Frontiers in Energy最新文献

Although the efficiency of three-dimensional (3D) perovskite solar cells (PSCs) has exceeded 26%, their commercialization remains hindered by environmental instability. Two-dimensional (2D) perovskites, with excellent hydrophobicity and barrier properties, exhibit enhanced environmental stability; however, their quantum-well structure limits charge transport. By combining the advantages of both, researchers have proposed wrapping 2D materials around the surface of 3D films to form a 2D/3D wrapping structure, which synergistically enhances environmental tolerance without compromising the optoelectronic performance of 3D phase. Focusing on this distinctive 2D/3D wrapping structure, this review systematically presents conventional preparation methods for 2D capping layer; commonly used cations for 2D capping materials, along with cations worthy of exploration and the characteristics of high-performance cations; the excellent physical properties reported for 2D/3D wrapping structures, including defect passivation, energy-level alignment, crystallization optimization, and phase-structure stabilization; current research hotspots concerning 2D/3D wrapping structures, including suppression of ion migration, facilitation of charge-carrier transport, enhancement of oriented growth of 2D components, and applications in tandem solar cells; and future issues requiring attention, including the influence of the 2D material’s n value on the performance of the wrapping structure and physical mismatches induced by 2D cations. This review supports further optimization of 2D/3D wrapping structures, elucidates their physicochemical mechanisms during photovoltaic energy conversion, and provides a foundation for improving the stability and efficiency of PSCs.

Due to their high energy density and long cycle life, lithium-ion batteries have become a preferred energy-storage solution for electronic devices, electric vehicles and renewable energy storage. In this study, a pseudo-two-dimensional (P2D) model of a half-cell with NCM622 as the cathode material was developed to investigate the effects of electrode thicknesses and heterogeneous porosity distribution on the kinetic performance of lithium-ion batteries. Discharge voltage–capacity curves were calculated under varying electrode thicknesses, discharge rates, and porosity distributions to elucidate their effects on discharge capacity. Based on the established database incorporating electrode thickness, discharge rate, heterogeneous porosity distribution, and discharge voltage–capacity curves, a conditional generative adversarial network (cGAN) was developed to predict discharge voltage–capacity curves. The optimal combinations of electrode thickness and porosity distribution that maximize the discharge capacity of NCM622 half-cells at different discharge rates were then determined using particle swarm optimization (PSO). Compared with the baseline porosity distribution at the same electrode thickness, the optimized heterogeneous porosity distribution increased the discharge capacity by 1.74%, 1.39%, 3.39%, and 1.66% at 0.5, 1, 2, and 4 C, respectively, with the largest improvement observed at 2 C.

As a key strategy for reducing CO₂ emissions, carbon capture and utilization (CCU) has gained increasing attention in the global pursuit of carbon neutrality and decarbonization. In particular, using a single material to achieve both efficient CO₂ capture and photocatalytic CO₂ conversion, thereby converting CO₂ into high-value-added products, represents an attractive strategy. Such an integrated design can not only simplify the process flow and reduce energy consumption and costs, but also minimize losses associated with intermediate steps through in situ conversion, significantly improving overall carbon utilization efficiency. Metal–organic frameworks (MOFs) are porous crystalline materials that can not only serve as physical adsorbents for CO₂ capture but also enable chemisorption through the rational design and optimization of secondary building units, organic linkers, and pore environments. Additionally, by engineering their electronic structures to optimize band structures and charge transport pathways, MOFs can serve as highly promising photocatalysts. Considerable progress has been made in the application of MOFs for CO₂ capture and conversion. However, significant advances are still needed in the development of integrated MOFs-based systems that combine CO₂ capture, enrichment, and in situ photocatalytic conversion. To accelerate progress in this area, a detailed review of recent developments is particularly important. Therefore, this review summarizes recent advances in MOFs-based materials for CCU, with particular emphasis on strategies for enhancing both CO₂ capture and photocatalytic CO₂ conversion. Finally, it discusses the current challenges and future research directions for developing integrated MOFs-based CCU systems.

Direct photocatalytic oxidation of methane to methanol under ambient pressure offers an attractive route for C₁ valorization, yet it remains challenged by the competing requirements of activating inert C–H bonds while preventing methanol overoxidation. Here, a WO₃/BN interface is reported that addresses this activity-selectivity trade-off by regulating the balance of reactive oxygen species. Interfacial coupling with BN promotes hole extraction from WO₃ and induces electron accumulation in the WO₃ domains, thereby preferentially enhancing ·OOH generation and increasing the ·OOH/·OH ratio. The elevated ·OOH/·OH ratio balances methane activation and methanol formation. Specifically,·OOH radicals facilitate C–H bond activation in methane, whereas ·OH radicals react with the resulting *CH₃ intermediates to form methanol. The optimized WO₃/BN-1:1 catalyst achieves a CH₃OH production rate of 741.6 µmol/(g·h), nearly twice that of pristine WO₃, with a CH₃OH selectivity of 77.2% under ambient-pressure conditions. This work provides insights into regulating reactive oxygen species through interface engineering for the selective photocatalytic conversion of methane.

Quantitative structure–property relationship (QSPR) modeling is a fundamental approach for property-oriented fuel design because it establishes quantitative relationships between molecular structures and physicochemical properties. Conventional QSPR models generally rely on manually engineered molecular descriptors, are usually developed to predict a single property, and often cannot readily incorporate newly available data, limiting their scalability and long-term applicability. Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in text understanding and parsing knowledge in specialized fields such as chemistry. This study proposes FuelProp-LM, a QSPR framework based on instruction tuning and in-context learning that predicts multiple fuel properties with only the SMILES notation of a fuel molecule as input. Four open-source language models were fine-tuned using 100000 data entries obtained from the PubChem database. To enhance contextual reasoning, high-information prompts were constructed through MACCS molecular fingerprint similarity retrieval. The proposed framework was evaluated on 10 fuel physicochemical property datasets and compared with the widely-adopted LLM DeepSeek-V3.2 and existing QSPR approaches. FuelProp-LM consistently achieved competitive or superior predictive performance across downstream tasks. Quantitative analysis further revealed how instruction tuning and in-context learning improve molecular property prediction by enhancing chemically relevant attention patterns. The accuracy, generalizability, data scalability, and ease of use of this work demonstrate that FuelProp-LM can serve as a practical and flexible tool for fuel property prediction and play a significant role in future AI-assisted fuel design.

Given the escalating challenge of global plastic pollution, pyrolysis has emerged as an important technology for converting waste plastics into high-value chemicals and liquid fuels. However, due to complex reaction networks, variable feedstock compositions, and multiphase flow characteristics, traditional methods face significant bottlenecks in process optimization and mechanistic exploration. Artificial intelligence (AI) offers a novel perspective for overcoming these complexities with its inherent advantages in processing high-dimensional data involving nonlinear interactions among variables. Against this backdrop, this review comprehensively surveys and analyzes the latest progress and limitations of applying AI to plastic pyrolysis, focusing on the identification of pyrolysis kinetic parameters using intelligent optimization algorithms and AI methods, the prediction of plastic pyrolysis reaction outcomes using AI, the exploration of intrinsic relationships between key process features and product distribution, and pyrolysis mechanisms using interpretability tools, and reactor optimization combining physical models such as computational fluid dynamics (CFD) with AI-based surrogate models. Furthermore, emerging applications of natural language processing (NLP) and large language models (LLMs) in automating the construction of standardized pyrolysis databases and knowledge graphs are explored. While summarizing technical advantages, current weaknesses such as data scarcity, insufficient model generalization, and the lack of physical information coupling are also analyzed and discussed. Finally, a conceptual architecture for an LLM-driven pyrolysis agent is proposed to integrate mechanism retrieval, process simulation, and experimental support. This review provides theoretical insights and future perspectives for intelligent process optimization and potential engineering applications of plastic pyrolysis for the conversion of waste plastics into fuels and chemicals.

Direct CO₂ hydrogenation to high-value light aromatics (BTX) has garnered significant research interest, but the current state-of-the-art BTX selectivity remains unsatisfactory. Herein, an oxide/dual-zeolite tandem catalyst was developed through physical mixing of SAPO-34 (hereafter abbreviated as SP34) and ZnZrO_x/ZSM-5 (hereafter referred to as ZZO/Z5). The optimized ZZO/2SP34/Z5 catalyst achieves a promising BTX selectivity (42.3% of aromatics), while maintaining a high CO₂ conversion (12.4%) and overall aromatic selectivity (75.3%, CO-free) at a relatively low temperature of 320 °C. By introducing the SP34 zeolite, the adsorption capacity of the ZZO/Z5 catalytic system for H and CO₂ is enhanced. Probe experiments and in situ DRIFTS spectra indicate that the SP34 component converts part of the methanol into light olefins, thereby reducing the concentration of oxygenated intermediates directly transferred to Z5 and inhibiting the surface methylation of BTX into C₉₊ aromatics. Compared with particle mixing and multi-bed mixing, proximate grinding mixing proves to be the optimal mixing method, facilitating the selective formation of BTX at 3 MPa and 1200 mL/(g_cat·h). Comparative experiments demonstrate that this dual-zeolite strategy is more facile and effective than the traditional passivation of external acid sites. Notably, such high BTX selectivity achieved by the ZZO/2SP34/Z5 catalyst at 320 °C has rarely been reported in the literature. This work provides an efficient non-petroleum pathway for the highly selective production of BTX and offers valuable insights into the reaction mechanisms associated with the two pathways of direct CO₂ hydrogenation to aromatics.

Protonic solid oxide electrolysis cells (P-SOECs) represent a promising technology for efficient and economical green hydrogen production. However, the development of high-performance anodes that simultaneously achieve excellent catalytic activity and mixed ionic–electronic conductivity remains a significant challenge. In this work, high-performance La_0.9Ba_0.1Co_0.7Ni_0.2Ag_0.1O_3−δ (LBCNA) anodes featuring the coexistence of three-phases—rhombohedral ABO₃-type LBCNA, Ruddlesden–Popper type LBCNA, and Ag metal nanoparticles—were successfully constructed. The optimized LBCNA-900 anode exhibits exceptional catalytic activity, with an ultralow area-specific resistance (ASR) of 0.10 Ω·cm² at 600 °C. When applied in P-SOECs, the LBCNA-900 anode demonstrates remarkable performance, achieving a high current density of 1.8 A·cm⁻² at 1.3 V at 600 °C. Beyond its promising application potential, this synergistic strategy provides a novel design principle for developing advanced electrocatalysts, with potential implications for a broad range of energy conversion and storage technologies.

In planar solid oxide fuel cells (SOFCs), operation at high fuel utilization inevitably induces strong fuel concentration gradients along the flow direction, leading to pronounced non-uniformity in current density distribution. Here, a thickness-gradient electrolyte strategy is proposed to homogenize the current distribution by deliberately modulating the local ohmic resistance, suppressing current density in fuel-rich inlet regions while enhancing it in fuel-depleted outlet regions. A gradient YSZ electrolyte with a thickness ranging from 5 to 13 µm was successfully fabricated on a 10 cm × 10 cm single cell via a wet-spraying process, together with a dense 1.8 µm gadolinia-doped ceria (GDC) barrier layer formed by in situ hydrothermal self-crystallization. Compared with conventional uniform-thickness electrolytes, the gradient electrolyte effectively reduces current density gradients and achieves improved current distribution uniformity under comparable fuel utilization conditions. Combined experimental characterization and multiphysics simulations demonstrate that the proposed gradient-electrolyte design significantly alleviates both current density non-uniformity and the associated thermal gradients in SOFCs operating at high fuel utilization. Overall, this work establishes a simple and scalable design strategy for regulating internal physical fields in SOFCs, offering a promising pathway toward improved conversion efficiency and enhanced durability under practically relevant operating conditions.

Light olefins, represented by ethylene, are key feedstocks in the petrochemical industry, but traditional production technologies suffer from high energy consumption and carbon emissions. Solid oxide electrolysis cells (SOECs) have emerged as a promising high-temperature electrochemical platform for light alkane upgrading, offering a green and efficient alternative for ethylene production. This review systematically summarizes the research progress, reaction mechanisms, and advanced material systems of SOEC-driven conversion of light alkanes (mainly methane and ethane) to ethylene. SOECs operate at 600–900 °C, enabling precise regulation of oxygen species activity and flux or proton extraction via electrochemical means, thereby overcoming the inherent conversion–selectivity trade-off of conventional thermochemical processes. The technology encompasses two main routes: oxygen-ion-conducting SOECs for the oxidative dehydrogenation (ODH) of ethane and oxidative coupling of methane (OCM), and proton-conducting SOECs for the non-ODH of ethane. Key reaction mechanisms involve the regulation of active oxygen species (e.g., lattice oxygen, peroxide, and superoxide) and proton transfer, while strategies such as in situ exsolution, elemental doping, and surface infiltration effectively enhance catalyst activity and selectivity. Advanced anode materials, including perovskites, metal–oxide heterointerfaces, and composite systems, have demonstrated remarkable performance. Ethane conversion rates of up to 80% with ethylene selectivity exceeding 85% have been achieved, comparable to those of conventional thermocatalytic routes, while highly stable systems capable of long-term operation have also been developed. For methane conversion, OCM technology achieves near-quantitative C₂ selectivity (> 99.5%) at moderate conversions, providing a foundation for further optimization. SOEC technology offers significant advantages such as low-carbon emissions, flexible feedstock adaptability, and compatibility with renewable energy. This review highlights the synergistic integration of material innovation, mechanism understanding, and system optimization in SOEC-driven alkane upgrading, providing a comprehensive reference for the development of low-carbon light olefin production technologies.