Strategies for Integrating Green Chemistry Principles into Data-Driven Synthesis of Functional Materials

Naufal Kurniawan; Meutia Firmansyah; Arif Setiawan

Authors

Naufal Kurniawan Institut Teknologi Sepuluh Nopember Author
Meutia Firmansyah Institut Teknologi Sepuluh Nopember Author
Arif Setiawan Universitas Indonesia Author

Keywords:

Green chemistry, Data-driven synthesis, Functional materials, Materials informatics, Sustainable laboratory practice

Abstract

This study aims to explore strategies for integrating green chemistry principles into the data-driven synthesis of functional materials, focusing on how researchers understand, negotiate, and apply sustainability considerations in laboratory practice. The study used a qualitative exploratory case study design involving researchers, lecturers, postgraduate students, laboratory analysts, and research managers in functional materials laboratories. Data were collected through semi-structured interviews, non-participant observation, and documentation of laboratory procedures, synthesis records, safety documents, and research reports. The data were analyzed using reflective thematic analysis to identify patterns of meaning related to green chemistry integration, data use, synthesis optimization, and laboratory decision-making. The findings reveal six main themes: the practical embedding of green chemistry in laboratory routines, the role of data-driven methods in reducing experimental uncertainty, the tension between material performance and environmental responsibility, the limited integration of sustainability indicators into predictive models, the influence of laboratory culture and institutional support, and the importance of documenting failed experiments for greener synthesis pathways. This study contributes to the literature by positioning data-driven synthesis as a socio-technical process that requires technical, institutional, and educational integration. The findings imply that laboratories need to connect performance data with safety, waste, solvent, and energy indicators to support more sustainable material discovery. Future research should develop measurable green chemistry indicators for machine learning models and compare implementation practices across different institutional contexts.

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