The amount of anthropogenic CO2 emission keeps increasing worldwide, and it urges the development of efficient CO2 capture technologies. Among various CO2 capture methods, adsorption is receiving more interest, and carbonaceous materials are considered good CO2 adsorbents. There have been many studies of N-containing carbon materials that have enhanced surface interaction with CO2; however, various N-containing functional groups existing in the carbon surface have not been investigated in detail. In this study, first-principle calculations were conducted for carbon models having various N-functional groups to distinguish N-containing heterogeneity and understand carbon surface chemistry for CO2 adsorption. Among N-functional groups tested, the highest adsorption energies of −0.224 and −0.218 eV were observed in pyridone and pyridine groups, respectively. Structural parameters including bond angle and length revealed an exceptional hydrogen-bonding interaction between CO2 and pyridone group. Charge accumulation on CO2 during interaction with pyridine-functionalized surface was confirmed by Bader charge analysis. Also, the peak shift of CO2 near Fermi level in the DOS calculation and the presence of HOMO on pyridinic-N in the frontier orbital calculation determined that the interaction of pyridinic-N is weak Lewis acid–base interaction by charge transfer. Furthermore, adsorption energies of N2 were calculated and compared to those of CO2 to find its selective adsorption ability. Our results suggest that pyridone and pyridine groups are most effective for enhancing the interaction with CO2 and have potential for selective CO2 adsorption.
The effect of argon ion bombardment, during deposition on the microstructure of several tens of nanometers thick Ag films, has been studied. The structure of the Ag films was analyzed by x-ray powder diffraction method. Results show that Ar ion bombardment not only influenced the film growth process but had a significant effect on the structure of the resulting films. In comparison to an evaporated thin Ag film, our films showed much less [111] preferred orientation and a lattice expansion normal to the film surface instead of contraction, with compressive rather than tensile surface strain and plane stress. We also observed much smaller grain sizes, and higher twin fault probabilities, microstrains and dislocation densities. These structural parameters varied systematically with the normalized energy En, that is, the energy deposited by incident energetic Ar+ at the film surface per arriving Ag atom; at first rapidly, then leveling off when En≥42% eV/Ag atom. Preferential orientation is believed to be dependent on film thickness as well as on En. Unlike other parameters, twin fault probability increased to a maximum at En=20 eV/Ag atom and then decreased as En increased further due to self-annealing during deposition.
Throughout the history of powder diffraction practice there has been uncertainty about whether or not a refractive-index correction should be made to Bragg's law. High-precision Bragg-angle measurements have been performed with synchrotron radiation on SRM-640 silicon powders at glancing angles; it is found that little or no correction is necessary for the usual 20 angle range.
We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI. It is to process neural networks on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signify the need for on-device AI, especially if we deploy a massive number of devices. NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal effort. Besides, NNStreamer simplifies implementations and allows reusing off-the-shelf media filters directly, which reduces developmental costs significantly. We are already deploying NNStreamer for a wide range of products and platforms, including the Galaxy series and various consumer electronic devices. The experimental results suggest a reduction in developmental costs and enhanced performance of pipeline architectures and NNStreamer. It is an open-source project incubated by Linux Foundation AI & Data, available to the public and applicable to various hardware and software platforms.
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