Direct air capture (DAC) based on adsorption is a promising negative-emission technology owing to its operational flexibility, modular deployment potential, and comparatively low regeneration temperature. In this study, a dynamic three-dimensional mathematical model was developed to investigate a structured adsorption-based DAC reactor operating under a temperature–vacuum swing adsorption cycle. The model couples heat and mass transfer among the gas, adsorbent, metal structure, and heat-transfer fluid and was used to evaluate the temporal and spatial evolution of temperature and CO₂ adsorption capacity during adsorption and regeneration. The effects of internal cooling, heat-source temperature, and vacuum pressure on cyclic performance were systematically analyzed. The results show that introducing an internal cooling source significantly accelerates adsorbent-bed cooling and increases the cyclic working capacity by approximately 10%. Parametric simulations indicate that higher regeneration temperature and lower vacuum pressure enhance CO₂ desorption, with optimal performance achieved at a heat-source temperature of 90 °C and a vacuum pressure of 1 kPa. Under these conditions, the DAC system reaches an annual CO₂ productivity of 125 tCO₂·year⁻¹, with mechanical and thermal energy consumptions of 4.72 and 11.91 GJ·tCO₂⁻¹, respectively. This work provides a useful modeling framework for reactor design and operating-parameter optimization in adsorption-based DAC systems.

This review evaluates design strategies for copper-based bifunctional catalysts in coupled NH ₃ -SCR and CO oxidation. It clarifies structure–activity relations, reaction pathways, and deactivation mechanisms under industrial conditions.

Carbon capture, utilization and storage (CCUS) is indispensable for achieving global carbon neutrality, and amine-based chemical absorption remains the most mature and widely deployed technology. However, amine emissions during operation result in absorbent loss, secondary pollution and potential public health risks, while conventional offline detection methods are unable to satisfy the requirements of real-time monitoring. This review systematically summarizes recent progress in metal oxide semiconductor (MOS) gas sensors for monoethanolamine (MEA) detection, with particular emphasis on AOx-based systems. The effects of the base material, microstructure and modification strategies, including noble metal decoration and heterojunction construction, on sensing performance are critically compared. Through such modifications, AOx-based sensors have achieved markedly improved sensing performance, and several are capable of detecting MEA at room temperature. The feasibility of existing sensing materials is further evaluated in the context of post-combustion amine capture. Among the reported systems, SnO₂-based sensors exhibit clear advantages for MEA detection, including high sensitivity, favorable selectivity, rapid response and recovery, and satisfactory stability and repeatability. In contrast, studies on ABO₃ perovskite-based sensors remain limited; although these materials show fast response, they continue to suffer from poor stability and insufficient humidity resistance. Future research should therefore focus on low-temperature operation, enhanced humidity tolerance and long-term durability, so as to accelerate the industrial translation of MOS gas sensors and support the sustainable development of CCUS.

Carbon capture, utilization and storage (CCUS) is a critical technology for the fossil energy industry to achieve the dual carbon goals. Organic amine-based absorption methods, represented by mono-ethanolamine (MEA), methyl-diethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP), are currently the most widely adopted approaches for carbon dioxide capture. If the concentration of leaked organic amines exceeds the safety threshold, inhalation will cause severe respiratory irritation and even serious illnesses in humans. Accordingly, in situ monitoring of organic amine concentrations in waste gas is of great significance for process optimization, environmental protection, early health warning, energy conservation and emission reduction. In this study, a metal oxide semiconductor (MOS) sensor array was developed to identify categories and concentration variations in small-molecule organic amines. To realize effective gas classification and accurate concentration prediction, single-component gas data and mixed gas data with varying concentrations of the three amines collected in the laboratory were adopted to train a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (Bi-GRU) via five-fold cross-validation. The proposed model achieved a classification accuracy of 0.9969 ± 0.0006. For MEA concentration prediction, the optimal determination coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE) were 0.9969, 2.1306 and 1.5503, respectively. The experimental results demonstrate that the proposed method possesses promising practical application prospects in in situ gas monitoring and early hazard warning.