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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.

Life in soil is a key driver of important ecosystem processes, such as the recycling of carbon and nutrients. In current intensive agricultural soils, however, richness and abundance of many groups of soil organisms are often reduced, which may threaten soil health and sustainable agriculture in the long run. Therefore, a switch to alternative agricultural practices (e.g., minimal tillage) that are less detrimental or even stimulate soil life has been suggested as a way to increase sustainable food production. Although we understand how some of these practices impact specific species or functional groups in soils, it is necessary to get a more complete overview to understand which practices can be used in agriculture to improve soil biodiversity. Here, we present a systematic literature review identifying which practices are studied as alternatives to current, intensive practices for four soil taxonomic groups encompassing a range of trophic groups and functions in the soil ecosystem: nematodes, earthworms, bacteria and fungi. Further, we review how these alternative practices impact the abundance and diversity of these four taxonomic groups, as well as for the 14 functional groups identified and retrieved from the review. We found that a total of 23 alternative agricultural practices, grouped into 10 groups of practices, were studied for the four target taxonomic groups. Three groups of practices, 'fertilization’, ‘soil cover’ and ‘tillage’ were studied for all taxa. In general, alternative agricultural practices had positive impacts on the species richness in the four taxonomic groups and on the abundance of organisms in the functional groups. However, there were some exceptions. For example, organic fertilizers reduced the abundance of epigeic earthworms, while enhancing the abundance of endogeic and anecic earthworms. There was only one alternative practice, i.e., the use of cover crops, that was neutral to positive for the abundance of all functional groups across all taxa. Our review revealed that there are gaps in the literature, as practices that are commonly studied for aboveground biodiversity, such as field margins or flower strips, are not studied well across taxonomic and functional groups and need to be further studied to improve our understanding of the impact of alternative practices on soil life. We conclude that alternative agricultural practices are promising to enhance soil biodiversity. However, as some practices have specific impacts on taxonomic groups in the soil, we may require careful application and combinations of alternative agricultural practices to stimulate multiple groups.

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

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.