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FeNiCoCu high-entropy alloys (HEAs) demonstrate promising potential for widespread use in structural and functional applications. However, a thorough understanding of dynamic deformation processes in FeNiCoCu HEA is limited due to technological constraints in detecting real-time microstructural developments at the atomic level. This study examines the shock-induced plastic deformations in the equiatomic FeNiCoCu HEA, focusing on crystallographic orientation and particle velocity, using nonequilibrium molecular dynamics simulations. We obtained the P−V/V0, P−T, P−Up, and Us−Up Hugoniot relations and evaluated their anisotropy. The shock velocity, stress, and shear stress exhibit orientation dependence due to the differences in the plastic deformation mechanism. For shock loading along [100] orientations, dislocation dominates at lower shock intensities. However, a phase transition from face-centered-cubic (FCC) to body-centered-cubic becomes the primary plastic deformation at high shock intensity. For shock loading along [ 11 2 ¯ ] and [111] orientations, the generation of disordered structures and dislocation activities is revealed to play an important role in the development of localized plastic deformation. Moreover, the competition of disordered and hexagonal-close-packed (HCP) atoms is observed. The transition from FCC to disordered atoms provides nucleation sites for dislocations, and the slip of dislocations around disordered atoms leads to the formation of HCP structures. These findings are very helpful for learning the dynamic deformation behavior of FeNCoCu HEA. [ABSTRACT FROM AUTHOR]

Molybdenum (Mo) is a strategic metal for the manufacture of aerospace equipment, satellite components, and vehicle armor. Thus, understanding its behaviors under harsh conditions like shock compression is crucial for its practical utilization. Through molecular dynamic simulations, we have explored the mechanical responses and microstructural evolutions of nano-polycrystalline (NPC) Mo under different shock intensities, with grain sizes ranging from 5 to 33 nm. Our study reveals that grain size considerably influences the Hugoniot data and waveform of NPC Mo. NPC Mo with a smaller grain size exhibits higher compressibility and lower Hugoniot shear stress. As the grain size increases, the presence of a double-wave structure becomes more pronounced. Additionally, with the increase in shock intensity, there is a reduction in the shock front width. Significantly, when the shock stress ranges from approximately 60 to 100 GPa, twinning structures are detected in samples with grain sizes ranging from 10 to 33 nm. Moreover, the elevated temperature behind the shock wave further promotes detwinning reactions. When the shock stresses exceed 100 GPa, twinning–detwinning as well as amorphization-recrystallization become the predominant deformation mechanisms, almost unaffected by grain size. As the shock stress exceeds 250 GPa, the atoms in the samples become completely disordered. These findings provide new insights into the mechanical responses as well as the microstructural evolutions of NPC Mo under shock compression. [ABSTRACT FROM AUTHOR]

[Objective] Facing the global issue of “carbon peak and carbon neutrality”, this study aimed to predict and simulate carbon emissions from energy consumption, explore the feasibility of carbon peaking and carbon neutrality time, and provide theoretical basis for promoting emission reduction. [Methods] In this study, the carbon emission coefficient method was used to calculate the carbon emissions from energy consumption in China from 1986 to 2019. Considering factors from the five aspects of economy, society, environment, energy, and technology, an indicator system including 18 variables was established, and five main factors were screened out by Lasso regression. Based on three machine learning methods and Lasso regression, six carbon emission prediction models including support vector regression (SVR), random forest (RF), back propagation neural network (BP), Lasso-SVR, Lasso-RF, and Lasso-BP were constructed. The models were evaluated based on the mean squared error (MSE) and mean average error (MAE). Combined with the scenario analysis method to set four scenarios of new normalization, green low-carbon, 2 ℃ target, and 1.5 ℃ target, the best prediction model was selected to simulate and analyze China’s energy consumption carbon emissions from 2020 to 2060 under the four scenarios. [Results] Under the four scenarios, with the intensification of emission reduction efforts, China’s carbon peaking platform period is shortened. The time points of carbon peak during the platform period in China are 2035, 2030, 2026, and 2025 respectively, and the peak values are 9.580, 7.448, 6.723 and 6.523 Gt CO2e. Under the 2 ℃ and 1.5 ℃ scenarios, China can achieve carbon neutrality as scheduled. Under all four scenarios China may achieve the 2 ℃ temperature rise target, but under the first two scenarios China will not achieve the 1.5 ℃ target. [Conclusion] Under the four scenarios, the peak value and peak time of China’s energy consumption carbon emissions are different, and the possibility of achieving carbon neutrality is also different. In order to achieve the goals of carbon peak and carbon neutrality as soon as possible, China must promote the optimization of industrial structure and energy system structure, increase investment in green assets, and help the development of emission reduction with technological progress.

为高效鉴定食用油脂加工厂污水处理过程的气味组成，采用感官检验以臭阈值量化不同处理工序污水的臭味浓度，并采用气相色谱-离子迁移谱法（GC-IMS）对其挥发性化合物进行分析，并对鉴定出的挥发性化合物进行指纹图谱和主成分分析。结果表明：污水经过处理臭阈值明显降低；GC-IMS鉴定出43种化合物，基本均为含氧有机物，包括酮类、醇类、醛类、酯类、酸类及其他类；总体上各处理工序污水挥发性物质总数和臭阈值的变化规律一致；各处理工序挥发性化合物存在一定差异。综上，GC-IMS是分析食用油脂加工厂污水处理过程气味变化的有效方法。 In order to effectively identify odorants in wastewater treatment process in edible oil plant, the odor concentration of wastewater from different treatment processes was quantified by the threshold odor number(TON) through sensory examination, and the volatile organic compounds (VOCs) were identified using gas chromatography-ion mobility spectrometry (GC-IMS) and analysed by fingerprint plot and principal component analysis. The results showed that the TON of wastewater was significantly reduced after treatment. Forty-three compounds were identified by GC-IMS, all of which were basically oxygen-containing organic compounds including ketones, alcohols, aldehydes, esters, acids and others. The change rule of TON of wastewater was overall consistent with volatile compounds among the treatment processes. There was a variation in VOCs′ overallcomposition among the treatment processes. In conclusion, the GC-IMS technique is an efficient method for analyzing the odor variation of wastewater treatment in edible oil plant.