Precise Preparation of Size-Uniform Two-Dimensional Platelet Micelles Through Crystallization-Assisted Rapid Microphase Separation Using All-Bottlebrush-Type Block Copolymers with Crystalline Side Chains.

Polymer nanoparticles with low curvature, especially two-dimensional (2D) soft materials, are rich in functions and outstanding properties and have received extensive attention. Crystallization-driven self-assembly (CDSA) of linear semicrystalline block copolymers is currently a common method of constructing 2D platelets of uniform size. Although accompanied by high controllability, this CDSA method usually and inevitably requires a longer aging time and lower assembly concentration, limiting the large-scale preparation of nanoaggregates.

MgCo(OH)@C as an electrode for supercapacitors: effect of doping level on energy storage capability.

Incorporating non-electrochemically active elements (such as Zn and Mg) into the framework of active components can enhance structural stability, leading to improved cycling performance. However, limited research has been conducted on the impact of varying doping concentrations. In this study, we conducted a comprehensive analysis of how different levels of Mg doping in Co(OH) affect the supercapacitor performance.

Effect of ambient O on respiratory mortality and synergies with meteorological factors in Shenyang, China.

The impact of O on the respiratory system is a significant global problem. Nevertheless, there is insufficient information about its impact on respiratory disorders in northeast China. In this study, we used a generalized additive model (GAM) to determine the correlation between O concentrations and respiratory deaths based on the daily meteorological data, pollutant concentrations, and respiratory deaths from 2014 to 2016 in Shenyang, a typical city in northeast China.

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent.