scholarly journals Network-Like Platinum Nanosheets Enabled by a Calorific-Effect-Induced-Fusion Strategy for Enhanced Catalytic Hydrogenation Performance

2022 ◽  
Vol 9 ◽  
Author(s):  
Ting-Wen Chen ◽  
Da-Wei Pang ◽  
Jian-Xin Kang ◽  
Dong-Feng Zhang ◽  
Lin Guo

In this paper, we report the construction of network-like platinum (Pt) nanosheets based on Pt/reduced graphite oxide (Pt/rGO) hybrids by delicately utilizing a calorific-effect-induced-fusion strategy. The tiny Pt species first catalyzed the H2-O2 combination reaction. The released heat triggered the combustion of the rGO substrate under the assistance of the Pt species catalysis, which induced the fusion of the tiny Pt species into a network-like nanosheet structure. The loading amount and dispersity of Pt on rGO are found to be crucial for the successful construction of network-like Pt nanosheets. The as-prepared products present excellent catalytic hydrogenation activity and superior stability towards unsaturated bonds such as olefins and nitrobenzene. The styrene can be completely converted into phenylethane within 60 min. The turnover frequency (TOF) value of network-like Pt nanosheets is as high as 158.14 h−1, which is three times higher than that of the home-made Pt nanoparticles and among the highest value of the support-free bimetallic catalysts ever reported under similar conditions. Furthermore, the well dispersibility and excellent aggregation resistance of the network-like structure endows the catalyst with excellent recyclability. The decline of conversion could be hardly identified after five times recycling experiments.

Author(s):  
Shihang Meng ◽  
Yujing Weng ◽  
Xiaolong Wang ◽  
Hongxing Yin ◽  
Zhenfei Wang ◽  
...  

Biomass furfural-like compounds are chemicals that cannot be extracted from fossil materials, through which a large number of fine chemicals and fuel additives can be opened up, but one big efficiency problem during the transformation is the accumulation of oligomers. Here, we propose a novel and efficient Ru-Mo bimetallic catalyst for selective hydrogenation-rearrangement of furfural-like compounds. The result showed that an unprecedented rearrangement product selectivity of 89.1% to cyclopentanol was achieved under an optimized reaction condition over a 1%Ru−2.5%Mo/CNT catalyst reduced at 600°C. Subsequent characterization suggested that the catalyst presented with weak acidity and strong hydrogenation activity for the reaction, which not only ensures the smooth hydrogenation-rearrangement reaction but also inhibits the accumulation of furan polymers. These findings provide a convenient strategy to tune the catalytic performance of Mo-based catalysts by controlling the reduction and carburization conditions, which appear to be versatile for the rearrangement of furans and similar compounds.


2020 ◽  
Vol 10 (7) ◽  
pp. 2268-2276 ◽  
Author(s):  
Chenguang Li ◽  
Ming Yang ◽  
Zhenjie Liu ◽  
Zhenlin Zhang ◽  
Ting Zhu ◽  
...  

The as-prepared 5 wt% Ru2.5Ni2.5/Al2O3 catalyst shows the best catalytic hydrogenation activity for NPCZ and the lowest hydrogenation activation energy.


2013 ◽  
Vol 678 ◽  
pp. 56-60 ◽  
Author(s):  
Cherukutty Ramakrishnan Minitha ◽  
Ramasamy Thangavelu Rajendrakumar

Reduced graphene oxide is an excellent candidate for various electronic devices such as high performance gas sensors. In this work Graphene oxide was prepared by oxidizing graphite to form graphite oxide. From XRD analysis the peak around 11.5o confirmed that the oxygen was intercalated into graphite. By using hydrazine hydrate, the epoxy group in graphite oxide was reduced then the solution of reduced graphite oxide (rGO) is exfoliated. Raman spectrum of rGO contains both G band (1580 cm-1), D band (1350 cm-1). The remarkable structural changes reveals that reduction of graphene oxide from the values of ID/IG ratio that increase from 0.727 (GO) to 1.414 (rGO). The exfoliated reduced graphite oxide solution is spin coated on to the SiO2/Si substrates.


ChemPhysChem ◽  
2009 ◽  
Vol 10 (11) ◽  
pp. 1763-1765 ◽  
Author(s):  
Shuliang Lu ◽  
Carl A. Menning ◽  
Yuexiang Zhu ◽  
Jingguang G. Chen

2014 ◽  
Vol 48 (1) ◽  
pp. 11-16
Author(s):  
G. A. Kichigina ◽  
P. P. Kushch ◽  
D. P. Kiryukhin ◽  
Yu. M. Shul’ga ◽  
S. A. Baskakov

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