Machine learning-assisted screening of copper-cerium bimetallic catalysts for tetracycline degradation

Key Points

• This research aims to identify efficient bimetallic catalysts for the degradation of tetracycline, a persistent organic pollutant.
• Employing machine learning algorithms to screen various copper-cerium bimetallic compositions.
• Assessing their catalytic activity and stability for effective pollutant removal.
• Identified optimal catalyst compositions that significantly enhance the degradation rates of tetracycline.
• Demonstrated strong stability and cost-effectiveness in comparison to conventional methods.