Phyllosilicate-derived Ni/SiO2 Catalyst for Liquid-Phase Hydrodeoxygenation of Phenol: Synergy of Lewis Acid Sites and Ni0
FUEL(2024)
Abstract
Designing effective catalysts using non-noble metals for bio-oil hydrodeoxygenation (HDO) into liquid fuels is greatly sought after, yet it remains a great challenge. Contrary to the prevailing belief that monometallic nickel lacked activity in the HDO of oxygen-containing compounds, this study demonstrates a remarkable catalytic performance over monometallic Ni catalyst. Herein, a Ni/SiO2 catalyst having both nickel phyllosilicate and Ni0 was synthesized by the ammonia evaporation (AE) method. The Ni-PS-400 catalyst reduced at 400 degree celsius achieves a significant high activity and yields 97 % cyclohexane at a low reaction temperature of 190 degree celsius, surpassing both Ni-IMP-400 prepared by impregnation method and most of the reported other non-noble metal catalysts which are generally used at high temperature of above 240 degree celsius. It was found that the reduction temperature of Ni-PS-X influences the catalytic activity, as more dispersed Ni nanoparticles and acidic sites can be produced on the surface of the support at an appropriate temperature. The outstanding catalytic performance can be ascribed to the collaborative effect of well-scattered Ni nanoparticles and the significant presence of Lewis acidic sites, which result from coordinatively unsaturated Ni2+ sites situated within the remaining nickel phyllosilicate.
MoreTranslated text
Key words
Nickel phyllosilicate,Phenol,Hydrodeoxygenation,Cyclohexane,Lewis acids
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined