Green Energy & Environment最新文献

Direct seawater splitting has emerged as a popular and promising research direction for the synthesis of clean, green, non-polluting, and sustainable hydrogen energy without depending on high-purity water in the face of the world's shortage of fossil energy. However, efficient seawater splitting is hindered by slow kinetics caused by the ultra-low conductivity and the presence of bacteria, microorganisms, and stray ions in seawater. Additionally, producing hydrogen on an industrial scale is challenging due to the high production cost. To address these challenges, this review presents that from the catalyst point of view, designing catalysts with high catalytic activity and high stability can directly affect the rate and effect of seawater splitting. From the ion transfer perspective, designing membranes can block harmful ions, improving the stability of seawater splitting. From the energy point of view, mixed seawater systems and self-powered systems also provide new and low-energy research systems for seawater splitting. Finally, ideas and directions for further research on direct seawater splitting in the future are pointed out, with the aims of achieving low-cost and high-efficiency hydrogen production.

The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for the CO₂ cycloaddition reaction. The descriptors for model training were judiciously chosen according to the reaction mechanism, which leads to high accuracy up to 97% for the 75% quantile of the training set as the classification criterion. The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. Among them, MOF-76(Y) achieved the top performance experimentally among reported MOFs, in good agreement with the prediction.

CO₂ photoreduction into carbon-based chemicals has been considered as an appropriate way to alleviate the energy issue and greenhouse effect. Herein, the 5, 10, 15, 20-tetra (4-carboxyphenyl) porphyrin cobalt(II) (CoTCPP) has been integrated with BiOBr microspheres and formed the CoTCPP/BiOBr composite. The as-prepared CoTCPP/BiOBr-2 shows optimized photocatalytic performance for CO₂ conversion into CO and CH₄ upon irradiation with 300 W Xe lamp, which is 2.03 and 2.58 times compared to that of BiOBr, respectively. The introduced CoTCPP significantly enhanced light absorption properties, promoted rapid separation of photogenerated carriers and boosted the chemisorption of CO₂ molecules. The metal Co²⁺ at the center of the porphyrin molecules also acts as adsorption center for CO₂ molecules, boosting the CO₂ convert into CO and CH₄. The possible mechanism of CO₂ photoreduction was explored by in-situ FT-IR spectra. This work offers a new possibility for the preparation of advance photocatalysts.

The application of industrial solid wastes as environmentally functional materials for air pollutants control has gained much attention in recent years due to its potential to reduce air pollution in a cost-effective manner. In this review, we investigate the development of industrial-waste-based functional materials for various gas pollutant removal and consider the relevant reaction mechanism according to different types of industrial solid waste. We see a recent effort towards achieving high-performance environmental functional materials via chemical or physical modification, in which the active components, pore size, and phase structure can be altered. The review will discuss the potential of using industrial solid wastes, these modified materials, or synthesized materials from raw waste precursors for the removal of air pollutants, including SO₂, NO_x, Hg⁰, H₂S, VOCs, and CO₂. The challenges still need to be addressed to realize this potential and the prospects for future research fully. The suggests for future directions include determining the optimal composition of these materials, calculating the real reaction rate and turnover frequency, developing effective treatment methods, and establishing chemical component databases of raw industrial solid waste for catalysts/adsorbent preparation.

Viscosity is one of the most important fundamental properties of fluids. However, accurate acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an approach integrating prior physical knowledge into the machine learning (ML) model was proposed to predict the viscosity reliably. The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size, structure, and interactions of the ILs. Three strategies based on the residuals of the COSMO-RS model were created as the output of ML, where the strategy directly using experimental data was also studied for comparison. The performance of six ML algorithms was compared in all strategies, and the CatBoost model was identified as the optimal one. The strategies employing the relative deviations were superior to that using the absolute deviation, and the relative ratio revealed the systematic prediction error of the COSMO-RS model. The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set (R² = 0.9999, MAE = 0.0325), reducing the average absolute relative deviation (AARD) in modeling from 52.45% to 1.54%. Features importance analysis indicated the average energy correction, solvation-free energy, and polarity moment were the key influencing the systematic deviation.