The inorganic ammonia carbon capture and co-production of ammonium fertilizer provides a good idea for low carbon production in coal-fired power plants. The integration of capture and utilization is expected to solve the problems of high energy consumption, high operating cost, and difficult utilization. In this paper, theories, product transformation, and application of the inorganic ammonia carbon capture are discussed, and the problems, such as low efficiency and serious NH₃ escape are summarized. The mass transfer kinetics is the common method to solve the above problems. Therefore, this paper summarizes the mass transfer kinetics model from three progressive levels, and focuses on the ‘mass transfer + reaction kinetics with the features of inorganic ammonia carbon capture’ theories and research methods of CO₂ absorption and NH₃ escape/absorption. Further, the relationship between the model and the design of the inorganic ammonia carbon capture system is established. The prediction of flux has been put forward based on the theory and research method of mass transfer kinetics model. The way of screening inhibitors or absorbents is proposed based on two principles of inhibiting NH₃ escape. Finally, the challenges and prospects of the inorganic ammonia carbon capture are mentioned, and the future trend of power plant centered on NH₃ is discussed.

Covalent organic framework (COF) membranes are promising for water treatment applications owing to their uniform pores and outstanding chemical stability. However, the harsh conditions and low synthetic efficiency of conventional solvothermal synthesis of COF membranes have severely hindered their practical development. Herein, we developed a dynamic interfacial polymerization strategy to rapidly construct a TpPa-1 selective layer on a polyacrylonitrile substrate within only 3 min. This method enabled the rapid and efficient fabrication of COF membranes by confining the reaction region to shorten the diffusion path of the monomers while exploiting the intrinsic high reactivity of TpPa-1 monomers. The rapid fabrication resulted in a thinner selective layer and enhanced permeance performance. In the methyl blue/Na₂SO₄ nanofiltration test, the separation factor reached 212, with a permeance of 865 L∙m^–2∙h^–1∙MPa^–1, outperforming most previously reported COF membranes.

As an unconventional hydrocarbon resource integrating coal, oil, and gas properties, tar-rich coal holds promise for advancing green and low-carbon utilization in the coal industry. The thermogravimetry and differential scanning calorimetry with different heating rates were employed to identify the combustion characteristics and oxidation reaction kinetics of coals. The research finds that the lower thermal thresholds accelerate tar-rich coal’s transition to ignition. At low heating rates, pyrolysis dominates tar-rich coal mass loss, the average activation energy for the pyrolysis of tar-rich coal exceeds that for tar-inclusive coal by more than 22.2 kJ∙mol^–1. During the entire exothermic process, the E value of tar-rich coal in the thermal decomposition stage is 15 kJ∙mol^–1 higher than that of tar-inclusive coal. In the combustion stage, the activation energy of tar-rich coal is 5 kJ∙mol^–1 lower than that of tar-inclusive coal. This indicates that the hydrogen-rich structure makes its decomposition process have a higher activation energy, and the small molecules produced during the decomposition enable tar-rich coal to enter the combustion stage more quickly and efficiently. It provided a certain basis for the in situ thermal decomposition mining of tar-rich coal, ultimately facilitating the safe and sustainable utilization of this hybrid energy resource.

Fine chemicals serve as the cornerstone of modern industry, and the level of their research and development is directly linked to a nation’s core competitiveness. However, traditional trial-and-error research and development paradigm faces severe challenges, including long development cycles, high costs, and low efficiency. In recent years, the rapid advancement of artificial intelligence has brought a disruptive transformation to the chemical research paradigm, enabling an end-to-end intelligent creation process that integrates molecular structure design with synthetic pathway planning, all driven by functional requirements. This article provides a systematic review of the latest research advances at the intersection of artificial intelligence and chemical engineering, focusing on three core aspects: intelligent structure-property relationship models, efficient molecular design methods, and intelligent synthetic pathway planning. It first explores how to construct high-precision property prediction models by integrating mechanistic knowledge with data. Second, it elaborates on novel inverse molecular design methods that shift the focus from screening to de novo design. Finally, it discusses how to bridge the gap from design to manufacturing through the intelligent planning of synthetic routes. This review aims to highlight the immense potential of artificial intelligence in driving the transformation of the fine chemicals industry toward greener, high-value-added, and more intelligent processes, and to provide an outlook on its future directions.

Graph neural networks (GNNs) have played an increasingly important role in molecular property prediction. However, GNN models are prone to face the oversmoothing problem. By reviewing existing works on addressing oversmoothing, we noticed that those methods are designed for graph data and often break the original topological structure. However, it is not acceptable in molecule data which the real physicochemical meanings are lost. Motivated by this, to address the problem of oversmoothing and fill the gap in molecule property prediction, we proposed AdapGNN, a novel model-agnostic framework that designed for molecule property prediction specially. This is achieved through the integration of original node feature into the message-passing step of GNN models. Besides, to emphasize the crucial part of a molecule during predicting and further enhance the explainability of our model, we proposed a weight projection module to generate node-specific weight when merging node features. Furthermore, to validate the efficiency of our method and addressing the problem that existing benchmark data set lacks of the ground-truth of atom importance. We proposed MolExplain, a new benchmark data set for quantitative explainability evaluation in molecule property prediction. Experimental results show that the AdapGNN significantly improves the explainability of GNN models while maintaining high predictive accuracy.

Under the global energy transition background, large-scale hydrogen energy application represents a crucial initiative for implementing national strategic demands. The development of effective and safe solid-state H₂ storage technologies serves as a key enabler for such large-scale implementation. Vanadium-based alloys with body-centered cubic structure have emerged as prime candidate materials for solid-state H₂ supply in fuel cells, owing to their theoretical hydrogen storage capacity of 3.8 wt% during near-room-temperature hydrogen absorption/desorption processes. This review systematically examines recent advances in various V-based hydrogen storage alloy systems, including the hydrogen absorption/desorption mechanisms of vanadium-based alloys; performance modulation strategies and underlying mechanisms for ternary (V-Ti-Cr), quaternary (V-Ti-Cr-Zr), quinary and higher-order (V-Ti-Cr-Fe-Al) systems; the effects of crucial factors, including chemical composition and lattice parameters, on thermodynamic and kinetic properties during hydrogenation/dehydrogenation cycles; and comprehensive performance optimization strategies. Addressing current limitations including compositional complexity and high design costs, this review highlights the importance of employing machine learning models (e.g., random forest, deep neural networks) to establish composition-property relationships, combined with optimization algorithms for efficient screening of V-based compositions to guide performance modulation. Lastly, a comprehensive analysis is provided on the coupling interaction mechanisms of engineering factors in complex service environments and their significant effects on the long-term service performance of alloys. This study offers valuable insights for the design and industrial application of V-based alloys, thereby contributing to the advancement of solid-state H₂ storage within the H₂ energy industry chain.