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Heterogeneous thermal-catalytic CO2 hydrogenation to alcohols using renewable energy is a highly attractive approach for recycling greenhouse gases into high-value chemicals and fuels, thereby reducing the dependence on fossil fuels, while simultaneously mitigating the CO2 emission and environmental problems. Currently, great advances have been made on the heterogeneous catalysts, but an in-depth and more comprehensive understanding to further promote this reaction process is still lacking. Herein, we highlight the thermodynamic and kinetic analysis of CO2 hydrogenation reaction firstly. Then, various reaction pathways for CO2 hydrogenation to methanol and higher alcohols (C2+ alcohols) have been discussed in detail, respectively, by combining the experimental studies and density functional theory calculations. On this basis, the key factors influencing the reaction performance, such as metal dispersion, support modification, promoter addition and their structural optimization, are summarized on the metal-based and metal-oxide-based catalysts. In addition, the catalytic performance of CO2 hydrogenation to alcohols and the relationship between structure and properties are mainly summarized and analyzed in the past five years. To conclude, the current challenges and potential strategies in catalyst design, structural characterization and reaction mechanisms are presented for CO2 hydrogenation to alcohols. [ABSTRACT FROM AUTHOR]

The specific air jet of a diffuser is formed by the complex internal structure, which affects the outlet airflow distribution of the diffuser directly and the indoor environment distribution indirectly. If the diffusers are developed based on their actual geometry structure and their boundary conditions are set as their inlet flowrate, the simulated indoor temperature distribution will be more accurate. However, it is noted that many problems may arise, such as model complexity, many grid cells, and slow convergence of calculations. Therefore, this paper focuses on a simplified method for four-way square diffusers in a computational fluid dynamics (CFD) simulation of indoor non-uniform temperature distribution. Firstly, the airflow distribution is simulated on the outlet air supply cross-section of the diffuser. Then, according to the outflow characteristics of the diffuser, the diffuser model is simplified and simulated in an experimental room. Finally, the temperature distribution at the 1.2 m height plane is obtained from CFD simulation and compared with the experimental results. The results show that the 68-point air supply opening model can well simulate the effects of the outlet airflow distribution of the diffuser, and the simulated indoor temperature distribution meets the experiment results well. The deviations for three scenarios are between −7.4~1.7% and the average deviation is −3.0%, while the root mean square error of temperature for three scenarios is 0.7 °C, 0.7 °C, and 1.0 °C, respectively. The results also demonstrate the mutual influence of the airflow from different diffusers and the indoor non-uniform temperature distribution under the action of multiple diffusers. The proposed method can contribute to balancing the model complexity and the accuracy in CFD simulation, especially for multiple diffusers in the room. [ABSTRACT FROM AUTHOR]

In this work, the contribution of different types of carbon blacks (N115, N330, N550, N660) and their primary and secondary thermally cracked recovered carbon blacks to the mechanical properties of NR composites was evaluated. The thermally cracked recovered carbon blacks were prepared by cracking the rubber composites at 500 °C and de-hybridizing them at 900 °C. The characterization of the thermally cracked recovered carbon blacks by scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy showed that carbon blacks after primary and secondary thermal cracking recovery were more prone to aggregation and exhibited a higher degree of carbon defects. The number and type of functional groups on the surface of these carbon blacks were significantly reduced. For NR composites with pristine samples added, the mechanical properties and the bound rubber content tests showed that the mechanical properties of the NR composites became weaker with the increase in carbon black particle size. The bound rubber content also decreased with increased carbon black particle size. The mechanical properties of the NR composites reinforced with carbon black recovered by primary and secondary thermal cracking would therefore decrease. The results of AFM and DSC tests further confirmed the decreasing trend of bound rubber. The present work demonstrates the effect of bound rubber content variation on the mechanical properties of rubber, demonstrates the morphology of bound rubber more visually, and provides new insights into the reinforcement theory of CB. [ABSTRACT FROM AUTHOR]

Due to the depletion of fossil fuels, empirics began looking at the factors that might encourage investment in renewable energy. Socioeconomic productivity can encourage renewable energy development by encouraging authorities, businesses, and families to rely more on renewable energy sources. Therefore, this analysis is the first-ever effort to detect the impact of socioeconomic productivity on renewable energy development. We have used the panel ARDL and QARDL to examine the estimates. The results of the panel ARDL model predict that national income, financial development, productive capacity index, human capital, ICT, institutional quality, and structural changes are beneficial for renewable energy development in the long run. In the short run, only financial development, productive capacity index, human capital, and ICT promote renewable energy development. Likewise, the panel QARDL model estimates that the national income, financial development, and productive capacity index promote renewable energy development in the long run. However, in the short run, only the productive capacity index and financial development promote renewable energy development. Therefore, by integrating productive assets, entrepreneurial skills, and industrial connections, policymakers must work to boost the productive socioeconomic potential. [ABSTRACT FROM AUTHOR]

The channel attention mechanism is widely used in deep learning. However, the existing channel attention mechanism directly performs the global average pooling and then full connection for all channels, which causes the local information to be ignored and the feature information cannot be reasonably assigned with the proper weights. This paper proposed a local channel attention module, based on the channel attention. This module focuses on the local information of the feature image, obtains the weight of each regional channel through convolution, and then integrates the information, so that the regional information can be fully utilized. Moreover, the local channel attention module is combined with the residual module, and the local channel attention residual network LSERNet is constructed to detect the abnormal state of the blast furnace tuyere image. With sufficient experiments on the collected datasets of the blast furnace tuyere, the results show that the proposed method can efficiently extract the feature information, and the recognition accuracy of the LSERNet model reached 98.59%. Further, our model achieved the highest accuracy, compared with SE-ResNet50, ResNet50, LSE-ResNeXt, SE-ResNeXt, and ResNeXt models. [ABSTRACT FROM AUTHOR]

The efficient and clean conversion of light alkanes is a research hotspot in the petrochemical industry, and the development of effective and eco-friendly non-noble metal-based catalysts is a key factor in this field. Among them, gallium is a metal component with good catalytic performance, which has been extensively used for light alkanes conversion. Herein, we critically summarize recent developments in the preparation of gallium-based catalysts and their applications in the catalytic conversion of light alkanes. First, we briefly describe the different routes of light alkane conversion. Following that, the remarkable preparation methods for gallium-based catalysts are discussed, with their state-of-the-art application in light alkane conversion. It should be noticed that the directional preparation of specific Ga species, strengthening metal-support interactions to anchor Ga species, and the application of new kinds of methods for Ga-based catalysts preparation are at the leading edge. Finally, the review provides some current limitations and future perspectives for the development of gallium-based catalysts. Recently, different kinds of Ga species were reported to be active in alkane conversion, and how to separate them with advanced in situ and ex situ characterizations is still a problem that needs to be solved. We believe that this review can provide base information for the preparation and application of Ga-based catalysts in the current stage. With these summarizations, this review can inspire new research directions of gallium-based catalysts in the catalysis conversion of light alkanes with ameliorated performances. [ABSTRACT FROM AUTHOR]

In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them. [ABSTRACT FROM AUTHOR]

Photoelectrocatalytic (PEC) oxidation is an advanced technology that combines photocatalytic oxidation (PC) and electrolytic oxidation (EC). PEC activity can be greatly enhanced by the PC and EC synergy effect. In this work, novel copolymerized g-C3N4 (denoted as CNx)/TiO2 core-shell nanowire arrays were prepared by chemical vapor deposition. CNx were deposited on the surface of TiO2 nanowire arrays using organic monomer 4,5-dicyanidazole and dicyandiamide as copolymerization precursor. TiO2 nanowire arrays provide a direct and fast electron transfer path, while CNx is a visible light responsive material. After CNx deposition, the light response range of TiO2 is broadened to 600 nm. The deposition of CNx shell effectively improves the PC efficiency and PEC efficiency of TiO2. Under visible light irradiation and 1 V bias potential, the rate constant k of PEC degradation of CNx/TiO2 core-shell nanowire arrays is 0.0069 min−1, which is 72% higher than that of pure TiO2 nanowires. The built-in electric field formed in the interface between TiO2 core and CNx shell would effectively promote photogenerated charge separation and PEC activity. [ABSTRACT FROM AUTHOR]

The development of an effective strategy for synthesizing two-dimensional MFI zeolites has attracted more and more attention. Herein, nanosheet-stacked hierarchical ZSM-5 zeolite was obtained by a seed-assisted hydrothermal synthesis route using a small amount of [C18H37-N+(CH3)2-C6H12-N+(CH3)2-C6H12]Br2 (C18-6-6Br2) as a zeolite structure-directing agent and triethylamine (TEA) as a zeolite growth modifier. By varying the molar ratio of C18-6-6Br2/TEA from 2.5/0 to 2.5/40, the morphologies and textural properties of the resultant HZ5-2.5/x catalysts were finely modulated. By increasing x from 5 to 40, the morphology of the HZ5-2.5/x changed from unilamellar assembly with narrow a–c plane to intertwined nanosheets with wide a–c plane and multilamellar nanosheets with house-of-cards morphology. The thickness of these nanosheets was almost 8–10 nm. In addition, selectivity to light olefins reached 70.7% for the HZ5-2.5/10 catalyst, which was 6.6% higher than that for CZSM-5 (64.1%). Furthermore, the MFI zeolite nanosheets exhibited better anticoking stability within the 60 h reaction time compared to conventional ZSM-5 zeolite, which could be attributed to the short diffusion path and hierarchical porosity. This work will provide valuable insights into the rational design of novel zeolite catalysts for the efficient cracking of hydrocarbons. [ABSTRACT FROM AUTHOR]

Graphene (GR) was used to blend with eugenol polysiloxane-polycarbonate (Si-PC) copolymer to prepare a Si-PC/GR nanocomposite via a solution blending method and the impact of graphene on the properties of Si-PC/GR nanocomposite was investigated. The morphology and structure of the Si-PC/GR nanocomposite were characterized. Combining morphology and property analysis, the result showed that when the graphene dispersed uniformly in the Si-PC matrix, the mechanical properties, thermostability and barrier property of Si-PC/GR nanocomposite were enhanced. Compared with Si-PC copolymer, the pyrolytic temperature of Si-PC/2.5%GR nanocomposite at 5% weight loss was 434.3 °C, which was 20.6 °C higher than Si-PC copolymer; and the oxygen barrier value of Si-PC/1.5%GR nanocomposite decreased to 160.2 cm3/m2 24 h 0.1 MPa, which was 53.2 less than pure Si-PC. The mechanical properties of Si-PC/GR nanocomposite were enhanced with an appropriate additive amount of graphene. The hydrophobicity also had been enhanced at the meantime. [ABSTRACT FROM AUTHOR]