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In this study, the microstructure evolution on hot deformation was investigated and a dynamic recrystallization (DRX) kinetic model was constructed based on the hot deformation behavior of the homogenized state Mg–7Gd–3Y–1Zn–0.5Zr (VW73B) alloy within 713–773 K and 0.01–0.1 s−1. The DRX critical strain reduced when the deformation temperature (T) increased and increased when the strain rate (ε˙) increased. The variation trend of the DRX volume fraction was inversely proportional to the critical DRX strain. The constructed DRX kinetic model was modified by introducing a compensation factor; the modified model showed a mean absolute deviation of the DRX volume fraction of 1.8%. Combined with the microstructure analysis, it can be found that the Long Period Stacking Ordered (LPSO) phase is able to promote DRX during the hot deformation, but its ability to promote DRX is less pronounced than that of grain boundaries. At the block-shaped LPSO phase interface, the grain boundary arched by strain induction provided a nucleation site for DRX, enabling discontinuous dynamic recrystallization (DDRX). At the lamellar LPSO phase interface, continuous dynamic recrystallization (CDRX) occurred predominantly through the continuous migration and merging of subgrains, resulting in the formation of DRX grains. The results of this study could guide future research on the DRX of the VW73B alloys with varied applications.

The effects of extrusion ratio (ER) on the microstructures, static mechanical properties, and tension-compression high cycle fatigue (HCF) behavior of the as-extruded Mg–5Zn–1Mn (ZM51) alloy was investigated systematically, and the HCF mechanism was discussed. The results show that texture weakening and grain refinement caused by increasing the ER can effectively enhance the static mechanical properties and alleviate the tensile-compressive asymmetry of the alloy. The fatigue strength at 107 cycles is 124 ± 5, 138 ± 4, and 142 ± 5 MPa for ER8, ER11.5, and ER23, respectively, and the corresponding fatigue ratio is approximately 0.45, 0.49, and 0.50, which means that the as-extruded ZM51 alloy possesses outstanding fatigue resistance. The failure analysis and microstructure evolution of post-fatigued samples demonstrated that the stress amplitudes and microstructure characteristics strongly influence the HCF mechanism of the alloy. At high-stress load, close to the tensile yield strength 160–180 MPa, abundant {101‾2} residual twin bands were observed near the fracture surface. Twinning/detwinning deformation is the dominant fatigue mechanism due to the plastic deformation incompatibility between the matrix and the residual twins. At low-stress load, close to the fatigue strength 125–145 MPa, the fatigue crack initiation mechanism transits from twinning/detwinning to dislocation slip. Grain refinement and texture weakening inhibit the activation of {101‾2} twins, alleviate the twin-dislocation interaction, and promote dislocation slip to dominate the fatigue deformation, thereby enhancing the fatigue life of the alloy. This work provides new insight into the design and development for enhancing the fatigue resistance of wrought Mg alloy to ensure the long-term service safety of structural materials.

The effects of Sn in as-extruded Mg-1.2Ga alloys on the electrochemical and discharge properties were investigated by in-situ electrochemistry, half-cell and air-cell tests to provide new ideas for the development of magnesium alloy anode materials. The addition of Sn promoted grain refinement and deformation precipitation on the extrusion streamlines. Extrusion can effectively reduce the |bc| value of the alloy, improving the cathodic hydrogen control process. After the addition of Sn, more secondary phases are introduced. The redeposition and dissolution of Sn and Ga are enhanced. In conclusion, the GT12 alloy has excellent anode efficiency and average cell voltage, and its maximum discharge capacity (1292.98 mAh·g−1 at 75 mA·cm−2) is 55 mAh·g−1 higher than that of the as-cast alloy, showing great potential for development.

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3−)). The mechanism between ε(NO3−) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3−). Here we introduce a supervised machine learning approach—the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42−, and temperature as pivotal drivers for ε(NO3−). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

The thermal compression deformation behavior of homogenized Mg–5Zn–1Mn (wt.%) alloy was systematically investigated over the temperature and strain rate ranges of 285–345 °C and 0.001–1 s−1, respectively. The constitutive model and thermal processing diagrams were established. The microstructure evolution process and dynamic recrystallization (DRX) mechanisms were analyzed. The results demonstrate that the flow stress curves of the ZM51 alloy exhibit prominent DRX characteristics. The peak stress can be improved by decreasing the deformation temperature or increasing the strain rate. A finite element simulation is performed, and the Arrhenius constitutive model can accurately depict the plastic flow process of the alloy with strain correction, and the average activation energy is about 143.821 kJ/mol. Both the percentage DRX and the DRX grain size are found to be sensitive to the applied deformation conditions, including temperature, strain rate, and true strain. The formation of DRX grains promotes the weakness of the texture and the refinement of the grains. Grain boundaries nucleation (including discontinuous and continuous DRX) and particle-stimulated nucleation (PSN) are the main DRX mechanisms. Based on the processing map, ZM51 alloy is suitable for large plastic deformation by multi-pass forging. The optimum processing temperature is 345 °C, and as the degree of plastic deformation increases, the strain rate can be suitably increased. Local plastic flow and microcracking at grain boundaries are the primary instability mechanisms of the alloy.

Ground-level ozone (O3) pollution has received widespread attention because its rising trend and adverse ecological impacts. However, the extremely strong photochemical reactions of its precursor volatile organic compounds (VOCs) increase the difficulty of reducing VOCs emissions to alleviate O3. Here, we carried out a one-year comprehensive observation in two representative cities, Tianjin (TJ, Northern China) and Guangzhou (GZ, Southern China). By revealing the concentration characteristics of three different types of VOCs, i.e., initial VOCs without photochemical reaction (In-VOCs), consumed VOCs (C-VOCs), and measured VOCs after the reaction (M-VOCs), we elucidated the important role of C-VOCs in the formation of O3. Although the overall trends were similar in both cities, the average concentration level of VOCs in GZ was 8.2 ppbv higher than that in TJ, and the photochemical loss of VOCs was greater by 2.2 ppbv. In addition, various drivers affecting O3 generation from C-VOCs were specifically explored, and it was found that most alkenes of TJ were key substances for rapid O3 formation compared to aromatics of GZ. Meanwhile, favorable meteorological conditions such as high temperature (T > 31 °C in TJ, and T > 33 °C in GZ), low relative humidity (56% in TJ and 45% in GZ), and stable atmospheric environment (proper pressure and gentle wind speed) also contribute to the generation of O3. More importantly, we combined chemical kinetics and receptor model to quantify the three-type VOCs source contributions and assess the potential impact of C-VOCs sources on O3 production, thus proposing environmental abatement technologies corresponding to the three types of VOCs. The differences in the comparison results of the three-type VOCs highlight the need to reduce O3 pollution from C-VOCs sources, which provides insights for future clean air policies development.

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects’ mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.