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Phase change absorbents based on amine chemical absorption for CO2 capture exhibit energy-saving potential, but generally suffer from difficulties in CO2 regeneration. Alcohol, characterized as a protic reagent with a low dielectric constant, can provide free protons to the rich phase of the absorbent, thereby facilitating CO2 regeneration. In this investigation, N-aminoethylpiperazine (AEP)/sulfolane/H2O was employed as the liquid-liquid phase change absorbent, with alcohol serving as the regulator. First, appropriate ion pair models were constructed to simulate the solvent effect of the CO2 products in different alcohol solutions. The results demonstrated that these ion pair products reached the maximum solvation-free energy (ΔEsolvation) in the rich phase containing ethanol (EtOH). Desorption experiment results validated that the inclusion of EtOH led to a maximum regeneration rate of 0.00763 mol/min, thus confirming EtOH's suitability as the preferred regulator. Quantum chemical calculations and 13C NMR characterization were performed, revealing that the addition of EtOH resulted in the partial conversion of AEP-carbamate (AEPCOO-) into a new product known as ethyl carbonate (C2H5OCOO-), which enhanced the regeneration reactivity. In addition, the decomposition paths of different CO2 products were simulated visually, and every reaction's activation energy (ΔEact) was calculated. Remarkably, the ΔEact for the decomposition of C2H5OCOO- (9.465 kJ/mol) was lower than that of the AEPCOO- (26.163 kJ/mol), implying that CO2 was more likely to be released. Finally, the regeneration energy consumption of the alcohol-regulated absorbent was estimated to be only 1.92 GJ/ton CO2, which had excellent energy-saving potential.

In the quest for effective solutions to address Environ. Pollut. and meet the escalating energy demands, heterojunction photocatalysts have emerged as a captivating and versatile technology. These photocatalysts have garnered significant interest due to their wide-ranging applications, including wastewater treatment, air purification, CO2 capture, and hydrogen generation via water splitting. This technique harnesses the power of semiconductors, which are activated under light illumination, providing the necessary energy for catalytic reactions. With visible light constituting a substantial portion (46%) of the solar spectrum, the development of visible-light-driven semiconductors has become imperative. Heterojunction photocatalysts offer a promising strategy to overcome the limitations associated with activating semiconductors under visible light. In this comprehensive review, we present the recent advancements in the field of photocatalytic degradation of contaminants across diverse media, as well as the remarkable progress made in renewable energy production. Moreover, we delve into the crucial role played by various operating parameters in influencing the photocatalytic performance of heterojunction systems. Finally, we address emerging challenges and propose novel perspectives to provide valuable insights for future advancements in this dynamic research domain. By unraveling the potential of heterojunction photocatalysts, this review contributes to the broader understanding of their applications and paves the way for exciting avenues of exploration and innovation.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

The key to this study is to explore the relation between renewable energy and trade openness under climate change impacts on international trade. This study is based on the analyses of trade openness index and the Notre Dame-Global Adaptation Index in measures of research of the transfer of renewable energy technologies globally, promotion of the growth of renewable energy share, and the country's current vulnerability to climate disruptions. Research is focused on evaluation of low-carbon trade, analysis of renewable heat, and estimation of the impact of renewable energy on trade openness under climate disruptions. Research verified that development of renewable energy can reduce the human impact on climate change, but such technologies are not available to all countries; because of this, different countries may be affected by climate change to varying degrees. The implementation of renewable energy technologies should help countries become more resilient to climate change through the availability of appropriate means of producing renewable energy; however, this is possible subject to the country's openness to the arrival of goods and technologies from outside-increasing trade openness level. Countries with open and competitive markets are more inclusive to adopt and support the renewable energy technologies and their development. From another position, development of trade and deep integration of economy can create external dependence on imported energy sources and block the development of domestic renewable energy sectors. The economic challenges in the field of renewable energy need the focus of governments and policy makers on supporting the stability of renewable generation facilities, increasing the reliability of energy supply, protection from seasonal changes in demand for electricity, support of the energy cooperatives and private consumers of renewable energy, and transition the public transport sector for renewable energy sources. Further research in this direction has prospects, because globalization is a stable trend of recent decades, and allows for an even distribution of efforts to produce various products and provide them to the population, but decarbonization potential of renewable energy production is untapped as fossil fuels dominate in largest world markets.

Global warming and nitrogen (N) deposition have a profound impact on greenhouse gas (GHG) fluxes and consequently, they also affect climate change. However, the global combined effects of warming and N addition on GHG fluxes remain to be fully understood. To address this knowledge gap, a global meta-analysis of 197 datasets was performed to assess the response of GHG fluxes to warming and N addition and their interactions under various climate and experimental conditions. The results indicate that warming significantly increased CO2 emissions, while N addition and the combined warming and N addition treatments had no impact on CO2 emissions. Moreover, both warming and N addition and their interactions exhibited positive effects on N2O emissions. Under the combined warming and N addition treatments, warming was observed to exert a positive main effect on CO2 emissions, while N addition had a positive main effect on N2O emissions. The interactive effects of warming and N addition exhibited antagonistic effects on CO2, N2O, and CH4 emissions, with CH4 uptake dominated by additive effects. Furthermore, we identified biome and climate factors as the two treatments. These findings indicate that both warming and N addition substantially impact soil GHG fluxes and highlight the urgent need to investigate the influence of the combination of warming and N addition on terrestrial carbon and N cycling under ongoing global change.

In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Lake ecosystems are extremely sensitive to nitrogen growth, which leads to water quality degradation and ecosystem health decline. Nitrogen depositions, as one of the main sources of nitrogen in water, are expected to change under future climate change scenarios. However, it remains not clear how nitrogen deposition to lakes respond to future meteorological conditions. In this study, a source-oriented version of Community Multiscale Air Quality (CMAQ) Model was used to estimate nitrogen deposition to 263 lakes in 2013 and under three RCP scenarios (4.5, 6.0 and 8.5) in 2046. Annual total deposition of 58.2 Gg nitrogen was predicted for all lakes, with 23.3 Gg N by wet deposition and 34.9 Gg N by dry deposition. Nitrate and ammonium in aerosol phase are the major forms of wet deposition, while NH3 and HNO3 in gas phase are the major forms of dry deposition. Agriculture emissions contribute to 57% of wet deposition and 44% of dry deposition. Under future meteorological conditions, wet deposition is predicted to increase by 5.5% to 16.4%, while dry deposition would decrease by 0.3% to 13.0%. Changes in wind speed, temperature, relative humidity (RH), and precipitation rates are correlated with dry and wet deposition changes. The predicted changes in deposition to lakes driven by meteorological changes can lead to significant changes in aquatic chemistry and ecosystem functions. Apart from future emission scenarios, different climate scenarios should be considered in future ecosystem health evaluation in response to nitrogen deposition.