Machine learning optimization of Cr(VI) bioreduction using novel strain CR9 immobilized on compost-derived humic acid gel microspheres

Key Points

• Bioreduction of chromium by strain CR9 leads to significant transformation efficiency.
• Using machine learning, optimization improved reduction rates by over 40% under specific conditions.
• Assessment using compost-derived humic acid gel microspheres enhanced the bioreduction process.
• This approach suggests potential for effective bioremediation strategies in environmental applications.