Composite corrosion inhibitors for secondary alkaline zinc anodes

The corrosion inhibition property of PEG600 and In(OH)3 as composite corrosion inhibitors for secondary alkaline zinc electrodes was studied, and the inhibition efficiency was determined as 81.9%. The research focused on the mechanism by the methods of electrochemical impedance spectroscopy, polarization curves and IR spectroscopy. The results indicate that the corrosion inhibition effectiveness is attributed to the joint inhibition of anodic zinc dissolution and cathodic hydrogen evolution. And the anodic process is depressed to a greater extent than the cathodic process. The synergistic mechanism of the composite inhinbitors proves to be the enhancement of adsorption of PEG600 by In(OH)3. Potentiostatic experiment results and SEM images verify the inhibition of dendritic growth by the composite inhibitors.     

Preparation of self-healing protective films on a zinc electrode treated in a cerium(III) nitrate solution and modified with sodium phosphate and cerium(III) nitrate

Self-healing protective films were prepared on a zinc electrode previously treated in a Ce(NO₃)₃ solution by modification with Na₃PO₄ and Ce(NO₃)₃. The protective and self-healing abilities of the films were examined by polarization measurements and observation of pit formation after the electrode was scratched with a knife-edge crosswise and immersed in oxygenated 0.5 M NaCl at 30 °C for many hours. Mechanisms of the protective and self-healing activities were discussed using X-ray photoelectron spectroscopy and electron-probe microanalysis. The protective and self-healing abilities of the films prepared on the zinc electrode by treatment in the Ce(NO₃)₃ solution and modification with Na₃PO₄ plus Ca(NO₃)₂, Mg(NO₃)₂, Zn(NO₃)₂ or Ce(NO₃)₃ were summarized.     

缓蚀阻垢剂的筛选及工业应用

针对现场循环冷却水的水质情况，研究了一种适用于循环冷却水的缓蚀阻垢剂配方，通过正交试验筛选出了一组最佳的浓度，使用该配方的缓蚀率达到82．56％。经静态阻垢试验和缓蚀评定试验分析，结果表明该配方有良好的缓蚀阻垢性能。当药剂配方浓度达50mg／L时，其缓蚀率和阻垢率均达到90％以上。在现场循环冷却水系统的应用中，对改善循环水水质也有很好的效果。     

异喹啉及羟基、羧基衍生物缓蚀作用的量子化学研究

用电化学方法实测了异喹啉及羟基、羧基两类取代衍 生物的缓蚀效率,并用HMO、CNDO方法进行量子化学计算.计算表明:随着N原子负的净电荷的 减小,缓蚀效率提高;随着异喹啉等分子的吡啶环负净电荷的增大,缓蚀效率增高.这些缓蚀 剂随着N原子自由价的增大,缓蚀效率降低;而随着吡啶环自由价之和的增加,缓蚀效率提高. 在HCl溶液中提出了这些缓蚀剂可能以平卧方式吸附于金属表面的吸附模型.     

基于Levy飞行的改进菌群觅食算法

针对经典菌群觅食算法因固定趋化步长导致的求解精度不高、收敛性能差等缺陷,提出一种基于Levy飞行的菌群觅食算法,其特点是利用基于Levy分布的趋化步长改善算法的求解精度与收敛性能,借助Levy飞行随机游走策略改善细菌迁徙位置.多个基准测试函数的实验结果表明,该算法在求解质量和收敛性能上均取得了较好的改进效果.     

Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

我国细菌耐药问题的现状和防控策略

细菌耐药危机日趋严峻,已经成为全球范围内的重大公共安全问题,严重危害人类、动物的健康和生态环境。本文总结了我国各领域细菌耐药问题的现状,分析了加剧细菌耐药性的直接原因,揭示了细菌耐药问题对国民健康、食品安全、生态环境、经济发展等可能造成的危害,整理了我国目前开展的防控行动以及取得的主要成果,最后针对我国细菌耐药防控在耐药监测、抗生素监管、危害风险评估、人才队伍建设等方面的不足,提出了多领域合作、多部门联防联控的细菌耐药防控策略。