Machine learning-driven dual-objective optimization of biomass-derived carbon quantum dots toward high conductivity and yield
Abstract
) of 323.18 min. Under these parameters, the corresponding optimal CQD yield was 32.02%. Subsequently, we conducted practical anaerobic digestion scenario tests on the optimized CQDs, demonstrating excellent methane productivity, process stability, and acid buffering capacity. These results demonstrate that machine learning can effectively guide the preparation of CQDs with both high conductivity and high yield, and that the prepared CQDs can practically optimize anaerobic digestion application processes.
Fields of Study & Subjects
Carbon and Quantum Dots Applications
Microbial Fuel Cells and Bioremediation
Supercapacitor Materials and Fabrication
Publication details
- Journal / source
- RSC Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 16
- Issue
- 36
- Pages
- 38228–38239
- Publication date
- 1 January 2026
- Language
- EN