Kinetic analysis of silicon-lithium alloying reaction in silicon single crystal using soft X-ray absorption spectroscopy.

Silicon has been considered to be one of the most promising anode active materials for next-generation lithium-ion batteries due to its large theoretical capacity (4200 mA h g-1, Li22Si5). However, silicon anodes suffer from degradation due to large volume expansion and contraction. To control the ideal particle morphology, an experimental method is required to analyze anisotropic diffusion and surface reaction phenomena. This study investigates the anisotropy of the silicon-lithium alloying reaction using electrochemical measurements and Si K-edge X-ray absorption spectroscopy on silicon single crystals. During the electrochemical reduction process in lithium-ion battery systems, the continuous formation of solid electrolyte interphase (SEI) films prevents the achievement of steady-state conditions. Instead, the physical contact between silicon single crystals and lithium metals can prevent the effect of SEI formation. The apparent diffusion coefficient and the surface reaction coefficient are determined from the progress of the alloying reaction analyzed by X-ray absorption spectroscopy. While the apparent diffusion coefficients show no clear anisotropy, the apparent surface reaction coefficient of Si (100) is more significant than that of Si (111). This finding indicates that the surface reaction of silicon governs the anisotropy of practical lithium alloying reaction for silicon anodes.

Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset.

This paper considers a partially linear regression model relating a right-censored response variable to predictors and an extra covariate with measured error. The main problem here is that censorship and measurement error problems need to be solved to estimate the model correctly. In this sense, we propose three modified semiparametric estimators obtained from local polynomial regression, kernel smoothing, and B-spline smoothing methods based on kernel deconvolution approach and synthetic data transformation. Here, kernel deconvolution technique is used to solve the measurement error problem in the model and synthetic data transformation is considered to add the effect of censorship to the estimation procedure, which is a very common method in the literature. The performances of the introduced estimators are compared in the detailed Monte-Carlo simulation study. In addition, Carotid endarterectomy data is used as real-world data example and results are presented. According to the results, it is seen that the deconvoluted local polynomial method gives more qualified estimates than other two methods.