Search

Dry reforming of methane (DRM) is considered a promising process to convert two major greenhouse gases (CH 4 and CO 2 ) into hydrogen and synthesis gas for chemical and fuel production. In the current work, NiMo Al 2 O 3 samples were prepared by combustion synthesis via one-pot process using urea and starch as fuel, and then treated in a flow of CH 4 /CO 2 at 800 °C to in-situ synthesize the Ni Mo 2 C/Al 2 O 3 catalyst, which was then tested in CH 4 /CO 2 for DRM. The results indicate that the Ni Mo 2 C/Al 2 O 3 catalyst was successfully formed, and there was a strong interaction of Ni 0 Mo 2 C via in-situ carburization; within DRM, CH 4 can be converted by Ni 0 into intermediate species of C ∗ , and was then further transformed into CO by the adjacent Mo 2 C species in a catalytic cycle. Meanwhile, there were improved specific surface area and resistance to oxidation and sintering, as suggested by BET, XRD and XPS. As a result, the conversion of CO 2 and mole ratio of H 2 /CO maintained stable near 94% and 0.99, respectively, and the conversion of CH 4 remained stable near 89.4% in a 15-h DRM test. [ABSTRACT FROM AUTHOR]

Human-oriented image communication should take the quality of experience (QoE) as an optimization goal, which requires effective image perceptual quality metrics. However, traditional user-based assessment metrics are limited by the deviation caused by human high-level cognitive activities. To tackle this issue, in this paper, we construct a brain response-based image perceptual quality metric and develop a brain-inspired network to assess the image perceptual quality based on it. Our method aims to establish the relationship between image quality changes and underlying brain responses in image compression scenarios using the electroencephalography (EEG) approach. We first establish EEG datasets by collecting the corresponding EEG signals when subjects watch distorted images. Then, we design a measurement model to extract EEG features that reflect human perception to establish a new image perceptual quality metric: EEG perceptual score (EPS). To use this metric in practical scenarios, we embed the brain perception process into a prediction model to generate the EPS directly from the input images. Experimental results show that our proposed measurement model and prediction model can achieve better performance. The proposed brain response-based image perceptual quality metric can measure the human brain's perceptual state more accurately, thus performing a better assessment of image perceptual quality.