Frontiers in Energy最新文献

With the global energy system transitioning to renewable energy, high-efficiency energy storage and conversion technologies have become crucial. However, traditional research paradigms for the research and development (R&D) of energy materials such as batteries and electrocatalysts present the limitations in efficiency. This review systematically summarizes the progress of artificial intelligent (AI) in this field, ranging from classical machine learning (ML) to advanced representation methods such as graph neural networks (GNNs) and transformers that enable precise property prediction and structure generation. It also covers generative models for inverse design and large language models (LLMs) for knowledge extraction, along with key domain databases. Current challenges include limited interpretability and the underutilization of emerging AI technologies. Finally, this review discusses future directions such as the applications of multimodal language models, aiming to provide insights for accelerating high-performance energy materials innovation and advancing the global renewable energy transition.

Achieving both a low operating temperature for photovoltaic (PV) and a high heat collection temperature for photothermal (PT) conversion in full-spectrum solar energy utilization is challenging with traditional spectrum-splitting methods. Therefore, this study focuses on the full-spectrum solar utilization and proposes a novel multi-stage concentrating and spectrum-splitting coupling approach for complementary photovoltaic-thermophotovoltaic (PV-TPV) conversion. Multi-stage thermophysical models are developed based on thermodynamic analysis, Shockley-Queisser model coupling, and external quantum efficiency model coupling, incorporating cell combinations with different bandgaps and temperature coefficients, enabling performance analysis from idealized scenarios to realistic conditions. A single-stage spectrum splitting PV-TPV system is optimized as a baseline, and the impact of multi-stage spectrum coupling on system performance is investigated. Results show that low-bandgap cells with higher temperature coefficients can achieve superior performance at lower concentration ratios compared with high-bandgap cells at higher concentration ratios. Considering the practical external quantum efficiency (EQE) model, low-bandgap cells demonstrate additional advantages, achieving a maximum system efficiency of 41.82% at C₁ = 500 and C₂ = 300. The multi-stage spectrum-splitting design allows decoupling of the spectrum and concentration ratio, effectively reducing the system concentration ratio by more than 50% while maintaining high system performance. This not only facilitates device design and practical implementation but also enhances theoretical efficiency, demonstrating significant application potential. The study provides valuable insights for the development of full-spectrum PV-TPV conversion methods.

Hydrogen peroxide (H₂O₂), a versatile chemical with critical applications in sterilization, wastewater treatment, and chemical synthesis, is conventionally produced via the anthraquinone process. However, this approach entails significant safety risks. Electrochemical in situ H₂O₂ production via the two-electron oxygen reduction reaction (2e⁻ ORR) has emerged as a sustainable and inherently safe alternative and has attracted increasing interest from both scientific research and industry. This review systematically summarizes recent advancements in various electrocatalysts for 2e⁻ ORR-based hydrogen peroxide (H₂O₂) production, with a focus on key determinants of activity and selectivity. Catalyst classification, structural design strategies, electronic property modulation, reaction mechanism insights, and optimal operating conditions are examined to guide enhanced H₂O₂ yield. It is anticipated that this comprehensive analysis will provide a foundational framework for future novel catalyst optimization efforts, ultimately advancing the efficiency, selectivity and stability of electrochemical H₂O₂ synthesis.

Thermally integrated Carnot battery (TI-CB) systems offer unique advantages for industrial waste heat recovery, but their performance under fluctuating, off-design conditions remains poorly understood. To address this gap, this study proposes a quasi-dynamic mathematical model with solution methodologies applicable to both design and off-design operating conditions. A dynamic evaluation framework is also developed to account for the temporal mismatch between energy storage and release processes. A multi-operating-condition set constructed via multivariable sampling is used to enable systematic analysis of key design parameters under both design and off-design conditions. The results reveal that heat source utilization parameters and heat pump temperature rise are dominant factors affecting TI-CB performance, while off-design analysis shows that ORC mass flow rate variations have a more significant impact on system performance than heat pump fluctuations. Due to irreversible heat losses, an increase in the heat source temperature difference leads to a decrease in round-trip efficiency (η_rt) from 62.6% to 45.8%, while η_orc and η_ex also exhibit downward trends. A higher temperature lift in the heat pump results a decrease in the mean COP from 7.6 to 4.8, whereas η_orc increases from 7.0% to 10.2%. Among working fluids evaluated, R1336mzz(Z) demonstrates superior performance but exhibits nonlinear behavior, while R1233zd(E) provides optimal stability across operating ranges, making it suitable for practical engineering applications.

The increasing emission of carbon dioxide (CO₂) has intensified global efforts toward its conversion and utilization. Electrocatalytic CO₂ reduction reaction (CO₂RR) has emerged as a promising sustainable strategy to address interconnected energy and environmental challenges. Among the various products of CO₂ reduction, methanol has attracted significant research attention as both an essential chemical feedstock and a promising renewable energy carrier. This review comprehensively summarizes recent advances in the electrocatalytic conversion of CO₂ to methanol, with systematic discussions on fundamental reaction mechanisms and pathways, innovative reactor configurations, diverse catalysts, and auxiliary optimization strategies. Particular emphasis is placed on categorizing and evaluating various catalysts, including mono-/bimetallic catalysts, molecular catalysts, enzyme catalysts, and carbon-based materials, while exploring their structure-activity relationships and performance enhancement strategies for improving methanol selectivity. Furthermore, the techno-economic viability of current processes is analyzed, assessing the cost-effectiveness and commercial potential of electrocatalytic methanol production. Finally, based on current research progress and existing challenges, key research directions are outlined to advance the development of commercially feasible electrocatalytic CO₂-to-methanol systems, providing practical guidance for future investigations.

The development of low-cost platinum-free electrocatalysts for the oxygen reduction reaction (ORR) is essential for the sustainable energy technologies. In this work, spinel-type LiMn₂O₄ was chemically modified via proton exchange to systematically investigate the effects of protonation on crystal structure, electronic configuration, and ORR performance. Experimental results reveal that proton exchange not only regulates the lattice parameters and Mn oxidation states, but also enhances surface hydrophilicity and oxygen adsorption capacity, leading to a significant improvement in ORR activity with at a half-wave potential of 0.81 V for pure Mn-based oxide. Physical characterizations and theoretical calculations reveal that protonation optimizes the surface electronic structure by mitigating the over-stabilization of oxygen intermediates on LiMn₂O₄, thus facilitating the rate-determining step *OH adsorption and improving reaction kinetics. This work establishes proton exchange as a versatile strategy for the construction of Mn-based oxide electrocatalysts containing alkali metals, offering valuable insights for the rational design of nonprecious metal catalysts in energy conversion applications.