氮、硫共掺杂的碳负载的钴@碳化钴:一种高效的非贵金属氧还原电催化剂

Development of high-efficiency and low-cost electrocatalysts for large-scale oxygen reduction reaction (ORR) remains a challenge. In this study, we employed melamine, trithiocyanuric acid, and cobaltous nitrate to fabricate a novel ORR electrocatalyst with cobalt and cobalt carbide supported on carbon co-doped with nitrogen and sulfur (hereafter referred to as MTC-0.1-900) by two-step pyrolysis. The MTC-0.1-900 was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, Brunauer-Emmett-Teller (BET) specific surface area analysis, and X-ray photoelectron spectroscopy (XPS). The electrochemical performance for ORR was investigated by cyclic voltammetry and linear sweep voltammetry in 0.1 mol·L^-1 KOH solutions. The results showed that the onset potential and half-wave potential of MTC-0.1-900 were 29 and 5 mV superior to the commercial Pt/C catalyst, respectively. After 12000 s operation at the potential of -0.3 V (vs Ag/AgCl), the current retention capacities of MTC-0.1-900 and Pt/C were 97.1% and 76.7%, respectively. MTC-0.1-900 also showed better methanol tolerance than Pt/C. These unique properties of MTC-0.1-900 provide us with an alternative for replacing or reducing the use of Pt catalyst in metal-air battery cathode materials.     

高分散钴氮共掺杂碳纳米纤维氧还原催化剂

过渡金属氮掺杂碳基催化剂已成为替代铂基氧还原反应(ORR)电催化剂的理想选择。本文通过静电纺丝技术制备了高比表面、高度分散的钴原子配位氮掺杂的碳纳米纤维催化剂(Co-N/C)。X射线衍射(XRD)和高分辨率透射电镜(HRTEM)结果证实Co元素高度分散于制备的Co-N/C催化剂中。X射线光电子能谱结果表明N元素主要以吡啶N和石墨N形式存在。该Co-N/C催化剂对ORR反应呈现出较高的电催化活性,其氧还原起始和半波电位分别为0.92 V和0.80 V(相对于标准氢电极),接近于商业化Pt/C催化剂的性能。以制备的Co-N/C催化剂作为阴极,25℃下锌空气燃料电池的开路电位1.54 V、最大功率密度达到了190 m V·cm~(-2)表明该催化剂具有良好的应用前景。     

TiO_2纳米管电极上电化学还原CO_2生成CH_3OH

采用原位阳极氧化-煅烧法制备TiO_2纳米管(TiO_2NTs)电极,运用X射线衍射(XRD)、电场发射扫描电子显微镜(FESEM)、X射线光电子能谱(XPS)、双电位阶跃测试等对制备电极进行表征,考察了其在0.1mol?L~(-1) KHCO_3水溶液中电化学还原CO_2的催化活性。结果表明TiO_2NTs电极上电化学还原CO_2的主产物为CH_3OH,CH_3OH由HCOOH和HCHO进一步还原而来。电极制备的最佳煅烧温度为450℃(TiO_2NTs-450),电解电位-0.56 V(vs RHE(可逆氢电极))时反应120 min后,生成CH_3OH的法拉第效率和分电流密度分别为85.8%和0.2 m A?cm~(-2)。与550和650℃煅烧的电极相比,TiO_2NTs-450电极具有更高的催化活性,归因于电极表面更多的三价钛活性位,有利于CO_2吸附,从而对·CO_2-起到稳定的作用,速率控制步骤转变为·CO_2-的质子化反应。     

无模板法水热合成CoTe及其可见光光催化还原CO_2性能

本文利用无模版水热法合成了碲化钴(CoTe)纳米催化剂。为了避免引入残炭,所有的反应物均为无机化合物。利用X射线衍射(XRD)、扫描电子显微镜(SEM)、紫外-可见(UV-Vis)吸收光谱和X射线光电子能谱(XPS)等技术对所制备的CoTe纳米材料进行了表征。结果表明,所得产物为具有六方结构的海绵状CoTe,且具有可见光(λ420 nm)光催化活性。当以N,N-二甲基乙酰胺(DMA)或纯水为溶剂时,二氧化碳(CO_2)均可被光催化还原为甲烷(CH_4),但产率较低。而在催化体系中加入牺牲剂三乙醇胺(TEOA)后,产物由CH_4变为一氧化碳(CO)。这些实验结果表明,溶剂和牺牲剂的引入均可影响CoTe纳米催化剂对CO_2的光催化还原性能。     

Edge agreement of second-order multi-agent system with dynamic quantization via the directed edge Laplacian

This work explores the edge agreement problem of second-order multi-agent systems with dynamic quantization under directed communication. To begin with, by virtue of the directed edge Laplacian, we propose a model reduction representation of the closed-loop multi-agent system depending on the spanning tree subgraph. Considering the limitations of the finite bandwidth channels, the quantization effects of second-order multi-agent systems under directed graph are considered. The static quantizers generally contain a fixed quantization interval and infinite quantization level, which are, to some extent, inefficient and impractical. To further reduce the bit depth (number of bits available) and to obtain better precision, the dynamic quantized communication strategy referring to zooming in-zooming out scheme is required. Based on the reduced model associated with the essential edge Laplacian, the asymptotic stability of second-order multi-agent systems under dynamic quantized effects with only finite quantization level can be guaranteed. Finally, the simulation of altitude alignment of micro air vehicles is provided to verify the theoretical results.     

Sufficient Dimension Reduction and Variable Selection for Large-p-Small-n Data With Highly Correlated Predictors

Sufficient dimension reduction (SDR) is a paradigm for reducing the dimension of the predictors without losing regression information. Most SDR methods require inverting the covariance matrix of the predictors. This hinders their use in the analysis of contemporary datasets where the number of predictors exceeds the available sample size and the predictors are highly correlated. To this end, by incorporating the seeded SDR idea and the sequential dimension-reduction framework, we propose a SDR method for high-dimensional data with correlated predictors. The performance of the proposed method is studied via extensive simulations. To demonstrate its use, an application to microarray gene expression data where the response is the production rate of riboflavin (vitamin B₂) is presented.     

Morphology-Directed Selective Production of Ethylene or Ethane from CO2 on a Cu Mesopore Electrode

The electrocatalytic conversion of CO₂ to value-added hydrocarbons is receiving significant attention as a promising way to close the broken carbon-cycle. While most metal catalysts produce C₁ species, such as carbon monoxide and formate, the production of various hydrocarbons and alcohols comprising more than two carbons has been achieved using copper (Cu)-based catalysts only. Methods for producing specific C₂ reduction outcomes with high selectivity, however, are not available thus far. Herein, the morphological effect of a Cu mesopore electrode on the selective production of C₂ products, ethylene or ethane, is presented. Cu mesopore electrodes with precisely controlled pore widths and depths were prepared by using a thermal deposition process on anodized aluminum oxide. With this simple synthesis method, we demonstrated that C₂ chemical selectivity can be tuned by systematically altering the morphology. Supported by computational simulations, we proved that nanomorphology can change the local pH and, additionally, retention time of key intermediates by confining the chemicals inside the pores.     

Nanostructured heterogeneous catalysts for electrochemical reduction of CO2

Electrochemical reduction of CO₂ provides a sustainable solution to address the intermittent renewable electricity storage while recycling CO₂ to produce fuels and chemicals. Highly efficient catalytic materials and reaction systems are required to drive this process economically. This Review highlights the new trends in advancing the electrochemical reduction of CO₂ by developing and designing nanostructured heterogeneous catalysts. The activity, selectivity and reaction mechanism are significantly affected by the nano effects in nanostructured heterogeneous catalysts. In the future, energy efficiency and current density in electrochemical reduction of CO₂ need to be further improved to meet the requirements for practical applications.