Co-Doped MoSe2 Nanoflowers as Efficient Catalysts for Electrochemical Hydrogen Evolution Reaction (HER) in Acidic and Alkaline Media

Transition metal dichalcogenides (TMDCs) based materials are considered as highly active alternatives to the precious Pt-based catalysts for the hydrogen evolution reaction (HER). In particular, MoSe₂ emerges as a promising catalyst due to its abundance and electrochemical stability, but further modifications are still required to enhance its performance, specifically in alkaline conditions. Here, we developed a method to obtain MoSe₂ with two cobalt doping patterns: homogeneously doped and edge doped nanoflowers, with abundant edge sites and extended surface area. The results show that low concentration of doping enhances the catalytic activity toward HER. Incorporation of cobalt as a substituent dopant within the layered structure of MoSe₂ appears to have two major contributions: it changes the chemical environment providing more active sites with favored hydrogen adsorption properties, and improves the charge transfer resistance and thus facilitates the HER kinetics. Moreover, the homogeneous and edge-doped nanoflowers show different pH-dependence of HER activity. Edge-doped samples exhibit significantly improved performance in acidic medium, while the overpotential increases in alkaline environment upon doping. A mechanistic explanation of the observed effect is proposed. This work opens up an additional path for improving the catalytic activity of TMDCs in acidic or alkaline medium using a simple and facile method with only small quantities of dopants.     

Routine sagittal whole-spine magnetic resonance imaging in finding incidental spine lesions

Magnetic Resonance Materials in Physics, Biology and Medicine - To examine the efficacy and feasibility of T2-weighted whole-spine sagittal magnetic resonance imaging (MRI) screening for all...     

Peak oil in the light of oil formation theories

The peak oil debate is underpinned by a biological paradigm of oil formation that generates a notion of fixed oil and gas reserves in the Earth's crust. However, the potential of the abiogenic theory of oil formation is underestimated. We will demonstrate the value of modern petroleum science based on a thermodynamic understanding of hydrocarbon formation, and both experimental and observational data, which can introduce a change into the debate. Now emotionally charged and contentious, the debate will supposedly develop a balanced and realistic scenario on gradual oil transition and a secure global energy supply.     

Cryo-staining techniques in cryo-TEM studies of dispersed nanotubes

The combination of cryo-TEM and staining is employed for studying protein-enabled dispersion of carbon nanotubes in aqueous solution. The same staining agent is used for both positive- and negative-staining. We are able to image the adsorbed layer of protein or polysaccharide on the nanotube but not the individual molecule. The process is not artifact-free due to change in ionic strength of the solution. However, our results are in line with other, not related, experimental techniques. The obtained information could be used to update models suggested based on, e.g., scattering data.     

Vertex cover might be hard to approximate to within 2−ε

Based on a conjecture regarding the power of unique 2-prover-1-round games presented in [S. Khot, On the power of unique 2-Prover 1-Round games, in: Proc. 34th ACM Symp. on Theory of Computing, STOC, May 2002, pp. 767–775], we show that vertex cover is hard to approximate within any constant factor better than 2. We actually show a stronger result, namely, based on the same conjecture, vertex cover on k-uniform hypergraphs is hard to approximate within any constant factor better than k.     

Mining manufacturing databases to discover the effect of operation sequence on the product quality

Data mining techniques can be used for discovering interesting patterns in complicated manufacturing processes. These patterns are used to improve manufacturing quality. Classical representations of quality data mining problems usually refer to the operations settings and not to their sequence. This paper examines the effect of the operation sequence on the quality of the product using data mining techniques. For this purpose a novel decision tree framework for extracting sequence patterns is developed. The proposed method is capable to mine sequence patterns of any length with operations that are not necessarily immediate precedents. The core induction algorithmic framework consists of four main steps. In the first step, all manufacturing sequences are represented as string of tokens. In the second step a large set of regular expression-based patterns are induced by employing a sequence patterns. In the third step we use feature selection methods to filter out the initial set, and leave only the most useful patterns. In the last stage, we transform the quality problem into a classification problem and employ a decision tree induction algorithm. A comparative study performed on benchmark databases illustrates the capabilities of the proposed framework.     

An algorithm for partitioning trees augmented with sibling edges

We investigate a special case of the graph partitioning problem: the partitioning of a sibling graph which is an ordered tree augmented with edges connecting consecutive nodes that share a common parent. We describe the algorithm, XS, and present a proof of its correctness.