提高金属有机骨架材料在二氧化碳热催化转化中的性能:策略及前景展望

近年来,大气中CO2的浓度不断增加,带来全球变暖等一系列严重后果,成为国际社会共同关注的环境问题.将CO2催化转化为高附加值化学品可有效降低其向大气中的排放,同时可实现其资源化利用,符合低碳社会的发展目标.目前,已有多种催化体系实现了CO2向不同化学品的转化.然而,由于CO2自身的热力学稳定性和动力学惰性,这些转化通常需要在苛刻的反应条件和较高能耗下进行.设计开发高效催化体系、实现温和条件下CO2的转化利用引起了工业界和学术界的广泛兴趣.金属有机骨架材料(MOFs)是一类由有机配体和金属中心通过配位键组装而成的有机-无机杂化材料,在很多方面展现出良好的应用性能.由于其结构的多样性、可设计性、高比表面积和多孔性等独特性质,MOFs在催化领域吸引了很多研究者的关注.其中,MOFs作为非均相催化剂在CO2热催化转化中表现出良好的应用前景,已实现多种CO2向高值化学品的转化路径.但这些催化体系也存在一些缺点,如有些MOFs材料在催化反应中稳定性差以及其微孔性对反应中的传质造成限制等.因此,设计稳定的MOFs和MOF-基材料并对其结构进行优化改性,从而在温和条件下实现高效的CO2转化具有重要意义.本文综述了提高MOFs在CO2热催化转化反应中性能的几种策略:(1)对MOFs结构中的配体进行设计,包括具有活性官能团的配体、活性配合物作为配体和引入混合配体设计多元MOF;(2)调节MOFs结构中的金属中心,设计混合金属中心和包含活性金属团簇的金属中心;(3)构筑多级孔MOFs;(4)设计MOF-基的复合材料,包括MOFs作为载体与金属纳米颗粒、活性配合物和聚合物构建复合材料;(5)利用MOFs作为前驱体制备MOF-基衍生物材料,重点阐述了如何增加MOFs作为非均相催化剂的催化活性位点以及在CO2转化反应中各位点之间的协同作用.此外,介绍了原位表征技术在MOF-基材料用于CO2固定和转化中的应用.最后,分析了MOF-基非均相催化材料在CO2热催化转化领域目前面临的问题和挑战,包括MOFs材料结构优化、催化机理研究和规模化制备等方面,并对未来的发展趋势进行了展望.     

Reglucosylation of the Benzoxazinoid DIMBOA with Inversion of Stereochemical Configuration is a Detoxification Strategy in Lepidopteran Herbivores

Benzoxazinoids are chemical defenses against herbivores and are produced by many members of the grass family. These compounds are stored as stable glucosides in plant cells and require the activity of glucosidases to release the corresponding toxic aglucones. In maize leaves, the most abundant benzoxazinoid is (2R)‐DIMBOA‐Glc, which is converted into the toxic DIMBOA upon herbivory. The ways in which three Spodoptera species metabolize this toxin were investigated. (2S)‐DIMBOA‐Glc, an epimer of the initial plant compound, was observed in the insect frass, and the associated glucosyltransferase activity was detected in the insect gut tissue. The epimeric glucoside produced by the insect was found to be no longer reactive towards plant glucosidases and thus cannot be converted into a toxin. Stereoselective reglucosylation thus represents a detoxification strategy in Spodoptera species that might help to explain their success as agricultural pests on benzoxazinoid‐containing crops.     

Reactions in Nitroimidazole Triggered by Low‐Energy (0–2 eV) Electrons: Methylation at N1‐H Completely Blocks Reactivity

Low‐energy electrons (LEEs) at energies of less than 2 eV effectively decompose 4‐nitroimidazole (4NI) by dissociative electron attachment (DEA). The reactions include simple bond cleavages but also complex reactions involving multiple bond cleavages and formation of new molecules. Both simple and complex reactions are associated with pronounced sharp features in the anionic yields, which are interpreted as vibrational Feshbach resonances acting as effective doorways for DEA. The remarkably rich chemistry of 4NI is completely blocked in 1‐methyl‐4‐nitroimidazole (Me4NI), that is, upon methylation of 4NI at the N1 site. These remarkable results have also implications for the development of nitroimidazole based radiosensitizers in tumor radiation therapy.     

Online Monitoring of Electrochemical Carbon Corrosion in Alkaline Electrolytes by Differential Electrochemical Mass Spectrometry

Carbon corrosion at high anodic potentials is a major source of instability, especially in acidic electrolytes and impairs the long‐term functionality of electrodes. In‐depth investigation of carbon corrosion in alkaline environment by means of differential electrochemical mass spectrometry (DEMS) is prevented by the conversion of CO₂ into CO₃^2?. We report the adaptation of a DEMS system for online CO₂ detection as the product of carbon corrosion in alkaline electrolytes. A new cell design allows for in situ acidification of the electrolyte to release initially dissolved CO₃^2? as CO₂ in front of the DEMS membrane and its subsequent detection by mass spectrometry. DEMS studies of a carbon‐supported nickel boride (Ni_xB/C) catalyst and Vulcan XC 72 at high anodic potentials suggest protection of carbon in the presence of highly active oxygen evolution electrocatalysts. Most importantly, carbon corrosion is decreased in alkaline solution.     

On the Relation between Prediction and Imputation Accuracy under Missing Covariates

Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the use of modern Machine-Learning algorithms for imputation. This originates from their capability of showing favorable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine-Learning-based methods for both imputation and prediction are used. We see that even a slight decrease in imputation accuracy can seriously affect the prediction accuracy. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as the coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study.     

Self-assembled alkanethiol structures on gold: A further insight into the origins of structural rearrangement phenomena

Self-assembled structures of alkanethiols that have been deposited on gold from ethanolic solutions are susceptible to both chemical and physical changes: ethanol provides a medium for the formation of S-alkyl hydrogen thiocarbonates and related compounds via reaction with dissolved, atmospheric, CO₂. Deposition from ethanolic solutions results in multilayered structures incorporating these compounds, which at room temperature are susceptible to time-dependent structural rearrangement and molecular migration.