Optimization of cobalt-based MOFs for super-capacitor electrode materials of new energy vehicle.

Super-capacitors (SCs), as new energy conversion storage elements, have attracted much attention, but there is still a research gap in the design of electrode materials. In this study, the optimization scheme of Metal-Organic Frameworks (MOFs) and cobalt-based MOF composites as electrode materials for SCs in new energy vehicles is explored, and a series of experiments are conducted to evaluate their performance. Scanning Electron Microscope (SEM) images reveal that the cobalt-based MOF composites have a surface morphology of particles with uniform distribution. The electrochemical performance test results show that the specific capacitance of the cobalt-based MOF composites is much higher than the sum of the two individual electrode materials and presents a remarkable increase with the scanning rate. Additionally, in the constant current charge-discharge test, cobalt-based MOF composites exhibit the longest charge-discharge time and good symmetry. Electrolyte particle contact tests for samples at different preparation temperatures display that high-temperature samples have better structural stability and electrolyte ion contact. In Cyclic Voltammetry (CV) and Galvanostatic Charge Discharge (GCD) tests, the 250 °C sample demonstrates the best electrochemical properties and the highest specific capacitance (269 F/g). Moreover, as the current density increases, the specific capacitance of the 600 °C sample decreases at a lower rate, showing stronger stability. However, the use of cobalt-based MOF materials may pose environmental and safety risks, such as the environmental impact of cobalt resource mining, instability under high-temperature conditions, and the possible production of hazardous substances. Therefore, these factors need to be fully considered when designing and using SCs to ensure the environmental friendliness and safety of cobalt-based MOFs. These results provide an important reference for selecting and optimizing electrode materials for SCs in new energy vehicles. Furthermore, this study offers research suggestions for improving new energy materials, filling the research gaps in related fields, and supporting the further development of SC technology.

Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19).

As COVID-19 swept over the world, people discussed facts, expressed opinions, and shared sentiments about the pandemic on social media. Since policies such as travel restriction and lockdown in reaction to COVID-19 were made at different levels of the society (e.g., schools and employers) and the government, we build a large geo-tagged Twitter dataset titled UsaGeoCov19 and perform an exploratory analysis by geographic location. Specifically, we collect 650,563 unique geo-tagged tweets across the United States covering the date range from January 25 to May 10, 2020. Tweet locations enable us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspire us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights. UsaGeoCov19 link:http://yunhefeng.me/geo-tagged_twitter_datasets/.