (1) PDA’s role as a universal textile modifier enhancing dye fastness, enabling structural color, and imparting antimicrobial/UV functions; (2) enzymatic (laccase-mediated) DA polymerization as an emerging green alternative; (3) under-explored potential in wool and scalability studies. AI insights were reintegrated into Citavi as “AI Insight Notes,” creating a dynamic human-AI collaborative environment. This approach offers a reproducible framework for domain mapping in fast-evolving fields.