Despite the high theoretical capacity and energy density of lithium-sulfur (Li-S) batteries, the development of Li-S batteries has been slow due to the poor electrical conductivity and the shuttle effect of the electrode materials, resulting in low sulfur utilization and fast long-term cycling capacity decay. The modified carbon materials are often used as sulfur hosts to significantly improve the cycling performance of the materials, but also bring high-cost issues. Here, the porous carbon materials are synthesized quickly and conveniently by the microwave cross-linking method using discarded medical masks as carbon sources and concentrated sulfuric acid as solvent. However, poor surface and structural properties limit the application of materials. The porous carbon material is modified with p-toluene disulfide and urea as the sulfur and nitrogen sources by the microwave cross-linking method, which not only improves the porosity and specific surface area of the porous carbon material, but also improved the electrical conductivity and interlayer spacing of the material. As synthesized SN-doped porous carbon is employed as the sulfur host, which exhibits a high discharge capacity (1349.3 mAh g) at 0.1°C, the S-porous C/S, N-porous C/S, and SN-porous C/S can maintain 78.1, 43.9, and 59.5% of the initial capacity after 500 cycles. The results indicate that the doping of S and N atoms provides sufficient active sites for the chemisorbed lithium polysulfides (LiPSs) to improve the reaction kinetics of the materials.

The COVID-19 pandemic has made a significant disruption in the renewable industry, and the effects will last longer. In this context, understanding how and which specific renewable power got affected due to this crisis is of crucial importance. This study examines the nexus between COVID-19 and Sweden's renewable electricity production from three sources of energy such as nuclear, solar, and wind, where the data ranges from January 1, 2019, to February 17, 2021. Since this study compares the period before and during the pandemic event, the study uses Air Quality Index as a measure of COVID-19 induced event and thus study the linkage between air quality and electricity production from three types of renewable energy. To analyse the above issue, several advanced techniques such as Wavelet Power Spectrum, Wavelet Coherence, Partial and Multiple Wavelet Coherence have been applied. The findings from the Wavelet Coherence approach demonstrate that COVID-19 has disrupted the linkage between wind energy generation and air quality, while the disruption in the case of solar and nuclear electricity generation has been minimal. Moreover, solar energy generation and air pollution both negatively affect each other, implying the need to generate solar power as well as reduce the level of air pollution in Sweden. In light of the above findings, the study discusses possible policy actions the country can take to fulfil its renewable development goals.

Coronavirus disease (COVID-19) has spread all across the world. Low- and medium-income countries are more affected economically and socially compared to developed countries due to the lack of a rapid, robust, and affordable testing infrastructure. Furthermore, the high cost of real-time polymerase chain reaction (PCR) system, sophisticated user-handling procedure, and high expense of the conventional clinical tests are the root causes of the less accessibility of the testing systems to the users. In this study, a COVID-19 Point-of-Care (POC) ecosystem model is proposed for the low- and medium-income countries (or energy deprived countries) that will facilitate the technological development with locally available fabrication components. In addition, the nontechnological development phases have also been discussed, which encompasses the collaboration among academia, local as well as government bodies, and entrepreneurial ventures. In addition, a hypothetical design of a microfluidic paper-based analytical (μPADs) POC platform is proposed to detect COVID-19 analyte using unprocessed patient-derived saliva, which is a miniaturized form-factor of conventional real-time polymerase chain reaction (PCR) technique. The device contains four major reaction zones, which are sample zone, buffer zone, loop-mediated isothermal amplification (LAMP) Master Mix zone, Ethylenediamine tetraacetic acid (EDTA) zone, and sensor zone. To obtain quicker test results and easier operation, a handheld image acquisition technique is introduced in this study. It is hypothesized that in a remote setting, the proposed design could be used as an initial guideline to develop a POC COVID-19 testing system, which may be simple, easy-to-use, and cost-effective.

The Coronavirus (COVID-19) outbreak hit the global economy like a tsunami. Every aspect of human society, including the energy industry and market, is affected by this pandemic. The pandemic has affected prices, demand, supply, investment, and several other aspects of the energy sector, including the oil and gas industry. This article is aimed to analyze the impacts of COVID-19 on the oil and gas industry and give a perspective of the post-COVID-19 oil and gas market. Results of this article show that COVID-19 impacts the oil and gas industry. The short-term impact is nearly 25% decrease in petroleum consumption, slowly recovering to its former amount and even growing more. The long-term impacts are the 30% to 40% decrease in the CAPEX and R&D investments over the oil and gas market, which is a regional scale in the United States, caused oil exploitation projects to decrease from more than 800 in 2019 to 265 in 2021. And it is predicted to reduce the competitiveness of oil and gas vs other energy carriers such as ever price-decreasing renewable energies. Thus, the oil and gas industry has to change rapidly before losing a substantial energy market share. Finally, this article discusses acknowledging oil and gas trade as a part of World trade organization (WTO/ECT) regulations. And considering it a general energy commodity. An act that reduces the freedom of action of oil-exporting governments and great oil cartels and protects their interests in a globalizing competitive energy market.

Due to the extraordinary impact of the Coronavirus Disease 2019 (COVID-19) and the resulting lockdown measures, the demand for energy in business and industry has dropped significantly. This change in demand makes it difficult to manage energy generation, especially electricity production and delivery. Thus, reliable models are needed to continue safe, secure, and reliable power. An accurate forecast of electricity demand is essential for making a reliable decision in strategic planning and investments in the future. This study presents the extensive effects of COVID-19 on the electricity sector and aims to predict electricity demand accurately during the lockdown period in Turkey. For this purpose, well-known machine learning algorithms such as Gaussian process regression (GPR), sequential minimal optimization regression (SMOReg), correlated Nyström views (XNV), linear regression (LR), reduced error pruning tree (REPTree), and M5P model tree (M5P) were used. The SMOReg algorithm performed best with the lowest mean absolute percentage error (3.6851%), mean absolute error (21.9590), root mean square error (29.7358), and root relative squared error (36.5556%) values in the test dataset. This study can help policy-makers develop appropriate policies to control the harms of not only the current pandemic crisis but also an unforeseeable crisis.

Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.

As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days-full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models.

Long-term stability and long-term performance of thermal storage media are a key issue that should be thoroughly analysed when developing storage systems. However, no testing protocol or guideline exists up to now for validating storage media, so that authors apply their own criteria, not only for designing testing procedures but also for predicting the material behaviour under long-term operation. This paper aims to cover this gap by proposing a methodology for validating thermal storage media; in particular, phase change materials (PCMs). This methodology consists of different stages that include PCM characterization, preliminary assessment tests, and accelerated life testing. For designing the accelerated life tests, lifetime relationship models have to be obtained in order to predict PCM long-term behaviour under service conditions from shorter tests performed under stress conditions. The approach followed in this methodology will be valid for materials to be used as sensible or thermochemical storage media, too.