Efficient and informative air quality modeling in future emission scenarios is vital for effective formulation of emission reduction policies. Traditional chemical transport models (CTMs) struggle with the computational demands required for timely predictions. While advanced emulator techniques greatly accelerate CTM simulating process, they fall short in providing comprehensive estimates of future air quality due to their limited model structure. Additionally, these emulators often have difficulty simultaneously accounting for varying emission variables and the effects of regional transport, which limits their applicability and undermines prediction accuracy. In this study, an informative future air quality prediction model “TGEOS v1.0” based on the Transformer framework is developed as an efficient agent model of GEOS-Chem v14.2.2. TGEOS is able to efficiently estimate key statistical indicators of PM2.5 and O3 concentrations under future emission scenarios and capture potential extreme pollution events, with approximately 2.51 s to execute one-year estimation. The model incorporates sectoral emissions of up to 26 distinct species as well as the impacts of regional emissions and meteorology on pollutant concentrations, enhancing its versatility and predictive accuracy. The spatial and probability distributions predicted by TGEOS are in good agreement with GEOS-Chem, with the correlation coefficients for PM2.5 and O3 exceed 0.98 in high-pollution months. Compared with other machine learning models, TGEOS based on Transformer framework showcases superior performance, underscoring the potential of the Transformer framework in air quality modeling.

As an outstanding corrosion inhibitor for copper, the adsorption behavior of benzotriazole on copper nano cluster has been investigated and compared with copper (111), (100) and (110) surfaces by means of density functional theory (DFT) calculations. Calculations reveal that benzotriazole has chemical adsorption on different copper surfaces and copper nano cluster when its N2-N3 bond is placed down on them. Also, the adsorption energy and closest interatomic distance show strong dependence on the surface structure, which indicates its ability to passivate the reactive under-coordinated surface sites. Relatively strong chemical bond between the copper cluster and the adsorbate is found to form. The chemisorption mechanism and the local structure of benzotriazole on copper surfaces and nano cluster are also discussed and presented.

Grid-stiffened composite structures not only allow for significant structural weight reduction but also are competitive in terms of structural stability and damage tolerance compared with conventional stiffened candidates. As the development of Automated Fibre Placement (AFP) technology matures, a unitized construction of skin and stiffeners is easily manufacturable. In this paper, a curved stiffener layout is optimized to enhance the structural buckling resistance. A linear variation of stiffener angles is used, resulting in the formation of a locally rhombic lattice pattern. Due to the spatial variation of angle and spacing induced by the use of curved stiffeners, analytic solutions for the responses are not generally applicable. Thus, global and local buckling loads are calculated based on finite element models by a previously-developed global/local coupled strategy. Since the stiffeners are not explicitly modelled in the finite element calculations, a fixed mesh is used for gradient-based optimization. Both parametric design and optimization are performed in order to find the optimal curved grid pattern, whose practical performances are assessed by post-buckling analysis. A comparison between the performances of structures with curved stiffeners, with straight stiffeners, and variable-stiffness skins with curved fibres, demonstrates the potential of curved stiffener configurations in improving the structural efficiency.