Activity and Selectivity of Bimetallic Catalysts Based on SBA-15 for Nitrate Reduction in Water

Nitrate from the application of nitrogen-based fertilizers in intensive agriculture is a notorious waste product, though it lacks cost-effective solutions for its removal from potential drinking water resources. Catalytic reduction appears to be a promising technique for converting nitrates to benign nitrogen gas. Mesoporous silica SBA-15 is a frequently used catalyst support that has large surface areas and highly ordered nanopores. In this work, mesoporous silica SBA-15 bimetallic catalysts for nitrate reduction were investigated. The catalyst was optimized for the selection of promoter metal (Sn and Cu), noble metal (Pd and Pt) and loading ratios of these metals at different temperatures and reduction conditions. The catalysts prepared were characterized by FT-IR, N2 physisorption, XRD, SEM, and ICP. All catalysts showed the presence of cylindrical mesoporous channels and uniform pore structures that remained even after metals loading. In the presence of a CO2 buffer, the catalysts 4Pd-1Cu/SBA-15 and 1Pt-1Cu/SBA-15 reduced at 100?C under H2 and 1Pd-1Cu/SBA-15 reduced at 200°C under H2 demonstrated very high nitrate conversion. Furthermore, the forementioned Pd catalysts had higher N2 selectivity (88% - 87%) compared to Pt catalyst (80%). Nitrate conversion by the 4Pd-1Cu/SBA-15 catalyst was significantly decreased to 81% in the absence of CO2.

Fighting against COVID-19: Who Failed and Who Succeeded?

Recently, governments and public authorities in most countries had to face the outbreak of COVID-19 by adopting a set of policies. Consequently, some countries have succeeded in minimizing the number of confirmed cases while the outbreak in other countries has led to their healthcare systems breakdown. In this work, we introduce an efficient framework called COMAP (COrona MAP), aiming to study and predict the behavior of COVID-19 based on deep learning techniques. COMAP consists of two stages: clustering and prediction. The first stage proposes a new algorithm called Co-means, allowing to group countries having similar behavior of COVID-19 into clusters. The second stage predicts the outbreak’s growth by introducing two adopted versions of LSTM and Prophet applied at country and continent scales. The simulations conducted on the data collected by WHO demonstrated the efficiency of COMAP in terms of returning accurate clustering and predictions.

MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.