Femtosecond laser micro-nano processing for boosting bubble releasing of gas evolution reactions

Coupling effect of chemical composition and physical structure is a key factor to construct superaerophobic electrodes.Almost all reports about superaerophobic electrodes were aimed at precisely controlling morphology of loaded materials(constructing specific structure)and ignored the due role of substrate.Nevertheless,in this work,by using high precision and controllable femtosecond laser,hierarchical micro-nano structures with superaerophobic properties were constructed on the surface of silicon substrate(fs-Si),and such special super-wettability could be successfully inherited to subsequent self-supporting electrodes through chemical synthesis.Femtosecond laser processing endowed electrodes with high electrochemical surface area,strong physical structure,and remarkable superaerophobic efficacy.As an unconventional processing method,the reconstructed morphology of substrate surface bears the responsibility of superaerophobicity,thus liberating the structural constraints on loaded materials.Since this key of coupling effect is transferred from the loaded materials to substrate,we provided a new general scheme for synthesizing superaerophobic electrodes.The successful introduction of femtosecond laser will open a new idea to synthesize self-supporting electrodes for gas-involving reactions.

Re-examining urban region and inferring regional function based on spatial-temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government,then reshaped by human activities and residents’needs.Understanding the changes of regional structure and dynamics of city function based on the residents’movement demand are important to evaluate and adjust the planning and management of urban services and internal structures.This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition,for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes.First,a four-dimensional tensor of time(T)×week(W)×origin(O)×destination(D)was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern.Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor.Finally,we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region.Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.

Mining hourly population dynamics by activity type based on decomposition of sequential snapshot data

The dynamic population distributions by activity type(e.g.working,shopping or in-home)are vital for resource allocation,urban planning and epidemic containment.Although studies have incorporated individual-level human mobility data to map population distribution by activity type,access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity knowledge.This paper presents a method for generating the population dynamics by activity type.We first introduce more readily available sequential snapshot data to construct the population mixture model,then decompose the population mixture,and finally estimate the dynamic population size for each activity.We test the method in the central districts of Guangzhou city,China,based on real-time Tencent user density data.Correlation analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working,stay-at-home,and socializing activities.The temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial patterns.We also find that there is an underlying relationship between a region’s function and its dynamic population structure.The presented method has great potential for application and could provide new insight for studying urban dynamic functions.

Reopen schools safely:simulating COVID-19 transmission on campus with a contact network agent-based model

As the COVID-19 vaccination has been quickly rolling out around the globe,the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators,decision-makers,and the general public.Within this context,we develop a contact network agent-based model(CN-ABM)to simulate on-campus disease transmission scenarios.The CN-ABM establishes contact networks for agents based on their daily activity patterns,evaluates the agents’health status change in different activity environments,and then simulates the epidemic curve.By applying the model to a real-world campus environment,we identify how different community risk levels,teaching modalities,and vaccination rates would shape the epidemic curve.The results show that without vaccination,retaining under 50%of on-campus students can largely flatten the curve,and having 25%on-campus students can achieve the best result(peak value<1%).With vaccination,having a maximum of 75%on-campus students and at least a 45%vaccination rate can suppress the curve,and a 65%vaccination rate can achieve the best result.The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools.