Frontiers in Energy最新文献

In this study, the Lewis doping approach of polyaniline (PANI) was employed to fabricate cobait–nitrogen–carbon (Co-N-C) oxygen electrocatalysts for Zn–air batteries, aiming to enhance the active spots of Co-N-C. This resulting Co-N-C catalysts exhibited well-defined nanofiber networks, and the Brunauer-Emmett-Teller (BET) analysis confirmed their substantial specific surface area. Electrochemical experiments demonstrated that the Co-N-C catalysts achieved the half-wave potential (vs. RHE) of 0.85 V in alkaline medium, overcoming Pt/C and iron–nitrogen–carbon (Fe-N-C) counterparts in extended cycle testing with only a 25 mV change in a half-wave potential after 5000 cycles. Remarkably, the highest power density measured in the zinc (Zn)–air battery reached 227 mW/cm², a significant improvement over the performance of 101 mW/cm² of the platinum on activated carbon (Pt/C) catalyst. These findings highlight the advantageous stability enhancement associated with the utilization of Co in the Co-N-C catalysts.

Solar-driven hydrogen production from seawater attracts great interest for its emerging role in decarbonizing global energy consumption. Given the complexity of natural seawater content, photocatalytic vapor splitting offers a low-cost and safe solution, but with a very low solar-to-hydrogen conversion efficiency. With a focus on cutting-edge photothermal–photocatalytic device design and system integration, the recent research advances on vapor splitting from seawater, as well as industrial implementations in the past decades were reviewed. In addition, the design strategies of the key processes were reviewed, including vapor temperature and pressure control during solar thermal vapor generation from seawater, capillary-fed vaporization with salt repellent, and direct photocatalytic vapor splitting for hydrogen production. Moreover, the existing laboratory-scale and industrial-scale systems, and the integration principles and remaining challenges in the future seawater-to-hydrogen technology were discussed.

This paper proposes a new power generating system that combines wind power (WP), photovoltaic (PV), trough concentrating solar power (CSP) with a supercritical carbon dioxide (S-CO₂) Brayton power cycle, a thermal energy storage (TES), and an electric heater (EH) subsystem. The wind power/photovoltaic/concentrating solar power (WP–PV–CSP) with the S-CO₂ Brayton cycle system is powered by renewable energy. Then, it constructs a bi-level capacity-operation collaborative optimization model and proposes a non-dominated sorting genetic algorithm-II (NSGA-II) nested linear programming (LP) algorithm to solve this optimization problem, aiming to obtain a set of optimal capacity configurations that balance carbon emissions, economics, and operation scheduling. Afterwards, using Zhangbei area, a place in China which has significant wind and solar energy resources as a practical application case, it utilizes a bi-level optimization model to improve the capacity and annual load scheduling of the system. Finally, it establishes three reference systems to compare the annual operating characteristics of the WP–PV–CSP (S-CO2) system, highlighting the benefits of adopting the S-CO₂ Brayton cycle and equipping the system with EH. After capacity-operation collaborative optimization, the levelized cost of energy (LCOE) and carbon emissions of the WP–PV–CSP (S-CO₂) system are decreased by 3.43% and 92.13%, respectively, compared to the reference system without optimization.

As a clean and efficient renewable energy source, solar energy has been rapidly applied worldwide. The growth rate of China’s installed capacity ranks first in the world. However, the life span of photovoltaic (PV) modules is 25 to 30 years, and the rapid development of installed capacity indicates that a large number of PV modules will be decommissioned in the future. Therefore, the ongoing treatment of the scrapped PV waste cells in the near future requires urgent plans and countermeasures. Proper recycling and disposal of decommissioned PV modules is a practical requirement for the sustainable development of the country and industry. Crystalline silicon (c-Si) solar cells currently occupy 85%–90% of the market share, and some scholars have begun to seek the utilization pathways of the waste Si in and outside the PV industry. In this paper, the research status of the separation and recycling process of crystalline Si PV modules is reviewed, and the recycling ways of crystalline silicon are particularly focused on. In addition, the current bottlenecks in the PV recycling industry in China are analyzed and some suggestions on the sustainable development of the PV industry are proposed.

In recent years, the rapid development of portable/wearable electronics has created an urgent need for the development of flexible energy storage devices. Flexible lithium-ion batteries (FLIBs) have emerged as the most attractive and versatile flexible electronic storage devices available. Carbon nanotubes (CNTs) are hollow-structured tubular nanomaterials with high electrical conductivity, large specific surface area, and excellent mechanical properties. Graphene (G) is to some extent comparable to CNTs, because both have unlimited value in flexible electrodes. Herein, a systematic summary of the application of CNT and G in FLIBs electrodes is presented, including different functional applications and services at different temperatures. Furthermore, the effects of electrode structures, including powder, wire-shaped, and film-shaped structures, on electrochemical properties is highlighted. The assembly structures of the FLIBs consisting of CNT and G-based flexible electrodes to realize different functions, including bendability, stretchability, foldability, self-healing, and self-detecting, are systematically reviewed. The current challenges and development prospects of flexible CNT and G-based flexible electrodes and corresponding FLIBs are discussed.

To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

For a climate-neutral future mobility, the so-called e-fuels can play an essential part. Especially, oxygenated e-fuels containing oxygen in their chemical formula have the additional potential to burn with significantly lower soot levels. In particular, polyoxymethylene dimethyl ethers or oxymethylene ethers (PODEs or OMEs) do not contain carbon-carbon bonds, prohibiting the production of soot precursors like acetylene (C₂H₂). These properties make OMEs a highly interesting candidate for future climate-neutral compression-ignition engines. However, to fully leverage their potential, the auto-ignition process, flame propagation, and mixing regimes of the combustion need to be understood. To achieve this, efficient oxidation mechanisms suitable for computational fluid dynamics (CFD) calculations must be developed and validated. The present work aims to highlight the improvements made by developing an adapted oxidation mechanism for OME₁₋₆ and introducing it into a validated spray combustion CFD model for OMEs. The simulations were conducted for single- and multi-injection patterns, changing ambient temperatures, and oxygen contents. The results were validated against high-pressure and high-temperature constant-pressure chamber experiments. OH*-chemiluminescence measurements accomplished the characterization of the auto-ignition process. Both experiments and simulations were conducted for two different injectors. Significant improvements concerning the prediction of the ignition delay time were accomplished while also retaining an excellent agreement for the flame lift-off length. The spatial zones of high-temperature reaction activity were also affected by the adaption of the reaction kinetics. They showed a greater tendency to form OH* radicals within the center of the spray in accordance with the experiments.

In modern alkaline batteries, the zinc anode is the performance-limiting and lifetime-limiting electrode, making the choice of zinc powder critical. Due to the various material fabrication processes that are used to manufacture industrial zinc powder, there exists a wide array of possible zinc particle shapes, sizes, and crystallinities. These industrial zinc powders are typically conceived, produced, and tested through trial-and-error processes using historical “rules of thumb.” However, a data-driven approach could more effectively elucidate the optimum combination of zinc particle properties. In this paper, the effect of Zn particle size, shape, and crystallinity on the achievable capacity and corrosion current is investigated. The Zn types are tested in both powder and slurry form. Following the data collection, a factorial-based statistical analysis is performed to determine the most statistically significant variables affecting capacity and corrosion. This information is then used to down-select to a subset of particles that are tested in cylindrical cells with an AA-equivalent geometry. The reported technique can be used to develop actionable principles for battery manufacturers to create cells that are more stable, longer lasting, and have higher energy densities.

Co-processing of biomass feedstock with petroleum feedstock in existing refineries is a promising technology that enables the production of low-carbon fuels, reduces dependence on petroleum feedstock, and utilizes the existing infrastructure in refinery. Much effort has been dedicated to advancing co-processing technologies. Though significant progress has been made, the development of co-processing is still hindered by numerous challenges. Therefore, it is important to systematically summarize up-to-date research activities on co-processing process for the further development of co-processing technologies. This paper provides a review of the latest research activities on coprocessing biomass feedstock with petroleum feedstock utilizing fluid catalytic cracking (FCC) or hydrotreating (HDT) processes. In addition, it extensively discusses the influence of different types and diverse physicochemical properties of biomass feedstock on the processing of petroleum feedstock, catalysts employed in co-processing studies, and relevant projects. Moreover, it summarizes and discusses co-processing projects in pilot or larger scale. Furthermore, it briefly prospects the research trend of co-processing in the end.

Due to the depletion of traditional fossil fuels and the aggravation of related environmental problems, hydrogen energy is gaining more attention all over the world. Solid oxide fuel cell (SOFC) is a promising power generation technology operating on hydrogen with a high efficiency. To further boost the power output of a single cell and thus a single stack, increasing the cell area is an effective route. However, it was recently found that further increasing the effective area of an SOFC single cell with a flat-tubular structure and symmetric double-sided cathodes would result in a lower areal performance. In this work, a multi-physical model is built to study the effect of the effective area on the cell performance. The distribution of different physical fields is systematically analyzed. Optimization of the cell performance is also pursued by systematically tuning the cell operating condition and the current collection setup. An improvement of 42% is revealed by modifying the inlet gas flow rates and by enhancing the current collection. In the future, optimization of cell geometry will be performed to improve the homogeneity of different physical fields and thus to improve the stability of the cell.