Dual-aligned porous electrodes for enhanced hydrogen evolution in alkaline water electrolysis

The efficiency of water electrolysis is significantly affected by the bubbles on the surface and inside the electrode.To enhance the gas-liquid transfer within the porous electrodes,we developed an innovative design termed dual-aligned porous electrode(D-APE),achieved by integrating magnetic alignment with freeze casting techniques.This paper investigates the hydrogen evolution performance of porous electrodes prepared using four different methods:evaporation,magnetic-aligned evaporation,freeze casting,and dual-aligned methods.The findings demonstrate that the magnetic-aligned process effectively alters the electrode structure,resulting in improved hydrogen evolution performance.Notably,among all the examined electrodes,the D-APE exhibits the highest hydrogen evolution performance,with further enhancements observed with prolonged the time of magnetic alignment.Furthermore,a comparison is made between electrodes prepared using the freeze casting method and the dual-aligned method at various thickness.The results show that the thinner D-APE exhibits excellent hydrogen evolution performance at high current density.Moreover,the D-APE demonstrates significantly improved material utilization rates compared to the conventional freeze casting method,offering promising prospects for enhancing the efficiency of water electrolysis.

Neglected Aspects of SARS-CoV-2 Aerosol Transmission in Bathrooms of Multistory and High-Rise Buildings—Beijing Municipality,China,October 2022

Summary What is already known about this topic?There is a toilet flush-soil stack-floor drain pathway of aerosol transmission in multistory and high-rise buildings,but the influencing factors are not completely clear.What is added by this report?The poor airtightness of the connecting parts of the floor drain,as well as pressure fluctuations in the sewage pipe during toilet flushing caused by blockage of the soil stack vent,may lead to the cross-floor transmission of viral aerosols through the soil stack and floor drains.

Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Training samples for deep learning networks are typically obtained through various field experiments,which require significant manpower,resource and time consumption.However,it is possible to utilize simulated data to augment the training samples.In this paper,by comparing the actual experimental model with the simulated model generated by the gprMax[1]forward simulation method,the feasibility of obtaining simulated samples through gprMax simulation is validated.Subsequently,the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples.At the same time,aiming at the detection and intelligent recognition of road sub-surface defects,the Swin-YOLOX algorithm is introduced,and the excellence of the detection network,which is improved by augmenting the simulated samples with real samples,is further verified.By comparing the prediction performance of the object detection models,it is observed that the model trained with mixed samples achieved a recall of 94.74%and a mean average precision(maP)of 97.71%,surpassing the model trained only on real samples by 12.95%and 15.64%,respectively.The feasibility and excellence of training the model with mixed samples are confirmed.The potential of using a fusion of simulated and existing real samples instead of repeatedly acquiring new real samples by field experiment is demonstrated by this study,thereby improving detection efficiency,saving resources,and providing a new approach to the problem of multiple interpretations in ground penetrating radar(GPR)data.