Fabrication of Si negative electrodes for Li-ion batteries (LIBs) using cross-linked polymer binders

MXene Clay (Ti2C)-Containing In Situ Polymerized Hollow Core-Shell Binder for Silicon-Based Anodes in Lithium-Ion Batteries

Silicon, an attractive anode material, suffers fast capacity fading due to the electrical isolation from massive volumetric expansion upon cycling. However, it holds a high theoretical capacity and low operation voltage in its practical application. In this study, a new water-based binder, MXene clay/hollow core-shell acrylate composite, was synthesized through an in situ emulsion polymerization technique to alleviate the fast capacity fading of the silicon anode efficiently. The efficient introduction of conductive MXene clay and the hollow core-shell structure, favorable to electron and ion transport in silicon-based electrodes, gives a novel conceptual design of the binder material. Such a strategy could alleviate electrical isolation after cycling and promises better electrochemical performance of the high-capacity anodes. The effect of the MXene introduction and hollow core-shell on the binder performance is thoroughly investigated using various characterization tools by comparison with no MXene-containing, core-shell acrylate, and commercial styrene-butadiene latex binders. Consequently, the silicon-based electrode containing the MXene clay/hollow core-shell acrylate binder exhibits a high capacity retention of 1351 mAh g-1 at 0.5C after 100 cycles and good rate capability of over 1100 mAh g-1 at 5C.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.