Enhancing catalytic performance of dilute metal alloy nanomaterials
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This paper addresses the use of Ce 0.8 Gd 0.2 O 2–δ (GDC) infiltration into the Ni–(Y 2 O 3 ) 0.08 (ZrO 2 ) 0.92 (YSZ) fuel electrode of solid oxide cells (SOCs) for improving their electrochemical performance in fuel cell and electrolysis operation. Although doped ceria infiltration into Ni–YSZ has recently been shown to improve the electrode performance and stability, the mechanisms defining how GDC impacts electrochemical characteristics are not fully delineated. Furthermore, the electrochemical characteristics have not yet been determined over the full range of conditions normally encountered in fuel cell and electrolysis operation. Here we present a study of both symmetric and full cells aimed at understanding the electrochemical mechanisms of GDC-modified Ni–YSZ over a wide range of fuel compositions and temperatures. Single-step GDC infiltration at an appropriate loading substantially reduced the polarization resistance of Ni–YSZ electrodes in electrolyte-supported cells, as measured using electrochemical impedance spectroscopy (EIS) at various temperatures (600–800 °C) in a range of H 2 O–H 2 mixtures (3–90 vol% H 2 O). Fuel-electrode-supported cells had significant concentration polarization due to the thick Ni–YSZ supports. Here, a distribution of relaxation times approach is used to develop a physically-based electrochemical model; the results show that GDC reduces the reaction resistance associated with three-phase boundaries, but also appears to improve oxygen transport in the electrode. Increasing the H 2 O fraction in the H 2 –H 2 O fuel mixture reduced both the three-phase boundary resistance and the gas diffusion resistance for Ni–YSZ; with GDC infiltration, the electrode resistance showed less variation with fuel composition. GDC infiltration improved the performance of fuel-electrode-supported full cells, which yielded a maximum power density of 2.28 W cm –2 in fuel cell mode and an electrolysis current density at 1.3 V of 2.22 A cm –2 , both at 800°C.
Monte Carlo simulations reveal the effects of inter-crystalline space on the adsorption of ethane and CO 2 in silicalite
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Abstract not provided
Microstructure controlled ammonolysis allowed the synthesis of oxynitrides La 1−x Y x Ta IV O 2 N ( x ≤ 0.3) and YTa(O,N) 3 of which the first ones showed a remarkable up-built of photovoltage even in the presence of reduced tantalum ( e.g. Ta 4+ ).
Abstract not provided
Hybrid nanoparticles composed of gold nanoparticles and carbon nanodots provide great signal amplification in SERS detection of herbicide molecules.
Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.
Alkali addition in organic–inorganic perovskite has become the standard recipe for achieving solar cells with efficiencies exceeding 20%, but the mechanism is not well understood. We use non-contact carrier lifetime measurements, mobility measurements, and synchrotron-based x-ray characterization techniques to show that there is a unique benefit to adding hybrid perovskite samples with Rb and Cs simultaneously. When either Rb or Cs is added, charge carrier mobility increases with alkali concentration. Charge carrier lifetime benefits from alkali incorporation as well, but is optimized with only moderate concentration at 1%. When both Rb and Cs are introduced, however, the high mobility is maintained and the charge carrier lifetime increases considerably. Our results show that when incorporated alone, Rb and Cs have very similar roles in a perovskite crystal, but when co-added, halide distribution becomes homogenized correlating with improved charge transport properties.
Strontium clock atom interferometry is a promising new technology, with multiple experiments under development around the world to explore its potential for dark matter and gravitational wave detection. In these detectors, large momentum transfer using sequences of many laser pulses is necessary, and thus high-fidelity pulses are important since small errors become magnified. Quantum optimal control (QOC) is a framework for developing control pulse waveforms that achieve high fidelity and are robust against experimental imperfections. Resonant single-photon transitions using the narrow clock transition of strontium involve significantly different quantum dynamics than more established atom interferometry methods based on far-detuned two-photon Raman or Bragg transitions, which leads to new opportunities and challenges when applying QOC. Here, we study in simulation QOC pulses for strontium clock interferometry and demonstrate their advantage over basic square pulses (primitive pulses) and composite pulses in terms of robustness against multiple noise channels. In conclusion, this could improve the scale of large momentum transfer in Sr clock interferometers, paving the way to achieving these scientific goals.
Abstract not provided
In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.
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