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.     

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...     

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     

基于演化算法的复杂机械广义优化设计实现策略研究

针对复杂机械广义优化设计过程中优化模型不易获得这一难题，利用人工神经网络的高度非线性是非线性映射能力，将优化建模转换为求取设计变量到设计方案的映射关系，构建了复杂机械广义优化柔性建模方法；引入模糊评价理论，设计了基于演化计算的优化方法，给出了优化流程图。该策略特别适用于复杂机械概念设计阶段的方案优化。     

注射坯成形质量与尺寸精度的控制模型

注射坯的尺寸精度反映注射坯质量的好坏，并对控制其后脱脂和烧结过程缺陷和最终产品尺寸精度起着很关键的作用。文章基于烧结收缩各向同性的前提下，提出了通过注射坯质量偏差控制注射坯尺寸精度和注射坯质量的数学模型，并在实验中验证了这一模型的正确性。     

矿山磨光球用钢XK5159H的生产试制

XK5159H钢连铸连轧棒材用于制造矿山磨光球，对于钢的纯净度、低倍组织、探伤结果及DI值等均有严格的要求，很难控制。我们通过制定内控化学成分及合理的工艺路线，使生产试制一次成功。