Frontiers in Energy最新文献

Rechargeable aqueous metal-ion batteries are promising alternative energy storage devices in the post-lithium-ion era due to their inherent safety and environmental compatibility. Among them, aqueous zinc ion batteries (AZIBs) stand out as next-generation energy storage systems, offering low cost, high safety, and eco-friendliness. Nevertheless, the instability of Zn metal anodes, manifested as Zn dendrite growth, interfacial side reactions, and hydrogen (H₂) evolution, remains a major obstacle to commercialization. To address these challenges, extensive research has been conducted to understand and mitigate these issues. This review comprehensively summarizes recent advances in Zn anode stabilization strategies, including artificial solid electrolyte interphase (SEI) layers, structural optimization, electrolyte modification, and bioinspired designs. These approaches collectively aim to achieve uniform Zn deposition, suppress parasitic reactions, and enhance cycling stability. Furthermore, it critically evaluates the advantages and feasibility of different strategies, discuss potential synergistic effects of multi-strategy integration, and provide perspectives for future research directions.

Gallium nitride (GaN) nanostructures are highly promising for photoelectrochemical (PEC) water splitting due to their excellent electron mobility, chemical stability, and large surface area. However, the wide bandgap (~3.4 eV) of GaN limits its ability to absorb a broad spectrum of solar radiation, restricting its PEC performance. To address this limitation, MoS₂/GaN nanorods (NRs) heterostructures for enhanced PEC applications were fabricated on thin tungsten foil using a combination of atmospheric pressure chemical vapor deposition (CVD) and laser molecular beam epitaxy (LMBE). The Raman spectroscopy and X-ray diffraction revealed the hexagonal phase of GaN and MoS₂. X-ray photoelectron spectroscopy examined the electronic states of the GaN and MoS₂. PEC measurements revealed that the MoS₂-decorated GaN NRs exhibited a photocurrent density of approximately172 μA/cm², nearly 2.5-fold compared to bare GaN NRs (~70 μA/cm²). The increased photocurrent density is ascribed to the Type II band alignment between MoS₂ and GaN, which promotes effective charge separation, the decrease in charge transfer resistance, and the increase in active sites. The findings of this work underscore that the CVD and LMBE technique fabricated MoS₂/GaN heterostructures on W metal foil substrate can provide the vital strategy to raise the PEC efficiency toward solar water splitting.

The presence of alkaline earth metal ions in biodiesel can exacerbate engine wear, impair fuel oxidation stability, and substantially reduce combustion efficiency. Improving the quality of biodiesel is therefore crucial for promoting its adoption as a viable alternative to conventional fossil fuels. This study investigates the removal of alkaline earth metal calcium (Ca²⁺) and magnesium (Mg²⁺) from Jatropha biodiesel using four amino polycarboxylate chelating agents: ethylenediaminetetraacetic acid (EDTA), diethylenetriaminepentaacetic acid (DTPA), 1,2-cyclohexanediaminetetraacetic acid (CDTA), and N-(2-hydroxyethyl)-ethylenediaminetriacetic acid (HEDTA). The results showed that CDTA demonstrated the highest removal efficiency and selectivity for Ca²⁺ and Mg²⁺ among the four chelating agents, resulting in removal rates of 98.6% and 94.3%, respectively. Furthermore, the oxidative stability of biodiesel, measured as induction period, increased from 3.38 to 8.31 h after treatment with EDTA solution and reached a maximum of 8.68 h after treatment with CDTA. Density functional theory (DFT) calculations were performed to analyze Mulliken charges, electrostatic potential, frontier molecular orbitals, and interaction energies. The results indicate that the four chelating agents form cyclic structure complexes by simultaneously coordinating with a metal ion through multiple coordination atoms (N atom in amino group and O atom in carboxyl group). CDTA has the strongest interaction energies with Ca²⁺ and Mg²⁺, calculated at −826 and −915 kcal/mol, respectively, corroborating its superior chelation performance.

As intrinsically carbon-free molecules, ammonia and hydrogen are considered as fuels for internal combustion engines, mainly for long-distance or off-road applications. These alternative fuels have different combustion characteristics, reactivity, and exhaust gas compositions compared to conventional fuels, raising questions about the suitability of lubricants in engines operating with them. The impact of ammonia, hydrogen, and their blends on lubricants in internal combustion engines is a relatively new topic, with few reference studies available. However, degradation processes of lubricants have been studied in the context of hydrocarbon fuels, and in compressors using ammonia as a refrigerant, for example. This work presents a review of the literature on engine oil degradation phenomena in relation to ammonia and hydrogen combustion characteristics. In particular, it highlights the current state of knowledge regarding compatibility with unburnt gases, elevated nitrogen oxide levels, and water. Additionally, it summarizes the latest insights into the contribution of lubricants to pollutant emissions.

Conventional flat-plate photovoltaic-thermal (PV-T) collectors generate electricity and heat simultaneously; however, the outlet temperature of the latter is typically below 60 °C, limiting their widespread application. The use of optical concentration can enable higher-temperature heat to be generated, but this can also lead to a rise in the operating temperature of the PV cells in the collector and, in turn, to a deterioration in their electrical performance. To overcome this challenge, an optical spectral-splitting filter that absorbs the infrared and transmits the visible portion of the solar spectrum can be used, such that wavelengths below the bandgap are sent to the cells for electricity generation, while those above it are sent to a thermally decoupled absorber for the generation of heat at a temperature that is considerably higher than that of the cells. In this study, a triangular primary PV-T channel, wherein the primary heat transfer fluid (water) flows, is integrated into a parabolic trough concentrator of geometrical concentration ratio ~10, while a secondary liquid filter (water, AgSiO₂-eg or Therminol-66) is introduced for spectral splitting. Optical, electrical and thermal-fluid (sub-)models are developed and coupled to study the performance of this collector. Each sub-model is individually checked against results taken from the literature with maximum deviations under 10%. Subsequently, the optical and electrical models are coupled with a 3-D thermal-fluid CFD model (using COMSOL Multiphysics 6.1) to predict the electrical and thermal performance of the collector. Results show that when water is used as the optical filter, the maximum overall thermal (filter channel plus primary channel) and electrical efficiencies of the collector reach ~45% and 15%, respectively. A comparison between water, AgSiO₂-eg and Therminol-66 reveals that AgSiO₂-eg improves the thermal efficiency of the filter channel by ~25% (absolute) compared to Therminol-66 and water, however, this improvement — which arises from the thermal performance of the filter — comes at an expense of a ~5% electrical efficiency loss.

Hydrogen, recognized as a critical energy source, requires green production methods, such as proton exchange membrane water electrolysis (PEMWE) powered by renewable energy. This is a key step toward sustainable development, with economic analysis playing an essential role. Life cycle costing (LCC) is commonly used to evaluate economic feasibility, but traditional LCC analyses often provide a single cost outcome, which limits their applicability across diverse regional contexts. To address these challenges, a Python-based tool is developed in this paper, integrating a bottom-up approach with net present value (NPV) calculations and Monte Carlo simulations. The tool allows users to manage uncertainty by intervening in the input data, producing a range of outcomes rather than a single deterministic result, thus offering greater flexibility in decision-making. Applying the tool to a 5 MW PEMWE plant in Germany, the total cost of ownership (TCO) is estimated to range between €52 million and €82.5 million, with hydrogen production costs between 5.5 and 11.4 €/kg H₂. There is a 95% probability that actual costs fall within this range. Sensitivity analysis reveals that energy prices are the key contributors to LCC, accounting for 95% of the variance in LCC, while iridium, membrane materials, and power electronics contribute to 75% of the variation in construction-phase costs. These findings underscore the importance of renewable energy integration and circular economy strategies in reducing LCC.

Electrochemical CO₂ reduction (CO₂RR) is a promising technology for mitigating global climate change. The catalyst layer (CL), where the reduction reaction occurs, plays a pivotal role in determining mass transport and electrochemical performance. However, accurately characterizing local structures and quantifying mass transport remains a significant challenge. To address these limitations, a systematic characterization framework based on deep learning (DL) is proposed. Five semantic segmentation models, including Segformer and DeepLabV3plus, were compared with conventional image processing techniques, among which DeepLabV3plus achieved the highest segmentation accuracy (> 91.29%), significantly outperforming traditional thresholding methods (72.35%–77.42%). Experimental validation via mercury intrusion porosimetry (MIP) confirmed its capability to precisely extract key structural parameters, such as porosity and pore size distribution. Furthermore, a series of ionomer content gradient experiments revealed that a CL with an ionomer/catalyst (I/C) ratio of 0.2 had the optimal pore network structure. Numerical simulations and electrochemical tests demonstrated that this CL enabled a twofold increase in gas diffusion distance, thereby promoting long-range mass transport and significantly enhancing CO production rates. This work establishes a multi-scale analysis framework integrating “structural characterization, mass transport simulation, and performance validation,” offering both theoretical insights and practical guidance for the rational design of CO₂RR CLs.