Manipulation of polymer foam structure based on CO_2-induced changes in polymer fundamental properties

Foaming of polymers with CO2 has attracted increasing attention in polymer processing studies. Some of the fundamental properties of polymer/CO2 systems is discussed in this short review, including solubility and diffusivity of CO2 in the polymer, polymer crystallization, interfacial tension between the polymer and the gas, and rheology of the CO2/polymers melt. These properties understandably affect the foaming process, and the structures of the foam products. Meanwhile, these properties can be changed via manipulation of CO2 in polymer. The proposed idea is to manipulate the foaming process and the foam structure by CO2-induced changes in these properties. Two cases from the authors＇ laboratory are presented for elucidating how to use the changes to manipulate the foaming process.

MXene quantum dots of Ti_(3)C_(2):Properties,synthesis,and energy-related applications

The emerging two-dimensional MXene-derived quantum dots(MQDs)have garnered considerable research interest owing to their abundant active edge atoms,excellent electrical conductivity,and remarkable optical properties.Compared with their two-dimensional(2D)counterpart MXene,MQDs with forceful size and quantum confinement effects exhibit more unparalleled properties and have considerably contributed to the advanced photocatalysis,detection,energy storage,and biomedicine fields.This critical review summarizes the fundamental properties of MQDs in terms of structure,electricity,and optics.The mechanism,characteristics,and comparisons of two typical synthesis strategies(traditional chemical method and novel fluorine-free or chemical-free method)are also presented.Furthermore,the similarities and differences between MQDs and 2D MXenes are introduced in terms of their functional groups,light absorption capacity,energy band structure,and other properties.Moreover,recent advances in the applications of MQD-based materials for energy conversion and storage(ECS)are discussed,including photocatalysis,batteries,and supercapacitors.Finally,current challenges and future opportunities for advancing MQD-based materials in the promising ECS field are presented.

From protonation & Li-rich contamination to grain-boundary segregation: Evaluations of solvent-free vs. wet routes on preparing Li_(7)La_(3)Zr_(2)O_(12) solid electrolyte

Garnet-type Li_(7)La_(3)Zr_(2)O_(12)(LLZO) has been recognized as a candidate solid electrolyte for high-safety Lianode based solid-state batteries because of its electro-chemical stability against Li-metal and high ionic conductivity. Solvent(e.g., isopropanol(IPA)) has been commonly applied for preparing LLZO powders and ceramics. However, the deterioration of the proton-exchange between LLZO and IPA/absorbed moisture during the mixing and tailoring route has aroused less attention. In this study, a solvent-free dry milling route was developed for preparing the LLZO powders and ceramics. For orthogonal four categories of samples prepared using solvent-free and IPA-assisted routes in the mixing and tailoring processes, the critical evaluation was conducted on the crystallinity, surficial morphology, and contamination of ascalcinated and as-tailored particles, the cross-sectional microstructure of green and sintered pellets,the morphology and electro-chemical properties of grain boundaries in ceramics, as well as the interfacial resistance and performance of Li anode based symmetric batteries. The wet route introduced Li-rich contaminations(e.g., Li OH·H)_(2)O and Li)_(2)CO)_(3)) onto the surfaces of LLZO particles and Li-Ta-O segregations at the adjacent and triangular grain boundaries. The LLZO solid electrolytes prepared through dry mixing in combination with the dry tailoring route without the use of any solvent were found to the optimal performance. The fundamental material properties in the whole LLZO preparation process were found, which are of guiding significance to the development of LLZO powder and ceramic production craft.

Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids

Viscosity is one of the most important fundamental properties of fluids.However,accurate acquisition of viscosity for ionic liquids(ILs)remains a critical challenge.In this study,an approach integrating prior physical knowledge into the machine learning(ML)model was proposed to predict the viscosity reliably.The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size,structure,and interactions of the ILs.Three strategies based on the residuals of the COSMO-RS model were created as the output of ML,where the strategy directly using experimental data was also studied for comparison.The performance of six ML algorithms was compared in all strategies,and the CatBoost model was identified as the optimal one.The strategies employing the relative deviations were superior to that using the absolute deviation,and the relative ratio revealed the systematic prediction error of the COSMO-RS model.The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set(R^(2)=0.9999,MAE=0.0325),reducing the average absolute relative deviation(AARD)in modeling from 52.45% to 1.54%.Features importance analysis indicated the average energy correction,solvation-free energy,and polarity moment were the key influencing the systematic deviation.