Effect of Lubricating Phase on Microstructure and Properties of Cu–Fe Friction Materials

Cu⁻Fe-based friction materials with flake graphite, granulated carbon black, and high-strength graphite as lubricating phase were prepared by the powder metallurgy method. The effects of different types and mass fraction of lubricating phase on the microstructure, mechanical properties, and tribological properties were investigated. The results show that when the mass fraction of granulated carbon black is 5 wt%, it is easy to form a good interface with the matrix, but the interface is prone to pores and cracks when its mass fraction is 10 wt%. The bending strength and compressive strength properties of the composites increased with increasing in the mass fraction of granulated carbon black and reached the maximum of 40 MPa and 70 MPa at 5 wt% granulated carbon black, after which bending strength and compressive strength all decreased. The friction coefficient and the wear loss of the materials initially decreased as the mass fraction of granulated carbon black increased and obtained minimum of 0.436 and 0.145 mm when the mass fraction of granulated carbon black was 5 wt%, then ascended. Compared with the sample with 5 wt% high-strength graphite as lubricating phase, the sample with 5 wt% granulated carbon black as lubricating phase had better sintering performance, mechanical properties, and tribological properties

A facile and versatile partitioned cooperative self-assembly process to prepare SBA-15s with larger mesopores, high microporosity and tunable particle sizes

A facile and versatile synthesis method, called the partitioned cooperative self-assembly process (PCSA process), is developed to prepare mesoporous SBA-15 silicas using P123 surfactant as template and cheap sodium silicate (SS) as silica precursor. It is demonstrated for the first time that by simply partitioning the cooperative self-assembly process, larger mesopore size (∼10 nm) can be achieved without using any additives or special synthetic conditions; high microporosities (>0.15 cm3 g-1) unprecendentedly persist upon hydrothermal treatment at 120 °C for 20 h with comparable mesopore sizes, which are in big contrast with those prepared by conventional method. The PHTS-type SBA-15s can also be favorably prepared by the PCSA process. In addition, the sizes of flower-like SBA-15 particle can be facilely tuned by adjusting the addition combinations of SS and interval time between the 1st and 2nd additions in the PCSA process. Interestingly, based on TEOS, the PCSA process yielded bimodal mesostructured SBA-15 with different wall thicknesses but the same mesopore size. The versatile PCSA process, which has shown its potential in tailoring the textural and morphological properties of SBA-15s, could lead to even wider spectrum of SBA-15s with various mesostructures if coupled with other synthetic measures

Fabrication of SiC Porous Ceramics by Foaming Method

In this work, hierarchically porous SiC ceramics were prepared via the foaming method. Porous ceramics with tunable, uniform, and bimodal pore structures were successfully fabricated in a facile way. The formation mechanisms of the 1st and 2nd modal macropores are the H2O2 foaming process and SiC particle overlap, respectively. The effect of pore-foaming agent amount, foaming temperature, and surfactant was investigated. According to the results, with increasing H2O2 amount, the porosity, pore size, and interconnectivity of the 1st modal pores increased, whereas bulk density and strength decreased. The porosity increased while the strength decreased as the foaming temperature increased. Surfactants increased pore interconnectivity and porosity. When the foaming temperature was 85 °C, and the addition of H2O2 was 5 wt.%, the porosity, bulk density, flexural strength, and compressive strength were 56.32%, 2.8301 g/cm3, 11.94 MPa, and 24.32 MPa, respectively. Moreover, SiC porous ceramics exhibited excellent corrosion resistance to acids and alkalis

Fabrication of SiC Porous Ceramics by Foaming Method

In this work, hierarchically porous SiC ceramics were prepared via the foaming method. Porous ceramics with tunable, uniform, and bimodal pore structures were successfully fabricated in a facile way. The formation mechanisms of the 1st and 2nd modal macropores are the H2O2 foaming process and SiC particle overlap, respectively. The effect of pore-foaming agent amount, foaming temperature, and surfactant was investigated. According to the results, with increasing H2O2 amount, the porosity, pore size, and interconnectivity of the 1st modal pores increased, whereas bulk density and strength decreased. The porosity increased while the strength decreased as the foaming temperature increased. Surfactants increased pore interconnectivity and porosity. When the foaming temperature was 85 °C, and the addition of H2O2 was 5 wt.%, the porosity, bulk density, flexural strength, and compressive strength were 56.32%, 2.8301 g/cm3, 11.94 MPa, and 24.32 MPa, respectively. Moreover, SiC porous ceramics exhibited excellent corrosion resistance to acids and alkalis

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies

Libra: In-network Gradient Aggregation for Speeding up Distributed Sparse Deep Training

Distributed sparse deep learning has been widely used in many internet-scale applications. Network communication is one of the major hurdles for the training performance. In-network gradient aggregation on programmable switches is a promising solution to speed up the performance. Nevertheless,existing in-network aggregation solutions are designed for the distributed dense deep training, and fall short when used for the sparse deep training.To address this gap, we present Libra based on our key observation of the extremely biased update frequency of parameters in distributed deep sparse training. Specifically, Libra offloads only the aggregation for "hot" parameters that are updated frequently onto programmable switches. To enable this offloading and achieve high aggregation throughput, we propose solutions to address the challenges related to hot parameter identification, parameter orchestration, floating-point summation on switches as well as system reliability. We implemented Libra on Intel Tofino switches and integrated it with PS-lite. Finally, we evaluate Libra's performance through extensive experiments and show that Libra can speed up the gradient aggregation by 1.5~4 times.Comment: 14 pages, 18 figure