xisting carbon capture and storage technology can only slow down the carbon emissions of large point sources but cannot treat the already accumulated CO₂ in the environment. Herein, we demonstrated a simple direct CO₂ capture method from air via reactive crystallization with a new trichelating iminoguanidine ligand (BTIG). It could strongly bind CO₂ to form insoluble carbonate crystals that could be easily isolated. In the crystal, CO₂ was transformed to CO₃ ^2- and trapped in a dense hydrogen bonding network in terms of carbonate-water clusters. This capture process was reversible, and the BTIG ligand could be regenerated by heating the BTIG-CO₂ crystal at a mild temperature, which was much lower than the decomposition temperature of CaCO₃ (∼900 °C). Thermodynamic and kinetics analyses indicate that the crystallization process was exothermic with an enthalpy of -292 kJ/mol, and the decomposition energy consumption was 169 kJ per mol CO₂. In addition, BTIG could also be employed for CO₂ capture from flue gas with a capacity of 1.46 mol/mol, which was superior to that of most of the reported sorbents.23:T49c,Desert sand is one of the current research hotspots in alternative materials for concrete aggregates. In the process of practical application, compressive strength is an essential prerequisite for studying other properties. Based on the current research situation, a prediction technology of compressive strength of desert sand concrete (DSC) is proposed based on an artificial neural network (ANN) and a particle swarm optimization (PSO). The technology is a prediction model that adjusts the network architecture by using the PSO method based on the ANN optimization model. Water‐binder ratio, sand ratio, replacement rate of desert sand, desert sand type, fly ash content, silica fume content, air content, and slump were selected as the neural network’s inputs. The compressive strength data of 118 different combinations of influencing variables were tested to establish the dataset. The results show that the PSO method is efficient for the ANN in DSC compressive strength research. Furthermore, referring to this method, DSC is applied to the shotcrete of tunnels in construction engineering successfully, and the dust particle content during construction is evaluated.

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1	Engineering	9	9	357
2	Chemical Engineering	6	6	235
3	Chemistry	6	6	235
4	Education	1	1	31
5	Civil Engineering	1	1	19

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