Water self-purification via electron donation effect of emerging contaminants arousing oxygen activation over ordered carbon-enhanced CoFe quantum dots.

The release of emerging contaminants (ECs) into aquatic environments poses a significant risk to global water security. Advanced oxidation processes (AOPs), while effective in removing ECs, are often resource and energy-intensive. Here, we introduce a novel catalyst, CoFe quantum dots embedded in graphene nanowires (CoFeQds@GN-Nws), synthesized through anaerobic polymerization. It uniquely features electron-rich and electron-poor micro-regions on its surface, enabling a self-purification mechanism in wastewater. This is achieved by harnessing the internal energy of wastewater, particularly the bonding energy of pollutants and dissolved oxygen (DO). It demonstrates exceptional efficiency in removing ECs at ambient temperature and pressure without the need for external oxidants, achieving a removal rate of nearly 100.0%. The catalyst's structure-activity relationship reveals that CoFe quantum dots facilitate an unbalanced electron distribution, forming these micro-regions. This leads to a continuous electron-donation effect, where pollutants are effectively cleaved or oxidized. Concurrently, DO is activated into superoxide anions (O2•-), synergistically aiding in pollutant removal. This approach reduces resource and energy demands typically associated with AOPs, marking a sustainable advancement in wastewater treatment technologies.

Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.