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Photocatalytic reduction of CO2 to valuable chemicals is one of the important solutions to the problems of global warming and energy shortage. Here, a Bi2S3/CdIn2S4 S-scheme heterojunction was constructed by a simple solvothermal method, which greatly facilitated the segregation and migration of photogenerated charge carriers. The optimized heterojunction achieved an efficient methyl formate (MF) generation rate (5464 μmol h−1 g−1) in the photocatalytic CO2/methanol reduction system, which was about 29-fold that of pure Bi2S3. The preliminary mechanism of the photocatalytic system over the S-scheme Bi2S3/CdIn2S4 heterojunction was proposed using in situ technology and experimental comparison. This work provides insights into the application of metal sulfide heterojunctions in the field of MF research and the photocatalytic CO2/methanol reduction system. [ABSTRACT FROM AUTHOR]

Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the process of preparing biodiesel using microreactors, cosolvents and homogeneous catalysts are generally used, which adds to the separation cost. Hence, this study explored the use of an opposite-side feed micro-fixed-bed reactor with an inner diameter of 2 mm to produce biodiesel by the transesterification of soybean oil and methanol without any cosolvent, in which a fixed solid base acted as catalyst and obstacles. A biodiesel yield of 99.4% was achieved when the methanol-to-oil molar ratio was 12, the temperature was 70 °C, and the residence time was 8.05 min. A mixing study was conducted for this microreactor through computational fluid dynamics (CFD) simulations. The results showed that the best mixing index could reach 0.94 within 25 mm (length). Then, the transesterification kinetics in this reactor were found to be consistent with the proposed pseudo-homogeneous secondary reaction model. With the kinetics data and mixed-model, the biodiesel yield could be predicted. The simulated results were consistent with the experimental data. [ABSTRACT FROM AUTHOR]

A series of cost-effective (X) K2CO3/γAl2O3 catalysts for efficient conversion of vinyltrimethoxysilane (VTMOS) to vinyltris(β-methoxyethoxy)silane (VTMOEOS) were synthesized. The microstructure and active site of the synthesized catalysts were analyzed by BET, XRD, FT-IR, SEM, and CO2-TPD. The results showed that the synthesized catalysts formed K–O–Al strong basic sites while maintaining the original structure, which greatly improved the basic strength of the catalysts. It was found that the 60%K2CO3/γAl2O3 possessed the highest the catalytic activity under the activation temperature of 600 °C by optimizing the preparation conditions of K2CO3/γAl2O3. Moreover, the optimal reaction process conditions were also determined by optimizing the experimental reaction temperature, reaction time, catalyst dosage, and reactant molar ratio under (60%, 600 °C) K2CO3/γAl2O3 catalysis. Under the optimal reaction conditions, the conversion of VTMOS was 92.5% and the yield of VTMOEOS was 65%. Meanwhile, the 60%K2CO3/γAl2O3 catalyst was satisfactorily reusable and maintained a high catalytic activity after four cycles of catalytic reactions. [ABSTRACT FROM AUTHOR]