低温水等离子体活化和表面接枝DMAE聚氯乙烯中空纤维膜研究

采用低温水等离子体技术，在三通道聚氯乙烯(PVC)膜表面接枝了甲基丙烯氧基苄基二甲基氯化铵(DMAE)单体，增强了膜亲水和抗菌性能。通过红外分析，表明DMAE成功接枝到了PVC膜上，水通量提高两倍，PVC-ir-H₂O膜(通过水等离子体处理的膜)对牛血清蛋白(BSA)的吸附能力下降67%，对BSA溶液的通量从7.7提高至40 kg?m^-2?h^-1，并且对BSA的截留能力不变。通过静态及动态抗菌实验，接枝后的PVC膜（PVC-g-PMAE膜）抗菌率达到100%，膜组件运行中的抗菌率也达到82%以上。在保证细菌截留率100%的同时，其渗透通量提高三倍。该膜表面修饰工程技术能实现膜表面的均一化改性，且绿色环保、操作简便、成本低，改性膜在饮用水处理领域，尤其是家用净水器中展现了很好的应用前景。     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

生物质源多孔碳制备及其对废水中药物吸附研究进展

城市现代化与工业化的快速发展给环境造成的污染日趋严重，尤其以水体污染最为明显。近年来，工业废水中药物的含量逐年上升，污染程度已经不容忽视。因此，开发新型多孔材料用于废水中药物分子的吸附分离，已经成为了当前的研究热点。综述了近年来生物质源多孔碳（生物炭）在废水中污染物吸附分离方面的研究，首先简单介绍了废水中污染物的治理方法，在此基础上重点探讨了生物炭的制备与修饰，并结合碳材料表面化学性质与孔道结构，总结并展望了生物炭对药物的吸附性能。     

四川某稀土矿选矿试验研究

针对四川某稀土矿选矿工艺存在的稀土回收率低的问题，开展了一系列选矿试验研究。试验结果表明，在矿浆质量浓度为30%，粗选NaOH用量为600g/t，水玻璃用量为3000g/t，EF1106用量为3500g/t，起泡剂用量为60g/t情况下，采用一粗、三精、两扫、中矿按顺序返回的选矿流程，可获得稀土REO的品位和回收率分别为66.32%和79.88%的稀土浮选精矿。与该选厂的生产指标相比，稀土回收率提高了13.93%。     

Analysis of Phytochemical Composition of Camellia oleifera Oil and Evaluation of its Anti-Inflammatory Effect in Lipopolysaccharide-Stimulated RAW 264.7 Macrophages

In China, Camellia oleifera oil (COO) is not only a common edible oil but also a traditional remedy widely applied to ameliorate a variety of illnesses associated with inflammation, such as mouth ulcers, thrush, eczema, etc. However, there has been a lack of relevant biological research on the anti-inflammatory capacity of COO, and the specific bioactive lipid phytochemicals contributing to the anti-inflammatory effect need further research. In this study, the RAW 264.7 macrophages model was used to investigate the anti-inflammatory capacity of COO. Our data showed that 33–200 μg/mL COO markedly inhibited the lipopolysaccharide lipopolysaccharide (LPS)-stimulated nitric oxide (NO.) secretion via the suppression of Nos2 and Cox-2 expression. The enzyme immunoassay confirmed that COO also exhibited a strong suppressive effect on the expression of proinflammatory cytokines such as Tnf-α and Il-6. To further explore the correlation between the anti-inflammatory effects and the lipid phytochemicals in COO, 10 samples were collected and screened for their chemical compositions. It was interestingly demonstrated that the polyphenol extracts of COO play a vital role in its anti-inflammatory properties. In addition, an oil-in-water (O/W) emulsion-based system was also developed to deliver the liposoluble COO into the cells, and the feasibility of this system was confirmed. Our research confirms the anti-inflammatory potential of COO and highlights that the main functional ingredient is polyphenol extracts. This may provide a scientific basis for the comprehensive utilization and development of COO and related functional foods.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.