铸件在凝固过程中的传热和自然对流计算

论文中以流函数、涡度和温度为基本变量,建立了铸件凝固时的传热和自然对流的数学模型。控制方程采用半隐式有限差分在固定网格上求解。在模型建立过程中,着重讨论了潜热的处理和固液相区(界面)速度条件。对于潜热处理,采用了基于合金平衡相图的比热焓法;对于固液相区(界面)速度条件,提出了一种新的、基于固相率进行修正的处理方法。另外还探讨了将本文提出的解法应用于复杂几何条件的可能性。最后,通过对一个典型例子进行计算,对自然对流对铸件凝固的影响作了深入的讨论。     

选矿产品矿物自动分析的光片制备

基于扫描电子显微镜的矿物自动分析仪(Quantitative Evaluation of Minerals by Scanning Electronic Microscopy)、MLA (Mineral Liberation Analyser)和AMICS(Advanced Mineral Identification and Characterization System)可用于测定选矿产品中目的矿物的粒度和单体解离度,为确定合理的磨矿细度以及优化选矿工艺流程提供依据。环氧树脂光片的制备是矿物自动识别和测量的最关键的一环,其代表性直接关系到后续数据测量的准确性和真实性。对于金属矿产品来说,由于选矿产品中矿物颗粒粗细不均、密度差异较大,在环氧树脂胶结固化过程中矿物颗粒会产生明显的分异作用并互相黏连,造成分析结果失真。实验证明,把样品与晶质石墨混均,然后加入环氧树脂以及固化剂搅拌混合倒入圆柱状模具进行冷镶嵌,待样品固化后再沿圆柱体的纵向进行切割,并对其切割面进行粗磨、细磨、精磨以及抛光,就可以制备出样品分散性好、分布均匀、表面光滑平整的具有代表性的环氧树脂光片。     

Catalyst-Free Photochemical Activation of Peroxymonosulfate in Xanthene-Rich Systems for Fenton-Like Synergistic Decontamination: Efficacy of Proton Transfer Process

Catalyst-free visible light assisted Fenton-like catalysis offers opportunities to achieve the sustainable water decontamination, but the synergistic decontamination mechanisms are still unclear, especially the effect of proton transfer process (PTP). The conversion of peroxymonosulfate (PMS) in photosensitive dye-enriched system was detailed. The photo-electron transfer between excited dye and PMS triggered the efficient activation of PMS and enhanced the production of reactive species. Photochemistry behavior analysis and DFT calculations revealed that PTP was the crucial factor to determine the decontamination performance, leading to the transformation of dye molecules. The excitation process inducing activation of whole system was composed of low energy excitations, and the electrons and holes were almost contributed by LUMO and HOMO. This work provided new ideas for the design of catalyst-free sustainable system for efficient decontamination.     

Recent Progress on Electrode Design for Efficient Electrochemical Valorisation of CO2, O2, and N2

CO₂ reduction, two-electron O₂ reduction, and N₂ reduction are sustainable technologies to valorise common molecules. Their further development requires working electrode design to promote the multistep electrochemical processes from gas reactants to value-added products at the device level. This review proposes critical features of a desirable electrode based on the fundamental electrochemical processes and the development of scalable devices. A detailed discussion is made to approach such a desirable electrode, addressing the recent progress on critical electrode components, assembly strategies, and reaction interface engineering. Further, we highlight the electrode design tailored to reaction properties (e.g., its thermodynamics and kinetics) for performance optimisation. Finally, the opportunities and remaining challenges are presented, providing a framework for rational electrode design to push these gas reduction reactions towards an improved technology readiness level (TRL).     

Deep learning-based on-line image analysis for continuous industrial crystallization processes

In situ microscopic imaging is a useful tool in monitoring crystallization processes, including crystal nucleation, growth, aggregation and breakage, as well as possible polymorphic transition. To convert the qualitative information to be quantitative for the purpose of process optimization and control, accurate analysis of crystal images is essential. However, the accuracy of image segmentation with traditional methods is largely affected by many factors, including solid concentration and image quality. In this study, the deep learning technique using mask region-based convolutional neural network (Mask R-CNN) is investigated for the analysis of on-line images from an industrial crystallizer of 10 m³ operated in continuous mode with high solid concentration and overlapped particles. With detailed label points for each crystal and transfer learning technique, two models trained with 70,908 and 7,709 crystals respectively are compared for the effect of training data amount. The former model effectively segments the aggregated and overlapped crystals even at high solid concentrations. Moreover, it performs much better than the latter one and traditional multi-scale method both in terms of precision and recall, revealing the importance of large number of crystals in deep learning. Some geometrical characteristics of segmented crystals are also analyzed, involving equivalent diameter, circularity, and aspect ratio.     

Intensifying extraction of biomolecules from macroalgae using vortex based hydrodynamic cavitation device

Macroalgae have a tremendous potential to become an important renewable resource for valuable biomolecules and chemicals. New and improved ways of cell disruption and of enhancing rate as well as yield of extraction of valuable products from macroalgae are needed to fully realise this potential. In this work, hydrodynamic cavitation (HC) was used for intensifying rate and yield of extraction of phycoerythrin, proteins and carbohydrates from marine macroalgae Palmaria palmata. We use vortex-based HC devices which do not use small restrictions like orifice-based HC devices or moving parts like rotor–stator based HC devices. A bench scale setup with a nominal slurry flow rate of 20 LPM was established. Dried and powdered macroalgae was used. Influence of key operating parameters like pressure drop and number of passes on extraction performance (the rate and yield) was measured. A simple, yet effective model was developed and used for interpreting and describing experimental data. The results indicate that there exists an optimum pressure drop across the device at which extraction performance is maximum. The extraction performance with HC was found to be significantly better than the stirred vessels. HC has resulted in 2 to 20 times improvement in the rate of extraction of phycoerythrin (R-PE), proteins and carbohydrates. Based on the results obtained in this work, pressure drop of 200 kPa and number of passes through the HC devices of about 100 were found to be most effective for HC-assisted intensified extraction from macroalgae. The presented results and model will be useful for harnessing vortex-based HC devices for intensifying the extraction of valuable products from macroalgae.     

Regulation of d-Band Centers in Localized CdS Homojunctions through Facet Control for Improved Photocatalytic Water Splitting

The accelerated kinetic behaviour of charge carrier transfer and its unhindered surface reaction dynamic process involving oxygenated-intermediate activation and conversion are urgently required in photocatalytic water (H₂O) overall splitting, which has not been nevertheless resolved yet. Herein, localized CdS homojunctions with optimal collocation of high and low index facets to regulate d-band center for chemically adsorbing and activating key intermediates (*-OH and *-O) have been achieved in H₂O overall splitting into hydrogen. Density functional theory, hall effect, and in situ diffuse reflectance infrared Fourier transform spectroscopy confirm that, electrons and holes are kinetically transferred to reductive high index facet (002) and oxidative low index facet (110) of the localized CdS homojunction induced by facet Fermi level difference to dehydrogenate *-OH and couple *-O for hydrogen and oxygen evolution, respectively, along with a solar conversion into hydrogen (STH) of 2.20 % by Air Mass 1.5 Global filter irradiation. These findings contribute to solving the kinetic bottleneck issues of photocatalytic H₂O splitting, which will further enhance STH.     

Modelling Development in Radical (Co)Polymerization of Multivinyl Monomers

Radical polymerization (RP) of multivinyl monomers (MVMs) provides a facile solution for manipulating polymer topology and has received increasing attention due to their industrial and academic significance. Continuous efforts have been made to understand their mechanism, which is the key to regulating materials structure. Modelling techniques have become a powerful tool that can provide detailed information on polymerization kinetics which is inaccessible by experiments. Many publications have reported the combination of experiments and modelling for free radical polymerization (FRP) and reversible-deactivation radical polymerizations (RDRP) of MVMs. Herein, a minireview is presented for the most important modelling techniques and their applications in FRP/RDRP of MVMs. This review hopes to illustrate that the combination of modelling and wet experiments can be a great asset to polymer researchers and inspire new thinking for the future MVMs experiment optimization and product design.