±800kV直流输电线路重冰区导、地线脱冰冲击敏感因素浅析.

结合±800 k V直流输电线路重冰区的实际工程数据,基于脱冰数值计算理论,通过数值方法模拟了六分裂导线在不同档数、档距、高差等复合因素影响下导线的脱冰动力响应过程,分析不同参数条件下导线和地线脱冰对悬垂绝缘子串和耐张绝缘串的冲击效应,得到最不利脱冰冲击效应的控制因素。研究表明:不同档距和高差组合下,档距和高差均最大时脱冰后的冲击效应最强;对于连续档,不同档脱冰时,对其相邻档导地线串的冲击效应最大;脱冰水平冲击效应主要受水平档距的影响,垂直冲击效应主要受高差的影响。     

ZrO2制备方法研究进展

以ZrO₂为活性组分或载体的催化剂,在酚类定向催化转化中具有较高的研究价值。总结了ZrO₂的各种制备方法,包括溶胶-凝胶法、共沉淀法、水热法、表面活性剂辅助法、静电纺丝法、热解法、多元醇还原法、反相微乳法和超临界合成法等。阐述了各种方法的反应原理,详细比较了不同制备方法的优劣,并具体分析了制备方法对催化剂结构及性能的影响。     

配电自动化模式下配电网保护的模式选择和整定计算

配电自动化设备大量使用在配电网中,目前配电网的继电保护配置只有在66 kV变电站10 kV出口配置,缺乏对配电网的整体准确计算,提出配电网故障电流的计算,基于故障电流对配电自动化设备继电保护定值进行配置,包括保护模式的选择、配电网网架的改造以及配电自动化设备的优化布局。     

国内外能源转型比较与启示

当前全球能源转型正处于加速推进时期,各国转型进程呈现出一定的差异化特征。基于对能源转型发展方向的剖析,提出涵盖能源结构、能源效率、能源安全、能源价格4个维度的能源转型评价指标体系。在此基础上,对中国、美国、德国、法国、日本、韩国等主要国家能源转型情况进行对比分析,进而提出对中国能源转型的结论与启示。     

室温下ZnO超疏水表面的制备及油水分离性能

利用简便的液相法,在室温下于不锈钢网上沉积ZnO纳米片和纳米花粗糙结构,接着通过浸渍法修饰低表面能物质硬脂酸,制备了超疏水不锈钢网。对沉积后的不锈钢网表面形貌、晶体结构、润湿性能、耐磨性能、油水分离性能等进行表征与测定。结果表明,该不锈钢网表面由纳米片和纳米花组成的微纳米结构ZnO构成,具有超疏水性,水接触角161 °;油水分离效率达98%,循环使用20次后分离效率仍保持在95.5%以上;具有良好的机械耐磨性,在高盐环境中表现出化学稳定性。     

负载活性组分的核壳结构纤维膜的制备及性能

采用同轴静电纺丝技术将蛛丝蛋白（Ss）和美洲大蠊提取物（PAE）分别负载于纳米纤维的壳层与核层。随着Ss的增加,纤维直径从350 nm降至280 nm,核层直径由120nm升至140 nm,壳层厚度由115 nm降至70 nm。Ss的加入使纳米纤维膜具有良好的机械性能和亲水性,纳米纤维膜的拉伸强度可达到4.31 MPa,溶胀率可达到150%,水蒸气透过率可达到1834 g/(m2?24h),水接触角减小到 32.7 ?。纳米纤维膜核壳结构能够有效抑制药物突释,实现药物长效释放,7天内药物释放可达77%;纳米纤维膜能够有效抑制细菌生长,促进细胞增殖,相较于未负载Ss的纳米纤维膜,负载20%Ss的纤维膜的细胞增殖效果提高25%,说明Ss和PAE在伤口愈合过程中能够起到协同作用。     

纳米颗粒强化胺法吸收CO2研究进展

碳捕集、利用与封存（CCUS）技术是缓解全球气候变化的重要措施之一。纳米颗粒因具有良好表面效应和体积效应,能够强化CO2捕集传质性能而备受青睐,其可增大胺法吸收CO2过程中的传质系数并减少溶剂再生所需的能量,有望进一步提高CO2捕集效率、降低能耗。本文从纳米颗粒强化胺法吸收CO2过程出发,讨论了纳米颗粒强化吸收的3种机理（抑制气泡聚并机理、传输机理、边界层混合机理）,总结了近年来国内外最新研究进展,论述了纳米颗粒强化CO2吸收过程中的主要影响因素,指出纳米颗粒对CO2吸收过程的强化是多重因素共同作用的结果,并对该技术的未来研究方向作了展望。     

N,N−二甲基−二硫代甲酰胺丙磺酸钠对THPED体系化学镀铜的影响

研究了N,N?二甲基?二硫代甲酰胺丙磺酸钠(DPS)作为添加剂对以四羟丙基乙二胺(THPED)为单一配位剂的化学镀铜体系的沉积速率、镀层形貌和晶体结构的影响。结果发现,当DPS的质量浓度从0 mg/L增大到1.0 mg/L时,沉积速率从2.91μm/h提高到6.73μm/h,所得镀层结晶均匀、细致。线性扫描伏安测量结果表明,DPS是通过促进甲醛的阳极氧化来加速化学镀过程。本体系的Cu镀层主要呈面心立方多晶取向,DPS的添加会令晶面取向从(220)转变为(111)。     

Artificial Intelligence in Aptamer–Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.     

Catalase (CAT) Gene Family in Rapeseed (Brassica napus L.): Genome-Wide Analysis,Identification, and Expression Pattern in Response to Multiple Hormones and Abiotic Stress Conditions

Catalase (CAT) is an antioxidant enzyme expressed by the CAT gene family and exists in almost all aerobic organisms. Environmental stresses induce the generation of reactive oxygen species (ROS) that eventually hinder plant growth and development. The CAT enzyme translates the hydrogen peroxide (H₂O₂) to water (H₂O) and reduce the ROS levels to shelter the cells’ death. So far, the CAT gene family has not been reported in rapeseed (Brassica napus L.). Therefore, a genome-wide comprehensive analysis was conducted to classify the CAT genes in the rapeseed genome. The current study identified 14 BnCAT genes in the rapeseed genome. Based on phylogenetic and synteny analysis, the BnCATs belong to four groups (Groups I–IV). A gene structure and conserved motif analysis showed that Group I, Group II, and Group IV possess almost the same intron/exon pattern, and an equal number of motifs, while Group III contains diverse structures and contain 15 motifs. By analyzing the cis-elements in the promoters, we identified five hormone-correlated responsive elements and four stress-related responsive elements. Further, six putative bna-miRNAs were also identified, targeting three genes (BnCAT4, BnCAT6, and BnCAT8). Gene ontology (GO) enrichment analysis showed that the BnCAT genes were largely related to cellular organelles, ROS response, stimulus response, stress response, and antioxidant enzymes. Almost 10 BnCAT genes showed higher expression levels in different tissues, i.e., root, leaf, stem, and silique. The expression analysis showed that BnCAT1–BnCAT3 and BnCAT11–BnCAT13 were significantly upregulated by cold, salinity, abscisic acid (ABA), and gibberellic acid (GA) treatment, but not by drought and methyl jasmonate (MeJA). Notably, most of the genes were upregulated by waterlogging stress, except BnCAT6, BnCAT9, and BnCAT10. Our results opened new windows for future investigations and provided insights into the CAT family genes in rapeseed.