ABSTRACT
The advancement of Zn-Se batteries has been hindered by significant challenges, such as the sluggish kinetics of Se cathodes, limited Se loading, and uncontrollable formation of Zn dendrites. In this study, a bidirectional optimization strategy is devised for both cathode and anode to bolster the performance of Zn-Se batteries. A novel bowl-in-ball structured carbon (BIBCs) material is synthesized to serve as a nanoreactor, in which tin-based materials are grown and derived in situ to construct cathodes and anodes. Within the cathode, the multifunctional host material (SnSe@BIBCs) exhibits large adsorption capacity for selenium, and demonstrates supreme catalytic properties and spatially confined characteristics toward the selenium reduction reaction (SeRR). On the anode, Sn@BIBCs displays triple-induced properties, including the zincophilic of the internal metallic Sn, the homogenized spatial electric field from the 3D spatial structure, and the curvature effect of the bowl-shaped carbon. Collectively, these factors induce preferential nucleation of Zn, ensuring its uniform deposition. As a result, the integrated Zn-Se battery system achieves a remarkable specific capacity of up to 603 mAh g-1 and an impressive energy density of 581 W kg-1, highlighting its tremendous potential for practical applications.
ABSTRACT
The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.
ABSTRACT
OBJECTIVE: conventional vascular risk factors (VRFs) are associated with cognitive impairment independent of stroke and detectable cerebral lesions. We used proton magnetic resonance spectroscopy ((1)H MRS) to examine the hypotheses that abnormal levels of brain metabolites may mediate the relationship between VRFs and cognitive impairment. METHODS: a group of 54 stroke-free subjects with various VRFs underwent comprehensive cognitive assessments and (1)H MRS scan of the left hippocampus and prefrontal cortex. We indirectly measured the concentrations of N-acetylaspartate (NAA), choline, inositol, creatine (Cr) and total concentrations of glutamate plus glutamine (Glx). VRFs were quantified by Framingham stroke risk profile (FSRP) score. Subjects were divided into low- (<10%), medium- (10-20%) and high-risk (>20%) groups according to their FSRP scores. Pearson and partial correlation analysis were used to investigate the correlation between FSRP scores and cognitive performance along with the brain metabolism. RESULTS: compared with subjects in low-risk group, high-risk group subjects had significantly poor performances on the tasks of working memory, delayed recall and executive function. In high-risk group, hippocampal Glx/Cr ratios and prefrontal NAA/Cr ratios were significantly lower than those in low-risk group. Lower prefrontal NAA/Cr ratios were associated with executive dysfunction, and lower hippocampal Glx/Cr ratios were associated with impaired delayed recall. CONCLUSION: abnormal concentrations of brain metabolites and decreased glutamate plus glutamine concentration may play an important role in the pathophysiology of VRF-associated cognitive impairment. Brain metabolites detected by (1)H MRS may serve as important markers for monitoring VRFs burden.
