Developing a Multiband Electronic Band Structure Model and Predictive Maps for Bismuth-Rich Mg3(Sb1-xBix)2 Thermoelectric Materials.

In recent years, bismuth-rich Mg3(Sb1-xBix)2 (x = 0.5-0.8) compositions have generated significant interest due to their excellent thermoelectric (TE) performance near room temperature, making them potential applicants for recovery of low-grade waste heat. The superior performance in these materials is due to its complex electronic band structure (EBS) with presence of multiple near degenerate bands close to the conduction band edge. The position and curvature of these bands strongly depend on the alloy composition, doping amount as well as temperature. Thus, identifying optimal material compositions to get the best TE performance depends on an understanding of the temperature dynamics of EBS and forms the objective of this work. Mg3Sb0.6Bi1.4 (x = 0.7) is chosen for this study due to its reported high near room temperature performance, and compositions with varying doping concentrations (Te used as dopant) have been synthesized. EBS parameters like effective mass and deformation potential of bands, interband separation and band gap values have been estimated using a recently developed refinement approach. Refinement results indicate that the interband separation between conduction bands to be a function of both temperature and doping concentration. Further, thermal conductivity (κ) was estimated for all of the compositions. Utilizing the EBS and κ information, predictive 3D maps indicating the variation in zT (TE figure of merit) with doping concentration and temperature have been generated. The 3D maps reveal an interesting surface topography with a broad peak zT region. This observation explains why these materials have high TE performance and are less sensitive to doping inhomogeneities. Our results provide detailed EBS information and fundamental insights on the TE properties of Mg3Sb0.6Bi1.4. Further, the proposed technique can be utilized to probe other Mg3(Sb1-xBix)2 compositions and TE materials.

COVID-19: Assessment of knowledge and awareness in Indian society.

COVID-19, which was initiated regionally at Wuhan of China, has become a global pandemic by infecting people of almost all the world. Human civilizations are facing threat for their survival and livelihood. No country are getting any substantial relief and solution from this pandemic rather to convince their citizens to make aware and taking precaution by changing their living style. In view of this, this study attempted to assess the awareness, threat, symptoms and its prevention among people of India about the COVID-19. A total of 522 responses from all over India were received. The respondents have adequate awareness for COVID-19 outbreak and its preventive measures, out of total, 98% (513) answered that the virus spreads from one person to another, 95% (494) answered that the disease is caused by a virus. Peoples understand the importance of social distancing and other preventive measures prescribed by the government with good attitude for coronavirus. Peoples are following trusted sources for corona information, having confidence to defeat disease but showed their concern for corona threat, are aware about the virus, its common symptoms and prevention, govt. testing and medical facilities. Principal component analysis was used to identify the latent dimensions regarding people's preventive measures and was found that they are majorly adopting three methods, that is, lockdown, naturopathy and social distancing. This study will help government and peoples to understand and handle this coronavirus pandemic effectively and in prevention of COVID-19, which is crucial for the awareness of society in coming time.

Correlation between weather and COVID-19 pandemic in India: An empirical investigation.

This study is an attempt to find and analyze the correlation between Covid-19 pandemic and weather conditions in Indian context. Secondary data analysis of surveillance data of COVID-19 is taken from Wikipedia (updating information from World Health Organization) & statista.com and weather data through Power Data Access Viewer (DAV) (power.Iarc.nasa.gov) from NASA after mentioning latitude and longitude of India. The minimum temperature (°C) at 2 metre, maximum temperature (°C) at 2 metre, temperature (°C) at 2 metre and relative humidity (%) are taken as component of weather. To find the association, Spearman's rank correlation test was applied. The minimum, maximum temperature (°C) at 2 m, temperatures (°C) at 2 m and humidity at 2 m are significantly correlated with COVID-19 pandemic cases (r = 0.93, 0.94, 0.83, and 0.30) at 99% two-tailed significance level. The findings serve as an initial evidence to reduce the incidence rate of COVID-19 in India and useful in policy making.