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China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Salt-concentrated electrolytes are emerging as promising electrolytes for advanced lithium ion batteries (LIBs) that can offer high energy density and improved cycle life. To further improve these electrolytes, it is essential to understand their inherent behavior at various operating conditions of LIBs. Molecular dynamics (MD) simulations are extensively used to study various properties of electrolytes and explain the associated molecular-level phenomena. In this study, we use classical MD simulations to probe the properties of the concentrated electrolyte solution of 3 mol/kg lithium hexafluorophosphate (LiPF6) salt in the propylene carbonate solvent at various temperatures ranging from 298 to 378 K. Our results reveal that the properties such as ionic diffusivity and molar conductivity of a concentrated electrolyte are more sensitive to temperature compared to that of dilute electrolytes. The residence time analysis shows that temperature affects the Li+ ion solvation shell dynamics significantly. The effect of temperature on the transport and dynamic properties needs to be accounted carefully while designing better thermal management systems for batteries made with concentrated electrolytes to garner the advantages of these electrolytes. [ABSTRACT FROM AUTHOR]

An alga belongs to the multi-pertinent group which can add to a significant sector of environment. They show a prevailing gathering of microorganisms for bioremediation due to their significant capacity to inactivate toxic heavy metals. It can easily absorb or neutralize the toxicity of heavy metals from water and soil through phytoremediation. Biosorption is a promising innovation that focuses on novel, modest, and exceptionally successful materials to apply in phytoremediation technology. Furthermore, algal biomass can be used for biofuel generation after phytoremediation using thermochemical or biological transformation processes. The algal components get affected by heavy metals during phytoremediation, but with the help of different techniques, these are yield efficient. The extreme lipid and mineral substances of microalgae have been proven helpful for biofuel manufacturing and worth extra products. Biofuels produced are bio-oil, biodiesel, bioethanol, biogas, etc. The reuse capability of algae can be utilized toward ecological manageability and economic facility. In this review article, the reuse and recycling of algal biomass for biofuel production have been represented. This novel technique has numerous benefits and produces eco-friendly and economically beneficial products. This article explains how algal biomass left over after phytoremediation can be reused and recycled to produce biofuel. It is a novel technique that is cost-effective, demands less time for biofuel production as it does not include cultivation and harvesting time, and produces products that are economically and environmentally beneficial for society. [ABSTRACT FROM AUTHOR]