A level set based immersed boundary method for simulation of non-isothermal viscoelastic melt filling process

In this work, the polymer melt filling process is simulated by using a coupled finite volume and level-set based immersed boundary (LS-IB) method. Firstly, based on a shape level set (LS) function to represent the mold boundary, a LS-IB method is developed to model the complex mold walls. Then the non-isothermal melt filling process is simulated based on non-Newtonian viscoelastic equations with differ-ent Reynolds numbers in a circular cavity with a solid core, and the effects of Reynolds number on the flow patterns of polymer melt are presented and compared with each other. And then for a true polymer melt with a small Reynolds number that varies with melt viscosity, the moving interface, the temperature distributions and the molecular deformation are shown and analyzed in detail. At last, as a commonly used application case, a socket cavity with seven inserts is investigated. The corresponding physical quantities, such as the melt velocity, molecular deformation, normal stresses, first normal stress differ-ence, temperature distributions and frozen layer are analyzed and discussed. The results could provide some predictions and guidance for the polymer processing industry.     

Modified g-C3N4 derived from ionic liquid and urea for promoting visible-light photodegradation of organic pollutants

In this work, modified g-C₃N₄ was fabricated successfully by calcination of ionic liquid (IL) and urea. The addition of IL changed the polymerization mode of urea, induced the self-assembly of urea molecules, modified the morphological structure of the tightly packed g-C₃N₄, and extended the electron conjugation system. When using 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) as a modifier, the heteroatom Cl could be inserted into the g-C₃N₄ to optimize the electronic structure. The results of characterizations indicate that the unique structure of modified g-C₃N₄ has an expanded electron delocalization range, introduces an interlayer charge transmission channel, promotes the charge transmission, reduces the band gap, enhances the absorption of visible light, and inhibits electron-hole recombination. Modified g-C₃N₄ showed excellent photocatalytic performance for the degradation of rhodamine B and tetracycline. Furthermore, the effect of different anions in 1-butyl-3-methylimidazolium salts ([Bmim]Cl, [Bmim]Br, [Bmim][BF₄], and [Bmim][PF₆]) on the structure and function of g-C₃N₄ are discussed.     

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.     

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.     

铜-铈/ZSM-5分子筛催化剂制备及脱硝性能研究

氮氧化物是大气主要污染源之一,危害人体健康,并引发酸雨。随着环保法规的升级,氮氧化物排放浓度的限值要求越来越高。这要求脱硝工艺尤其是脱硝催化剂必须进一步提高脱硝性能。以ZSM-5分子筛为载体,以铜、铈为活性组分,制备脱硝催化剂活性组分;用铝胶将脱硝催化剂活性组分附着在蜂窝状陶瓷上,得到脱硝催化剂。与ZSM-5分子筛相比,脱硝催化剂活性组分增加了4.73%的铜（以CuO质量分数计）和5.40%的铈（以CeO₂质量分数计）,比表面积由287.04 m²/g降到275.05 m²/g,孔容、孔径未发生变化,酸性增强,晶型基本保持不变。脱硝催化剂最佳反应条件：反应时间≥60 min,反应温度≥250 ℃,空速≤10 200 h^-1。脱硝催化剂在最佳反应条件下的脱硝效率约为85%。脱硝催化剂能适应较低的反应温度,并且具有较高的脱硝效率。     

Effect and Evolution of Oxide Film in the HDH-Ti Powder Surface on Densification Behavior During Sintering

Metallurgical and Materials Transactions A - In this work, we used hydride-dehydride Ti (HDH-Ti) powders (with the oxygen levels of 0.17 and 0.51 wt pct, respectively) combined with microstructural...     

合理选择锻造工艺降低锻件制造成本

150 MN水压机柱塞锻件重70.67 t。在保证钢锭质量的前提下,选取93 t钢锭(钢锭利用率75.9%),通过KD锻造方法的压实作用,成功地生产出合格的柱塞锻件。     

粉末注射成形耐热不锈钢的工艺研究

以元素混合粉为原料,研究了耐热不锈钢的注射成形工艺.重点讨论了烧结温度、时间及气氛对耐热不锈钢致密度、力学性能及微观组织的影响,获得的最佳工艺为烧结温度1375℃,时间60 min,真空气氛.在此条件下制备了致密度98.12%、抗拉强度510 MPa、铁素体组织的PIM耐热不锈钢.     

Tensile behavior change depending on the varying tungsten content of W–Ni–Fe alloys

Tungsten heavy alloys (WHAs) are metal–metal composites consisting of nearly pure spherical tungsten particles embedded in a Ni–Fe–W or Ni–Co–W or Ni–Cu–W ductile matrix. In this dual phase alloy, there are several complicated relations between the ductile matrix and hard tungsten particles. The aim of this research was to examine the effect of varying tungsten content on the microstructure and mechanical properties of tungsten heavy alloys. The microstructural parameters (grain size, connectivity, contiguity and solid volume fraction) were measured and were found to have a significant effect on the mechanical properties of tungsten-based heavy alloys. The result shows that the binding strength between the W and the matrix phase has a major influence on the ductility of tungsten-based alloys. The larger this binding force is, the better the ductility is.     

金属粉末注射成形技术的最新研究动态与发展趋势

简要阐述了粉末注射成形技术的特点与应用 ,并从粉末的制备、粘结剂体系的设计、注射成形过程及其计算机模拟等几个方面阐明了粉末注射成形技术的最新研究动态 ,最后提出了粉末注射成形技术的重要发展趋势