No abstract

No abstract

In this work, a series of Cu/ZnO/ZnAl 2 O 4 catalysts with different metal molar fractions (Cu : Zn : Al) were successfully prepared using a one-pot method via the evaporation-induced self-assembly (EISA) of Pluronic P123 and the corresponding metal precursors. The catalysts were characterized using N 2 adsorption, H 2 temperatureprogrammed reduction (H 2 -TPR), X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectra (XPS). The catalytic properties of the resulting Cu/ZnO/ZnAl 2 O 4 with different molar fractions of metals were investigated for the selective hydrogenolysis of glycerol to 1,2-propanediol (1,2-PDO). It was observed that the ZnAl 2 O 4 support exerts a strong positive effect on the catalytic activity of the copper-based catalysts, and the presence of ZnO further improves the catalytic activity of the Cu/ZnAl 2 O 4 catalysts. The Cu/ZnO/ZnAl 2 O 4 catalyst (Cu 10 Zn 30 Al 60 , Cu/Zn/Al molar ratio is 10 : 30 : 60), which was the best catalyst, exhibited the highest yield (79%) of 1,2-PDO with 85.8% glycerol conversion and 92.1% 1,2-PDO selectivity at 180 °C reaction temperature in 80 wt% glycerol aqueous solution over 10 h reaction time. The high catalytic activity was attributed to the presence of the ZnAl 2 O 4 support, the strong interaction between ZnO and Cu nanoparticles and the small particle size of ZnO and Cu. Moreover, the Cu/ZnO/ZnAl 2 O 4 catalysts exhibited higher stability than Cu/ZnO and Cu/ZnO/Al 2 O 3 catalysts prepared by a co-precipitation method during consecutive cycling experiments, which is due to the high chemical and thermal stability of crystalline ZnAl 2 O 4 under harsh reaction conditions.

No abstract

Pt/N-doped carbons with extra framework magnesium catalysts exhibit high activity and selectivity in glycerol oxidation to tartronic acid under base-free conditions.

Although the pre-trained Vision Transformers (ViTs) achieved great success in computer vision, adapting a ViT to various image and video tasks is challenging because of its heavy computation and storage burdens, where each model needs to be independently and comprehensively fine-tuned to different tasks, limiting its transferability in different domains. To address this challenge, we propose an effective adaptation approach for Transformer, namely AdaptFormer, which can adapt the pre-trained ViTs into many different image and video tasks efficiently. It possesses several benefits more appealing than prior arts. Firstly, AdaptFormer introduces lightweight modules that only add less than 2% extra parameters to a ViT, while it is able to increase the ViT's transferability without updating its original pre-trained parameters, significantly outperforming the existing 100% fully fine-tuned models on action recognition benchmarks. Secondly, it can be plug-andplay in different Transformers and scalable to many visual tasks. Thirdly, extensive experiments on five image and video datasets show that AdaptFormer largely improves ViTs in the target domains. For example, when updating just 1.5% extra parameters, it achieves about 10% and 19% relative improvement compared to the fully fine-tuned models on Something-Something v2 and HMDB51, respectively.

No abstract

Herein, N-doped carbon embedded Ni catalysts were prepared by co-impregnation method followed by pyrolysis in N2 atmosphere for transfer hydrogenolysis of lignin β-O-4 model compounds using isopropanol as the hydrogen...