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Ionic Microporous Polymer Membranes for Advanced Gas Separations
No full textMicroporous polymers are uniquely attractive for membrane-mediated gas separations; however, conventional microporous polymers suffer a ubiquitous trade-off between gas permeability and selectivity, leading to bottlenecks in their practical applications. Functionalization of microporous polymers via molecule engineering is an effective way to enhance their gas separation performance and processability. This review outlines the research progress of ionization to improve the gas separation performance of typical microporous polymers (e.g., polymers of intrinsic microporosity (PIM), perfluorinated polymers, microporous polyimides, etc.) and summarizes the different ionization methods, including carboxylation, sulfonation, quaternization, and other ionization processes. Additionally, the review also explores the research progress of ionization to regulate the processability, microporosity, and gas separation properties of microporous polymers. Specifically, ionization can effectively tailor the microporosity, improve the solubility coefficients of gas molecules, especially CO2, and enhance gas selectivities. In addition, ionization can improve the processability of PIMs and enhance the membrane plasticization resistance. Ionic microporous polymers provide an essential platform for developing energy-efficient and high-performance gas separation membranes
Experimental Study of Unsteady Drag Coefficient of Droplets in a Liquid-Liquid System
No full textIn liquid-liquid contact process, the motion of droplets relative to the surrounding fluid always involves accelerating and decelerating, which affects the mass, heat, and momentum transfer. The lack of experimental data of the unsteady drag coefficient has been one of the limitations on the prediction of the unsteady flow field. In this study, the accelerated and decelerated water droplets in an organic phase were measured by a high-speed camera. The results show that with a decrease in droplet diameter, the acceleration becomes more significant, while the absolute relative velocity decreases, causing a lower Reynolds number. The Basset force and add mass force were solved numerically and compared with drag force. The unsteady drag coefficient is always smaller than the corresponding steady drag coefficient in the case of accelerating relative flow and larger than that in the case of decelerating relative flow. A new unsteady drag coefficient model has been established, which has an acceptable agreement with the experimental data
Constructing BaTiO3/TiO2@polypyrrole composites with hollow multishelled structure for enhanced electromagnetic wave absorbing properties
No full textBaTiO3/TiO2@polypyrrole (PPy) composites with hollow multishelled structure (HoMS) were constructed to enhance the electromagnetic wave absorbing properties of BaTiO3-based absorbing material. BaTiO3/TiO2 HoMSs were prepared by hydrothermal crystallization using TiO2 HoMSs as template. Then, FeCl3 was introduced to initiate the oxidative polymerization of pyrrole monomer, forming BaTiO3/TiO2@PPy HoMSs successfully. The electromagnetic wave absorbing properties of BaTiO3/TiO2 HoMSs and BaTiO3/TiO2@PPy HoMSs with different shell number were investigated using a vector network analyzer. The results indicate that BaTiO3/TiO2@PPy HoMSs exhibit improved microwave absorption compared with BaTiO3/TiO2 HoMSs. In particular, tripled-shelled BaTiO3/TiO2@PPy HoMS has the most excellent absorbing performance. The best reflection loss can reach up to -21.80 dB at 13.34 GHz with a corresponding absorber thickness of only 1.3 mm, and the qualified absorption bandwidth of tripled-shelled BaTiO3/TiO2@PPy HoMS is up to 4.2 GHz. This work paves a new way for the development of high-performance composite microwave absorbing materials
State Key Laboratory of Catalytic Materials and Reaction Engineering (RIPP, SINOPEC)[33600000-20-ZC0607-0024]
No full textA Liquid Crystal Ionomer-Type Electrolyte toward Ordering-Induced Regulation for Highly Reversible Zinc Ion Battery
No full textNovel electrolyte is being pursued toward exploring Zn chemistry in zinc ion batteries. Here, a fluorine-free liquid crystal (LC) ionomer-type zinc electrolyte is presented, achieving simultaneous regulated water activity and long-range ordering of conduction channels and SEI. Distinct from water network or local ordering in current advances, long-range ordering of layered water channels is realized. Via manipulating water activity, conductivities range from approximate to 0.34 to 15 mS cm(-1), and electrochemical window can be tuned from approximate to 2.3-4.3 V. The Zn|Zn symmetric cell with LC gel exhibits highly reversible Zn stripping/plating at 5 mA cm(-2) and 5 mAh cm(-2) for 800 h, with retained ordering of water channels. The capability of gel for inducing in situ formation of long-range ordered layer SEI associated with alkylbenzene sulfonate anion is uncovered. V2O5/Zn cell with the gel shows much improved cycling stability comparing to conventional zinc electrolytes, where the preserved structure of V2O5 is associated with the efficiently stabilized Zn anode by the gel. Via long-range ordering-induced regulation on ion transport, electrochemical stability, and interfacial reaction, the development of LC electrolyte provides a pathway toward advancing aqueous rechargeable batteries
Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models
No full textPulsed disk and doughnut column (PDDC) is widely applied in liquid-liquid solvent extraction. Due to a nonlinear and complex mechanism in PDDC, existing single empirical models often fail to predict the performance of different PDDCs. In this work, machine learning (ML) models such as random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are developed to predict the PDDC's performance including dispersed-phase holdup (xd), drop size (d32), axial diffusion coefficient (Ec) and the height of mass transfer unit (Hoc). ML models were trained based on a comprehensive dataset and the results showed that the prediction performances of the ML models are better than the empirical correlations. The best average absolute relative error (AARE) and correlation coefficient (R2) of d32, xd, Ec and Hoc were 3.97% and 0.99, 10.16% and 0.955, 12.71% and 0.973, 13.44% and 0.982, respectively. RF and SVM exhibited the highest predictive accuracy. Furthermore, the feature importance was determined, which indicated the most significant features for d32, xd, Ec and Hoc were pulse intensity, the velocity of dispersed phase, the velocity of continuous phase and the properties of continuous phase, respectively. This study provided a new perspective to model and design PDDC