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Current-induced spin–orbit torque (SOT) has attracted much attention due to its potential applications in energy-efficient logic, memory, and artificial neuron devices. In this work, we report an enhanced SOT efficiency in perpendicularly magnetized Pt/Sm/Co/Ta heterostructures by inserting a light rare-earth Sm layer with large spin–orbit coupling. A series of Ta/Pt/Sm/Co/Ta samples with the Sm layer thickness (tSm) of 0, 0.6, 1.2, and 1.6 nm were prepared using direct-current magnetron sputtering. Perpendicular magnetic anisotropy, SOT efficiency, and current-driven magnetization reversal were characterized using electrical transport methods based on the anomalous Hall effect. The experimental results indicated that the switching field and magnetic anisotropic field decreased monotonically with an increase in tSm, while the damping-like effective field and effective spin Hall angle ( θ S H eff ) gradually increased. It demonstrates that interface modification with a Sm layer can improve the SOT efficiency and reduce the pinning potential barrier. Owing to the enhanced SOT and reduced pinning field, the critical switching current density (Jc) exhibits a steady decline when increasing tSm. In particular, the lowest Jc of approximately 7.83 × 106 A/cm2 was obtained when tSm was 1.6 nm. X-ray photoelectron spectroscopy revealed that electron transfer occurred between the Co, Pt, and Sm layers, which may be primarily responsible for the enhanced SOT by interface alloying to effectively strengthen the spin Hall effect of Sm and/or Pt. Our results provide a strategy for improving SOT efficiency and reducing Jc by interface alloying in SOT-based spintronic devices. [ABSTRACT FROM AUTHOR]

Solar thermal storage ceramic materials use photothermal power generation technology to store heat energy, which is an important way to use clean energy and reduce carbon emissions. In this paper, MgAl2O4 ceramics were prepared by pressureless sintering with fused magnesia and α‐Al2O3 as the primary raw materials and TiO2 as the additive. The phase composition, microstructure, physical properties, thermal shock resistance, and thermal physical properties of MgAl2O4 thermal storage ceramics were studied. The results show that the main crystalline phase of B4 is MgAl2O4 and the secondary crystalline phase is MgTi2O5. Ti4+ is dissolved into MgAl2O4 lattice, which distorts the lattice, reduces activation energy, and promotes sintering. The sample B4 (69.73 wt% α‐Al2O3, 30.27 wt% fused magnesia, and 7 wt% TiO2) sintered at 1510°C had the best comprehensive physical properties, and its bending strength and bulk density were 89.41 MPa and 3.27 g/cm3, respectively. After 30 thermal shock cycles, the bending strength of the sample B4 is slightly lost. The thermal stress generated during the expansion and contraction of the grain volume led to the decrease of the bending strength. From room temperature to 1000°C, the specific heat capacity of sample B4 is.44–1.25 J/(g·K). The heat capacity increases with the increase of temperature, and the heat storage density increases. The heat storage density is 1218.75 kJ/kg (1000°C). Heat capacity increases with temperature due to intensified lattice vibration at high temperatures. Therefore, magnesia alumina spinel ceramics show great potential in solar thermal energy storage. [ABSTRACT FROM AUTHOR]

This study characterizes cancer and non-cancer risks due to inhalation exposure to volatile organic compounds (VOCs) in a border city of Windsor in southern Ontario, Canada, using hourly ambient concentrations collected from 17 November 2021 to 17 March 2023. The total incremental lifetime cancer risk (CR) due to benzene and ethylbenzene is 4.33 × 10−6, which is in the acceptable risk range of 1 × 10−6 to 1 × 10−4 used by the USEPA. The CR was higher in winter (5.20 × 10−6), followed by fall (4.32 × 10−6), spring (3.86 × 10−6), and summer (2.96 × 10−6), all in the acceptable range. The total chronic non-cancer risk (Hazard Quotient, HQ) of inhalation exposure to 16 VOCs was 0.0488, with a higher value in fall (0.0571), followed by winter (0.0464), and lower in spring (0.0454) and summer (0.0451), all in the safe level of below HQ = 1 used by the USEPA. The hazard index (HI) by organs was higher for the nervous system (0.0213), followed by the hematologic system and immune system (0.0165 each), but much lower for the other five target organs, i.e., the liver/kidney (1.52 × 10−4), developmental system (3.38 × 10−4), endocrine system and urinary system (2.82 × 10−4 each), and respiratory system (9.70 × 10−5). Similar hour-of-day trends were observed in the total CR, total HQ, and HI by organs with higher values in the early morning hours of 5:00–8:00 and lower values during 12:00 to 15:00. Benzene was the major contributor to both total CR (89%) and total HQ (34%) due to its high toxicity and high concentrations. Benzene, toluene, ethylbenzene, and xylenes (BTEX) contributed 100% of the total CR and 51% of the total HQ. Further, BTEX is the sole contributor to the HI for the hematologic system and immune system and the major contributor to the HI for the nervous system (39%) and developmental system (55%). Higher cancer and non-cancer risks were associated with the airmass from the east, southeast, and southwest of Windsor. [ABSTRACT FROM AUTHOR]

The electrochemical NOx reduction reactions, involving nitrate and nitrite reduction reactions (NO3−RR and NO2−RR), have emerged as promising approaches for both NO3− and NO2− removal, and ammonium (NH3) synthesis under ambient conditions. However, the incorporation and stabilization of sulfur dopants in the catalysts for efficient NOx reduction are rarely explored, leading to an unclear effect of sulfur on the NOx reduction mechanism. Herein, sulfur‐doped Cu2O (S‐Cu2O) nanoneedle arrays via in situ electrochemical treatment are synthesized. The S‐Cu2O catalyst possesses excellent durability and selectivity for NH3 over a wide range of potentials in NO3−RR, attaining a maximum NH3 Faradaic efficiency of 94% at −0.6 VRHE and a maximum NH3 yield as high as 1.06 mmol h−1 cm−2. In NO3−RR, the sulfur dopant can accelerate the step from NO2− to NH3, contributing superior performance in NO2−RR and assembled Zn−NO2− battery device. Density functional theory (DFT) calculations reveal that the presence of sulfur can enhance the initial step of *NO3 adsorption, lower the reaction barriers for the formation of *NHO intermediate, and activate the H2O dissociation process. The work sheds light on the role of sulfur in enhancing electrocatalytic performance and provides a unique perspective for understanding the NOx reduction mechanism. [ABSTRACT FROM AUTHOR]

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering. [ABSTRACT FROM AUTHOR]

In order to observe the status of tobacco leaves exactly in real time and set appropriate curing parameters accordingly in the process of flue-curing, a CNN-LSTM model for identifying tobacco flue-curing stages based on Convolutional Neural Network and Long Short-Term Memory Neural Network was developed. An image acquisition device composed of industrial cameras and light sources was used to collect tobacco image data during curing, and the acquired image data were categorized into 10 curing stages according to the color and texture changes of tobacco leaves. The CNN-LSTM model was verified and compared with three conventional recognition models: CNN, ResNet50 and Inception-ResNet v2. The results showed that: 1) For the 10 flue-curing stages, the recognition accuracies of the training and verification sets of the CNN-LSTM model reached 99.55% and 98.70%, respectively. The recognition accuracy and average F1 score of the test set reached 99.58% and 99.36%, respectively, which were 1.17 and 1.72 percentage points higher than those of the Inception-ResNet v2 model. 2) The number of samples which were incorrectly judged by the CNN-LSTM model was far less than that by the other three models, and most of them fell in adjacent curing stages. This method supports the rapid and accurate identification of tobacco flue-curing stages. [ABSTRACT FROM AUTHOR]