Designing and preparing high-performance lignin-based dispersants are crucial steps in realizing the value-added utilization of lignin on an industrial scale. Such process depends heavily on an understanding of the dispersion mechanism of lignin-based dispersants. Here, atomic force microscopy (AFM) is employed to quantitatively investigate the dispersion mechanism of a lignosulfonate/silica (LS/SiO2) system under different pH conditions. The results show that the repulsive force between SiO2 particles in LS solution is stronger than it is in water, resulting in better dispersion stability. The Derjaguin–Landau–Verwey–Overbeek (DLVO) formula as well as the DLVO formula combined with steric repulsion is utilized for the fitting of the AFM force/distance (F/D) curves between the SiO2 probe and substrate in water and in LS solution. Based on these fitting results, electrostatic and steric repulsive forces are respectively calculated, yielding further evidence that LS provides strong steric repulsion between SiO2 particles. Further studies indicate that the adsorbance of LS on SiO2 (Q), the normalized interaction constant (A), and the characteristic length (L) are the three critical factors affecting steric repulsion in the LS/SiO2 system. Based on the above conclusions, a novel quaternized grafted-sulfonation lignin (QAGSL) dispersant is designed and prepared. The QAGSL dispersant exhibits good dispersing performance for SiO2 and real cement particles. This work provides a fundamental and quantitative understanding of the dispersion mechanism in the LS/inorganic particle system and provides important guidance for the development of high-performance lignin-based dispersants.
Recent reports on the selective laser melting (SLM) process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products. Although the physical process of SLM in a vacuum has been investigated by high-speed imaging, the underlying mechanisms governing the heat transfer and molten flow are still not well understood. Herein, we first developed a mesoscopic model of SLM under variable ambient pressure based on our recent laser-welding studies. We simulated the transport phenomena of SLM 316L stainless steel powders under atmospheric and 100 Pa ambient pressure. For typical process parameters (laser power: 200 W; scanning speed: 2 m∙s-1; powder diameter: 27 μm), the average surface temperature of the cavity approached 2800 K under atmospheric pressure, while it came close to 2300 K under 100 Pa pressure. More vigorous fluid flow (average speed: 4 m∙s-1) was observed under 100 Pa ambient pressure, because the pressure difference between the evaporation-induced surface pressure and the ambient pressure was relatively larger and drives the flow under lower pressure. It was also shown that there are periodical ripple flows (period: 14 μs) affecting the surface roughness of the as-printed track. Moreover, the molten flow was shown to be laminar because the Reynolds number is less than 400 and is far below the critical value of turbulence; thus, the viscous dissipation is significant. It was demonstrated that under a vacuum or lower ambient pressure, the ripple flow can be dissipated more easily by the viscous effect because the trajectory length of the ripple is longer; thus, the surface quality of the tracks is improved. To summarize, our model elucidates the physical mechanisms of the interesting transport phenomena that have been observed in independent experimental studies of the SLM process under variable ambient pressure, which could be a powerful tool for optimizing the SLM process in the future.
Given the current global energy and environmental issues resulting from the fast pace of industrialization, the discovery of new functional materials has become increasingly imperative in order to advance science and technology and address the associated challenges. The boom in metal–organic frameworks (MOFs) and MOF-derived materials in recent years has stimulated profound interest in exploring their structures and applications. The preparation, characterization, and processing of MOF materials are the basis of their full engagement in industrial implementation. With intensive research in these topics, it is time to promote the practical utilization of MOFs on an industrial scale, such as for green chemical engineering, by taking advantage of their superior functions. Many famous MOFs have already demonstrated superiority over traditional materials in solving real-world problems. This review starts with the basic concept of MOF chemistry and ends with a discussion of the industrial production and exploitation of MOFs in several fields. Its goal is to provide a general scope of application to inspire MOF researchers to convert their focus on academic research to one on practical applications. After the obstacles of cost, scale-up preparation, processability, and stability have been overcome, MOFs and MOF-based devices will gradually enter the factory, become a part of our daily lives, and help to create a future based on green production and green living.
Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg•s−1). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.