Construction of Ag/CdZnS QDs nanocomposite for enhanced visible light photoinactivation of Staphylococcus aureus

With increasing use of antibiotics, the development of antibiotic-resistant pathogens poses a serious threat to human health and the environment. Photocatalytic inactivation of these harmful pathogens is one of the novel and non-antibiotic treatments. The study fabricated Ag NPs decorated CdZnS QDs via a facile and biological co-precipitation method using L. camara plant extract as a green alternative to treat the toxic chemicals. The fabricated Ag/CdZnS QDs (NCs) were prepared for the efficient treatment of antibiotic-resistant pathogens as they raise a major global concern. The fabricated NCs were characterized with various characterization techniques to verify its physicochemical properties. The fabricated NCs have shown excellent photo-sterilization performance of 97 % against S. aureus. The excellent activity was attributed to the decoration of Ag NPs on CdZnS QDs as it helped in shortening band gap, improved visible light absorption ability, increased active sites, and boosted photogenerated electron/hole pairs stability. Radical trapping experiment and ESR analysis indicated the involvement of •OH and h+ in the photoinactivation of bacteria. The photo sterilization reaction of NCs was carried out under different environmental conditions, including light and dark conditions and different pH conditions. The experiment was carried out in sewage-treated water in order to test the real-time application, and the fabricated NCs achieved excellent 95.9 % photo-inactivation of S. aureus cells in sewage treated water and the Chemical Oxygen Demand (COD) of the system was increased after photo inactivation treatment. The fabricated NCs have also shown excellent reusable efficiency of 95% after six runs and the photostability and anti-corrosive nature of NCs were confirmed. The study provides an insight for the employment of photocatalysis for the sterilization of pathogens in real time aquatic environment across the globe.

Endogenous brain-machine interface based on the correlation of EEG maps

In this paper, a non-invasive endogenous brain-machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life.