SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4205-8
The global energy crisis and environmental pollution necessitate efficient recovery and utilization of thermal energy resources such as industrial waste heat. Thermoelectric materials, enabling direct conversion between thermal and electrical energy, offer broad application prospects in waste heat power generation and chip cooling. The energy conversion efficiency is determined by the dimensionless figure of merit, ZT = (S^2σ/κ)T, where S is the Seebeck coefficient, σ is the electrical conductivity, κ is the thermal conductivity, and T is the absolute temperature. Ideal thermoelectric materials require both a high power factor (PF = S^2σ) and low thermal conductivity. However, the strong coupling between electrical and thermal transport parameters makes synergistic optimization challenging. Over the past two decades, strategies such as band engineering, nanostructuring, liquid-like ions, interstitial atoms, phonon softening, and defect engineering have been explored. Among these, entropy engineering has emerged as a novel strategy that achieves synergistic optimization by introducing multiple components to increase configurational entropy. High entropy materials, originating from alloys, are defined as multi-principal element systems with five or more elements in near-equiatomic ratios forming single-phase solid solutions. The molar configurational entropy ΔS_conf = R∑x_i ln x_i, with materials classified as high entropy (ΔS_conf > 1.5R), medium entropy (1R < ΔS_conf < 1.5R), or low entropy (ΔS_conf < 1R). Four core effects are summarized: high entropy effect, lattice distortion effect, sluggish diffusion effect, and cocktail effect. Research has expanded from alloys to oxides, chalcogenides, and half-Heusler compounds. This review systematically summarizes the mechanisms by which lattice distortion in high entropy materials affects electrical and thermal transport, and discusses optimization strategies for thermoelectric performance.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025112603
The cycloaddition of carbon dioxide (CO2) to epoxides (CCE) is a 100% atom-economical transformation yielding cyclic carbonates, which are valuable chemical products. This reaction valorizes CO2 as a carbon feedstock, mitigating the greenhouse effect and aligning with carbon neutrality goals. Conventional covalent organic framework (COF) catalysts often require co-catalysts to achieve high efficiency. To address this, we designed and prepared a series of ionic COFs, denoted EB-BT(nOH), that simultaneously incorporate acid (hydroxyl), base (nitrogen), and nucleophilic bromide (Br−) functionalities. These materials efficiently catalyze the CCE reaction without any co-catalyst. Among them, EB-BT(OH) exhibited the highest catalytic activity, achieving a 99% yield of the target product at 120 °C and 2.0 MPa CO2 pressure. By systematically varying the hydroxyl content in the COF backbone, we investigated the critical role of hydrogen bond donors (HBDs) in the CCE reaction. This work provides new design principles for COF-based catalysts for CCE, eliminating the need for co-catalysts and enhancing process sustainability.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60655-X
Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.