192 research outputs found

    Rayleigh Instability Driven Nodular Cu<sub>2</sub>O Nanowires via Carbothermal Reduction of CuO Nanowires

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    Nodular cuprous oxide (Cu<sub>2</sub>O) vertical nanowire (NW) arrays with a length of more than 20 μm and diameter of ∼300 nm have been synthesized by the carbothermal reduction of thermally grown cupric oxide (CuO) NWs. The transformation initiates at a low temperature of 350 °C. Two important effects are observed: First, the CuO → Cu<sub>2</sub>O reduction occurs by oxygen out diffusion under a highly reducing atmosphere afforded by the presence of carbon monoxide (CO). Second, the surface reduction creates instabilities, which propagate and grow into a string of nodules along the length of the Cu<sub>2</sub>O NWs. This effect is determined to be due to Rayleigh instability, but initiated via Cu<sub>2</sub>O phase transformation

    Excess Electron and Hole in 1‑Benzylpyridinium-Based Ionic Liquids

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    The study of ionic liquids that may be compatible with the type of radiation chemistry events occurring in nuclear separation processes is a topic of high current interest. In this article, we focus on two ionic liquids based on the benzylpyridinium cation. This cation has been proposed to be able to capture either an excess electron or hole without undergoing fast dissociation. Shkrob, Wishart, and collaborators (J. Phys. Chem. B 2013, 117 (46), 14385−14399) have indicated that the stabilization is likely in the form of dimers in solution with the excess electron localized on adjacent pyridinium rings and the excess hole localized on phenyl rings. Our first-principles dynamical studies support these ideas but present a more nuanced view of the time-dependent behavior that is likely to occur at short time for systems at room temperature

    Role of Organic Solvents in Immobilizing Fungus Laccase on Single-Walled Carbon Nanotubes for Improved Current Response in Direct Bioelectrocatalysis

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    Improving bioelectrocatalytic current response of redox enzymes on electrodes has been a focus in the development of enzymatic biosensors and biofuel cells. Herein a mediatorless electroreduction of oxygen is effectively improved in terms of a remarkable enhancement by ca. 600% in maximum reductive current by simply adding 20% ethanol into laccase solution during its immobilization onto single-walled carbon nanotubes (SWCNTs). Conformation analysis by circular dichroism and attenuated total reflectance infrared spectroscopy demonstrate promoted laccase-SWCNTs contact by ethanol, thus leading to favorable enzyme orientation on SWCNTs. Extended investigation on acetone-, acetonitrile-, <i>N</i>,<i>N</i>-dimethylformamide (DMF)-, or dimethyl sulfoxide (DMSO)-treated laccase-SWCNTs electrodes shows a 400% and 350% current enhancement at maxima upon acetone and acetonitrile treatment, respectively, and a complete diminish of reductive current by DMF and DMSO. These results together reveal the important role of organic solvents in regulating laccase immobilization for direct bioelectrocatalysis by balancing surface wetting and protein denaturing

    A two-step approach for fluidized bed granulation in pharmaceutical processing: Assessing different models for design and control

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    <div><p>Various modeling techniques were used to understand fluidized bed granulation using a two-step approach. First, Plackett-Burman design (PBD) was used to identify the high-risk factors. Then, Box-Behnken design (BBD) was used to analyze and optimize those high-risk factors. The relationship between the high-risk input variables (inlet air temperature X<sub>1</sub>, binder solution rate X<sub>3</sub>, and binder-to-powder ratio X<sub>5</sub>) and quality attributes (flowability Y<sub>1</sub>, temperature Y<sub>2</sub>, moisture content Y<sub>3</sub>, aggregation index Y<sub>4</sub>, and compactability Y<sub>5</sub>) of the process was investigated using response surface model (RSM), partial least squares method (PLS) and artificial neural network of multilayer perceptron (MLP). The morphological study of the granules was also investigated using a scanning electron microscope. The results showed that X<sub>1</sub>, X<sub>3</sub>, and X<sub>5</sub> significantly affected the properties of granule. The RSM, PLS and MLP models were found to be useful statistical analysis tools for a better mechanistic understanding of granulation. The statistical analysis results showed that the RSM model had a better ability to fit the quality attributes of granules compared to the PLS and MLP models. Understanding the effect of process parameters on granule properties provides the basis for modulating the granulation parameters and optimizing the product performance at the early development stage of pharmaceutical products.</p></div

    (a) VIP and (b) coefficients for the PLS model.

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    <p>(a) VIP and (b) coefficients for the PLS model.</p

    Indirect Phase Transformation of CuO to Cu<sub>2</sub>O on a Nanowire Surface

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    The reduction of CuO nanowires (NWs) to Cu<sub>2</sub>O NWs undergoes an indirect phase transformation on the surface: from single crystalline CuO, to a disordered Cu<sub>2−δ</sub>O phase, and then to crystalline Cu<sub>2</sub>O. A 9–12 nm disordered Cu<sub>2−δ</sub>O is formed on the NW surface by exposing CuO NWs to CO at 1 Torr, 300 °C for 30 min. After 60 min, this layer decreases to 2–3 nm and is eliminated after 180 min. Energy dispersive X-ray spectroscopy using a scanning tunneling electron microscope and across a single NW reveals the disordered layer to be O-rich with respect to Cu<sub>2</sub>O with a maximum at. % Cu:O = 1.8. X-ray photoelectron spectroscopy shows adsorbed CO on the surface as evidence of the reduction reaction. Micro-Raman spectroscopy tracks the transformation in NWs as a function of reduction time. A CO enabled surface reduction reaction coupled to diffusion-limited transport of “nonlattice” O to the surface is proposed as a mechanism for Cu<sub>2−δ</sub>O formation. The initial buildup of out-diffusing O to the surface appears to aid the formation of the disordered surface layer. The transformation follows Ostwald–Lussac’s law which predicts formation of unstable phases over stable phases, when phase transformation rates are limited by kinetic or diffusional processes. The study provides a generalized approach for facile growth of few nanometer transient layers on multivalent, metal oxide NW surfaces

    MLP neural network architecture used for modeling granule properties.

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    <p>MLP neural network architecture used for modeling granule properties.</p

    Plasmonic Metal-to-Semiconductor Switching in Au Nanorod-ZnO nanocomposite films

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    We demonstrate conductivity switching from a metal to semiconductor using plasmonic excitation and charge injection in Au-nanorod (AuNRs)-ZnO nanocomposite films. ZnO films 12.6, 20.3, and 35.6 nm were deposited over AuNRs using atomic layer deposition. In dark conditions, the films transitioned from metallic to semiconducting behavior between 150 and 200 K. However, under sub-bandgap, white light illumination, all films behaved as semiconductors from 80 to 320 K. Photoresponse (light/dark conductivity) was strongly dependent on the thickness of ZnO, which was 94.4 for AuNR–12.6 nm ZnO and negligible for AuNR–35.6 nm ZnO. Conductivity switching and thickness dependence of photoresponse were attributed to plasmonically excited electrons injected from AuNRs into ZnO. Activation energies for conduction were extracted for these processes

    Contour plots showing the effects of X<sub>1</sub> and X<sub>3</sub> on granule compactability obtained by using: (a) RSM, (b) PLS, and (c) MLP (X<sub>5</sub> = 11.5).

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    <p>Contour plots showing the effects of X<sub>1</sub> and X<sub>3</sub> on granule compactability obtained by using: (a) RSM, (b) PLS, and (c) MLP (X<sub>5</sub> = 11.5).</p

    Standard Pareto chart showing the effects of various process factors on (a) flowability, (b) temperature, (c) moisture, (d) aggregation index, and (e) compactability.

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    <p>Standard Pareto chart showing the effects of various process factors on (a) flowability, (b) temperature, (c) moisture, (d) aggregation index, and (e) compactability.</p
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