In situ imaging of catalytic etching on silver during methanol oxidation conditions by environmental scanning electron microscopy

Polycrystalline silver is used to catalytically oxidise methanol to formaldehyde. This paper reports the results of extensive investigations involving the use of environmental scanning electron microscopy (ESEM) to monitor structural changes in silver during simulated industrial reaction conditions. The interaction of oxygen, nitrogen, and water, either singly or in combination, with a silver catalyst at temperatures up to 973 K resulted in the appearance of a reconstructed silver surface. More spectacular was the effect an oxygen/methanol mixture had on the silver morphology. At a temperature of ca. 713 K pinholes were created in the vicinity of defects as a consequence of subsurface explosions. These holes gradually increased in size and large platelet features were created. Elevation of the catalyst temperature to 843 K facilitated the wholescale oxygen induced restructuring of the entire silver surface. Methanol reacted with subsurface oxygen to produce subsurface hydroxyl species which ultimately formed water in the subsurface layers of silver. The resultant hydrostatic pressure forced the silver surface to adopt a "hill and valley" conformation in order to minimise the surface free energy. Upon approaching typical industrial operating conditions widespread explosions occurred on the catalyst and it was also apparent that the silver surface was extremely mobile under the applied conditions. The interaction of methanol alone with silver resulted in the initial formation of pinholes primarily in the vicinity of defects, due to reaction with oxygen species incorporated in the catalyst during electrochemical synthesis. However, dramatic reduction in the hole concentration with time occurred as all the available oxygen became consumed. A remarkable correlation between formaldehyde production and hole concentration was found.

Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Validating SMPS-measured size distribution of double-mode spherical Silica nanoparticles by Transmission Electron Microscope (TEM)

A nanoparticles size is one of their key physical characteristics that can affect their fate in a human’s respiratory tract (in case of inhalation) and also in the environment. Hence, measuring the size distribution of nanoparticles is absolutely essential and contributes greatly to their characterization. For years, Scanning Mobility Particle Sizers (SMPS), which rely on measuring the electrical mobility diameter of particles, have been used as one of the most reliable real-time instruments for the size distribution measurement of nanoparticles. Despite its benefits, this instrument has some drawbacks, including equivalency problems for non-spherical particles (i.e. assuming a non-spherical particle is equal to a spherical particle of diameter d due to the same electrical mobility), as well as limitations in terms of its use in workplaces, because of its large size and the complexity of its operation...

Culture in global business transformation projects : the discovery of a grounded theory

Presently organisations engage in what is termed as Global Business Transformation Projects [GBTPs], for consolidating, innovating, transforming and restructuring their processes and business strategies while undergoing fundamental change. Culture plays an important role in global business transformation projects as these involve people of different cultural backgrounds and span across countries, industries and disciplinary boundaries. Nevertheless, there is scant empirical research on how culture is conceptualised beyond national and organisational cultures but also on how culture is to be taken into account and dealt with within global business transformation projects. This research is situated in a business context and discovers a theory that aids in describing and dealing with culture. It draws on the lived experiences of thirty-two senior management practitioners, reporting on more than sixty-one global business transformation projects in which they were actively involved. The research method used is a qualitative and interpretive one and applies a grounded theory approach, with rich data generated through interviews. In addition, vignettes were developed to illustrate the derived theoretical models. The findings from this study contribute to knowledge in multiple ways. First, it provides a holistic account of global business transformation projects that describe the construct of culture by the elements of culture types, cultural differences and cultural diversity. A typology of culture types has been developed which enlarges the view of culture beyond national and organisational culture including an industry culture, professional service firm culture and 'theme' culture. The amalgamation of the culture types instantiated in a global business transformation project compromises its project culture. Second, the empirically grounded process for managing culture in global business transformation projects integrates the stages of recognition, understanding and management as well as the enablement providing a roadmap for dealing with culture in global business transformation projects. Third, this study identified contextual variables to global business transformation projects, which provide the means of describing the environment global business transformation projects are situated, influence the construct of culture and inform the process for managing culture. Fourth, the contribution to the research method is the positioning of interview research as a strategy for data generation and the detailed documentation applying grounded theory to discover theory.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts

In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

Investigation of mediated oxidation of ascorbic acid by ferrocenemethanol using large-amplitude fourier transformed ac voltammetry under quasi-reversible electron-transfer conditions at an indium tin oxide electrode

The ability of the technique of large-amplitude Fourier transformed (FT) ac voltammetry to facilitate the quantitative evaluation of electrode processes involving electron transfer and catalytically coupled chemical reactions has been evaluated. Predictions derived on the basis of detailed simulations imply that the rate of electron transfer is crucial, as confirmed by studies on the ferrocenemethanol (FcMeOH)-mediated electrocatalytic oxidation of ascorbic acid. Thus, at glassy carbon, gold, and boron-doped diamond electrodes, the introduction of the coupled electrocatalytic reaction, while producing significantly enhanced dc currents, does not affect the ac harmonics. This outcome is as expected if the FcMeOH (0/+) process remains fully reversible in the presence of ascorbic acid. In contrast, the ac harmonic components available from FT-ac voltammetry are predicted to be highly sensitive to the homogeneous kinetics when an electrocatalytic reaction is coupled to a quasi-reversible electron-transfer process. The required quasi-reversible scenario is available at an indium tin oxide electrode. Consequently, reversible potential, heterogeneous charge-transfer rate constant, and charge-transfer coefficient values of 0.19 V vs Ag/AgCl, 0.006 cm s (-1) and 0.55, respectively, along with a second-order homogeneous chemical rate constant of 2500 M (-1) s (-1) for the rate-determining step in the catalytic reaction were determined by comparison of simulated responses and experimental voltammograms derived from the dc and first to fourth ac harmonic components generated at an indium tin oxide electrode. The theoretical concepts derived for large-amplitude FT ac voltammetry are believed to be applicable to a wide range of important solution-based mediated electrocatalytic reactions.

Voltammetric monitoring of gold nanoparticle formation facilitated by glycyl-L-tyrosine : relation to electronic spectra and transmission electron microscopy images

Voltammetric techniques have been introduced to monitor the formation of gold nanoparticles produced via the reaction of the amino acid glycyl-L-tyrosine with Au(III) (bromoaurate) in 0.05 M KOH conditions. The alkaline conditions facilitate amino acid binding to Au(III), inhibit the rate of reduction to Au(0), and provide an excellent supporting electrolyte for voltammetric studies. Data obtained revealed that a range of time-dependent gold solution species are involved in gold nanoparticle formation and that the order in which reagents are mixed is critical to the outcome. Concomitantly with voltammetric measurements, the properties of gold nanoparticles formed are probed by examination of electronic spectra in order to understand how the solution environment present during nanoparticle growth affects the final distribution of the nanoparticles. Images obtained by the ex situ transmission electron microscopy (TEM) technique enable the physical properties of the nanoparticles isolated in the solid state to be assessed. Use of this combination of in situ and ex situ techniques provides a versatile framework for elucidating the details of nanoparticle formation.

A combined scanning electron micrograph and electrochemical study of the effect of chemical interaction on the cyclability of lithium electrodes in an ionic liquid electrolyte

The effect of storage time on the cyclability of lithium electrodes in an ionic liquid electrolyte, namely 0.5 m LiBF4 in N-methyl-N-propyl pyrrolidinium bis(fluorosulfonyl)imide, [C3mpyr+][FSI–], was investigated. A chemical interaction was observed which is time dependent and results in a morphology change of the Li surface due to build up of passivation products over a 12-day period. The formation of this layer significantly impacts on the Li electrode resistance before cycling and the charging/discharging process for symmetrical Li|0.5 m LiBF4 in [C3mpyr+][FSI–]|Li coin cells. Indeed it was found that introducing a rest period between cycling, and thereby allowing the chemical interaction between the Li electrode and electrolyte to take place, also impacted on the charging/discharging process. For all Li surface treatments the electrode resistance decreased after cycling and was due to significant structural rearrangement of the surface layer. These results suggest that careful electrode pretreatment in a real battery system will be required before operation.

Reusable surface confined semi-conducting metal-TCNQ and metal-TCNQF4 catalysts for electron transfer reactions

The synthesis of organic semiconducting materials based on silver and copper-TCNQ (TCNQ = 7,7,8,8-tetracyanoquinodimethane) and their fluorinated analogues has received a significant amount of attention due to their potential use in organic electronic applications. However, there is a scarcity in the identification of different applications for which these interesting materials may be suitable candidates. In this work, we address this by investigating the catalytic properties of such materials for the electron transfer reaction between ferricyanide and thiosulphate ions in aqueous solution, which to date has been almost solely limited to metallic nanomaterials. Significantly it was found that all the materials investigated, namely CuTCNQ, AgTCNQ, CuTCNQF4 and AgTCNQF4, were catalytically active and, interestingly, the fluorinated analogues were superior. AgTCNQF4 demonstrated the highest activity and was tested for its stability and re-usability for up to 50 cycles without degradation in performance. The catalytic reaction was monitored via UV-vis spectroscopy and open circuit potential versus time measurements, as well as an investigation of the transport properties of the films via electrochemical impedance spectroscopy. It is suggested that morphology and bulk conductivity are not the limiting factors, but rather the balance between the accumulated surface charge from electron injection via thiosulphate ions on the catalyst surface and transfer to the ferricyanide ions which controls the reaction rate. The facile fabrication of re-usable surface confined organic materials that are catalytically active may have important uses for many more electron transfer reactions.

Lateral charge propagation effects during the galvanic replacement of electrodeposited MTCNQ (M= Cu, Ag) microstructures with gold and its influence on catalysed electron transfer reactions

The galvanic replacement of isolated electrodeposited semiconducting CuTCNQ microstructures on a glassy carbon (GC) substrate with gold is investigated. It is found that anisotropic metal nanoparticles are formed which are not solely confined to the redox active sites on the semiconducting materials but are also observed on the GC substrate which occurs via a lateral charge propagation mechanism. We also demonstrate that this galvanic replacement approach can be used for the formation of isolated AgTCNQ/Au microwire composites which occurs via an analogous mechanism. The resultant MTCNQ/Au (M = Cu, Ag) composite materials are characterized by Raman, spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) and investigated for their catalytic properties for the reduction of ferricyanide ions with thiosulphate ions in aqueous solution. Significantly it is demonstrated that gold loading, nanoparticle shape and in particular the MTCNQ–Au interface are important factors that influence the reaction rate. It is shown that there is a synergistic effect at the CuTCNQ/Au composite when compared to AgTCNQ/Au at similar gold loadings.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.