Crystallization-induced formation of two-dimensional carbon nanosheets derived from sodium lignosulfonate for fast lithium storage.

Sodium lignosulfonate, an abundant natural resource, is regarded as an ideal precursor for the synthesis of hard carbon. The development of high-performance, low-cost and sustainable anode materials is a significant challenge facing lithium-ion batteries (LIBs). The modulation of morphology and defect structure during thermal transformation is crucial to improve Li+ storage behavior. Synthesized using sodium lignosulfonate as a precursor, two-dimensional carbon nanosheets with a high density of defects were produced. The synergistic influence of ice templates and KCl was leveraged, where the ice prevented clumping of potassium chloride during drying, and the latter served as a skeletal support during pyrolysis. This resulted in the formation of an interconnected two-dimensional nanosheet structure through the combined action of both templates. The optimized sample has a charging capacity of 712.4 mA h g-1 at 0.1 A g-1, which is contributed by the slope region. After 200 cycles at 0.2 A g-1, the specific charge capacity remains 514.4 mA h g-1, and a high specific charge capacity of 333.8 mA h g-1 after 800 cycles at 2 A g-1. The proposed investigation offers a promising approach for developing high-performance, low-cost carbon-based anode materials that could be used in advanced lithium-ion batteries.

Double/debiased machine learning for logistic partially linear model.

We propose double/debiased machine learning approaches to infer a parametric component of a logistic partially linear model. Our framework is based on a Neyman orthogonal score equation consisting of two nuisance models for the nonparametric component of the logistic model and conditional mean of the exposure with the control group. To estimate the nuisance models, we separately consider the use of high dimensional (HD) sparse regression and (nonparametric) machine learning (ML) methods. In the HD case, we derive certain moment equations to calibrate the first order bias of the nuisance models, which preserves the model double robustness property. In the ML case, we handle the nonlinearity of the logit link through a novel and easy-to-implement 'full model refitting' procedure. We evaluate our methods through simulation and apply them in assessing the effect of the emergency contraceptive pill on early gestation and new births based on a 2008 policy reform in Chile.