Release Mechanism of Volatile Products from Oil Shale Pressure-Controlled Pyrolysis Induced by Supercritical Carbon Dioxide

The compactness of the oil shale reservoir and the complexity of the pore structure lead to the secondary reaction of kerogen in the process of hydrocarbon expulsion, which reduces the effective recovery of shale oil. In this paper, supercritical carbon dioxide was used as a heat carrier and a displacement medium. In a self-designed fluidized bed experimental system for pressure-controlled pyrolysis of oil shale, the experiments of oil shale pyrolysis under standard atmospheric pressure and 7.8–8.0 MPa pressure in nitrogen and carbon dioxide atmospheres were completed. The extraction efficiency of supercritical carbon dioxide at low temperature is obvious, but with the increase of temperature, the effect of extraction on pyrolysis is lower than that of temperature. Under a nitrogen atmosphere, the secondary reaction of shale oil is mainly secondary pyrolysis and aromatization. However, in a supercritical carbon dioxide atmosphere, the main reactions are secondary addition and aromatization. In addition, compared with that in the standard atmospheric pressure, it was found that the olefin synthesis reaction was obviously inhibited under a high-pressure nitrogen or supercritical carbon dioxide atmosphere.© 2022 The Authors. Published by American Chemical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.fi=vertaisarvioitu|en=peerReviewed

Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington’s and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases

A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI) is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods

Release Mechanism of Volatile Products from Oil Shale Pressure-Controlled Pyrolysis Induced by Supercritical Carbon Dioxide

Funding Information: This experimental work was supported by “the Open Fund for Research on the Control Mechanism of Gas Injection Parameters on the Coking Degree in the Product Migration Process of the National Oil Shale Exploitation Research and Development Center”, “the Fundamental Research Funds for the Central Universities, grant number 2021QN1002”,“The Natural Science Foundation of Jiangsu Province, grant number BK20221133”, the “China Postdoctoral Science Foundation, grant number 2021M693421”, “Laboratory opening project of China University of Mining and Technology in 2021, grant number 2021SYF97” and “The 2021 Jiangsu Shuangchuang (Mass Innovation and Entrepreneurship) Talent Program, grant number JSSCBS20211238”. The authors also express their appreciation for the technical reviewers for their constructive comments. Publisher Copyright: © 2022 The Authors. Published by American Chemical Society.The compactness of the oil shale reservoir and the complexity of the pore structure lead to the secondary reaction of kerogen in the process of hydrocarbon expulsion, which reduces the effective recovery of shale oil. In this paper, supercritical carbon dioxide was used as a heat carrier and a displacement medium. In a self-designed fluidized bed experimental system for pressure-controlled pyrolysis of oil shale, the experiments of oil shale pyrolysis under standard atmospheric pressure and 7.8-8.0 MPa pressure in nitrogen and carbon dioxide atmospheres were completed. The extraction efficiency of supercritical carbon dioxide at low temperature is obvious, but with the increase of temperature, the effect of extraction on pyrolysis is lower than that of temperature. Under a nitrogen atmosphere, the secondary reaction of shale oil is mainly secondary pyrolysis and aromatization. However, in a supercritical carbon dioxide atmosphere, the main reactions are secondary addition and aromatization. In addition, compared with that in the standard atmospheric pressure, it was found that the olefin synthesis reaction was obviously inhibited under a high-pressure nitrogen or supercritical carbon dioxide atmosphere.Peer reviewe

Comparative Study of Prior Particle Boundaries and Their Influence on Grain Growth during Solution Treatment in a Novel Nickel-Based Powder Metallurgy Superalloy with/without Hot Extrusion

The prior particle boundaries (PPBs), as one of the typical defects in the nickel-based powder metallurgy superalloy, largely affect the microstructure and thus properties/performance of alloys. However, the effect of PPBs on the microstructure evolution in nickel-based powder metallurgy superalloy during heat treatment is still unclear. In this paper, a comparative study of PPBs and their influence on grain growth during solution treatment in a novel nickel-based powder metallurgy superalloy FGH4113A (i.e., WZ-A3 from Shenzhen Wedge, China) with/without hot extrusion (HEX) was conducted. Firstly, through a combination of scanning electron microscope (SEM), electron probe microanalyzer (EPMA) and transmission electron microscope (TEM) techniques, PPBs in FGH4113A alloys were characterized to be Al2O3, carbides (TiC, M6C, M23C6) and large-size γ′ particles. After HEX, the oxides broke, carbides deformed, and γ′ phase redistributed. After solution treatment at 950 °C, the TiC decomposed to M6C and M23C6, while no such decomposition occurred in FGH4113A alloys after solution treated at 1050 °C and 1150 °C. Secondly, the evolution of grain size in FGH4113A alloys was analyzed using the electron backscattered diffraction (EBSD) technique. At 950 °C, the decomposition of carbide TiC resulted in the increase of PPBs and the enhancement of their pinning effect on grain boundaries, thus inhibiting grain growth. At 1050 °C, the nucleation rate due to recrystallization is comparable to the grain growth rate, leading to the stable distribution of grain size. While at 1150 °C, the higher temperature can induce a higher content of PPBs. However, the driving force for grain growth surpassed the pinning force of PPBs, making the grains quickly coarsen. Finally, it was concluded that the HEX process is an effective method to modify the microstructure of powder metallurgy superalloy after HIP that can heavily refine the grains in the powder metallurgy superalloys. Furthermore, based on the present experiment and analysis, an appropriate solution treatment mechanism (i.e., 1050 °C for 2 h) was proposed for FGH4113A alloys

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR

Visualizing the intercity correlation of PM_2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data - Fig 4

<p><b>Matrix views of the intercity correlations and time lags for PM</b>_<b>2.5</b><b>time series between 17 cities in (A) February, (B) May, (C) August, and (D) November of 2014 in the Beijing-Tianjin-Hebei area.</b> The color and size of the circles indicate the strength of the correlation. The color bar on the right provides a scale of the correlation coefficients. Orange, red, and claret colors mean the correlation coefficients are over 0.5, 0.7, and 0.9, respectively, while the green color indicates correlation coefficients <0.5. The label within the circle refers to the time lag. A positive time lag, τ, means the city on the y-axis lags the city on the x-axis by τ hours. Similarly, a negative time lag, τ, means the city on the y-axis leads the city on the x-axis by -τ hours. A hash sign (#) means the correlation for the time lag is significantly larger than the correlation without the time lag at the 10% level. A dollar sign ($) refers to the statistical significance at the 5% level. An asterisk (*) refers to the statistical significance at the 1% level. Note that for clear presentation, all matrix views show only the upper portion of the matrix to avoid duplication. Matrix views for other months are provided in the supplementary information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192614#pone.0192614.s002" target="_blank">S2 Fig</a>). This figure was produced using Python 2.7.5 (<a href="https://www.python.org/" target="_blank">https://www.python.org</a>) and Matplotlib 1.5.0 (<a href="https://matplotlib.org/" target="_blank">https://matplotlib.org</a>/).</p