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Selective capture of carbon dioxide as a C1 feedstock for the preparation of high‐value chemicals has been recognised as an important way to mitigate the crisis. In this work, a new heterogeneous catalyst, Cu@CP‐PII, was developed through the rational construction of an open‐pore organic skeleton, which is easy to synthesise and highly efficient. With the synergistic effect between the high CO2 affinity of the porous skeleton and the highly dispersed Cu(I) sites of the activated alkynes, it can be used for the efficient preparation of α‐methylene cyclic carbonates at ambient temperature and pressure. The structure and morphology were exhaustively characterised by Solid State 13C‐NMR, FT‐IR, XPS, TEM and N2 adsorption‐desorption tests, and this catalyst possessed a high surface area of 273.7 m2 ⋅ g−1 and uniformly dispersion of CuI. To test its catalytic activity, Cu@CP‐PII in an amount of only 0.4 mol% was used as a catalyst for the carboxylative cyclization reaction of propargylic alcohols with CO2, which resulted in substrate conversions up to 100 % and yields greater than 99 %. In addition, controlled experiments and mechanistic elucidation confirmed that anhydrous and low temperatures are necessary to achieve high selective control of the target product α‐methylene cyclic carbonates. [ABSTRACT FROM AUTHOR]

Rising stress, mental health issues, and suicide rates among farmers highlight the need to understand factors influencing their job satisfaction. Farming presents distinct challenges with its unique mix of positive and negative characteristics. This study utilized dual‐factor theory to investigate how various factors, such as economic dynamics, farm financial health, stewardship views, experience with extreme weather, and climate change concerns, influence farmers' job satisfaction and dissatisfaction. Data from a 2020 survey of Iowa farmers were analyzed using ordinal logistic regression. Financial health, stewardship motivations, and perceived adequacy of conservation practices emerged as significant predictors of both job satisfaction and dissatisfaction. Farmers' extreme weather events experiences were positively associated with job satisfaction, indicating successful coping enhances contentment. Conversely, climate change concern was negatively associated with job satisfaction. As a revenue protection strategy, faith in crop insurance was negatively related to job dissatisfaction. In summary, besides the anticipated positive impact of farm financial health, the findings show that job satisfaction in farming is linked to stewardship motivations and perceived adequacy of conservation practices on the farm. The results suggest an opening for policies and programs aligning financial and conservation goals, potentially enhancing long‐term farmer well‐being and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Rising stress, mental health issues, and suicide rates among farmers highlight the need to understand factors influencing their job satisfaction. Farming presents distinct challenges with its unique mix of positive and negative characteristics. This study utilized dual‐factor theory to investigate how various factors, such as economic dynamics, farm financial health, stewardship views, experience with extreme weather, and climate change concerns, influence farmers' job satisfaction and dissatisfaction. Data from a 2020 survey of Iowa farmers were analyzed using ordinal logistic regression. Financial health, stewardship motivations, and perceived adequacy of conservation practices emerged as significant predictors of both job satisfaction and dissatisfaction. Farmers' extreme weather events experiences were positively associated with job satisfaction, indicating successful coping enhances contentment. Conversely, climate change concern was negatively associated with job satisfaction. As a revenue protection strategy, faith in crop insurance was negatively related to job dissatisfaction. In summary, besides the anticipated positive impact of farm financial health, the findings show that job satisfaction in farming is linked to stewardship motivations and perceived adequacy of conservation practices on the farm. The results suggest an opening for policies and programs aligning financial and conservation goals, potentially enhancing long‐term farmer well‐being and sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Catalytic conversion of glycerol to lactic acid has been considered as one of the effective ways to solve the problem of glycerol surplus. In this paper, we reported a facile synthesis of Cu/ZnO/C by direct calcination of Cu-supported zeolitic imidazolate framework(ZIF-8), and the performance of the catalysts for converting glycerol to lactic acid were investigated. The properties of Cu/ZnO/C catalysts were characterized by XRD, BET, SEM and TG techniques. The results showed that nano-copper was distributed uniformly on the porous carbon carrier. The zinc oxide after thermal treatment provided abundant metal active sites and had synergistic effect with nano copper. In addition, the lactic acid selectivity of 84% was obtained at the glycerol conversion of 95.1% at 230 °C, 1.5 MPa and 15% copper loading for 6 h reaction. Moreover, the yield of lactic acid still remained above 80% after being recycled four times. [ABSTRACT FROM AUTHOR]

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]