Pickering High Internal Phase Emulsions Stabilized by Metal-Organic Framework Nanoribbons
Journal of Molecular Liquids(2024)
Beijing Univ Chem Technol | Petrochina | Univ Sci & Technol China | Chinese Acad Sci
Abstract
Pickering high internal phase emulsions (HIPEs) stabilized by colloidal particles are of great practical significance in the formulation of food, pharmaceutical, cosmetic, paint products and the chemical oil flooding. Herein, we report that a manganese-based metal-organic framework (MOF) (Mn3(BTC)2 (BTC = 1,3,5-benzenetricarboxylate)) nanoribbons synthesized by a simple solution method can stabilize water-in-oil (W/O) high viscosity Pickering HIPEs with crude oil as oil phase for more than one month at 43 degrees C. The Mn3(BTC)2 nano ribbons assemble at the oil/water interface of HIPE, owing to its advantageous structural features with the size of several micrometers, thickness of about 100 nm and amphiphilicity originated from the hydrophilic metal ions and hydrophobic organic ligands. The phase behavior and properties of the as-prepared HIPEs strongly depend on the MOF concentration (CMOF) and internal phase volume fraction of water (Fw) of emulsion. The MOF dispersions and crude oil are miscible into stable W/O HIPEs at Fw = 75 % when the CMOF ranges from 50 ppm to 5000 ppm. More interestingly, the viscosity of W/O emulsions greatly increases as the Fw varying from 50 % to 80 % and a viscosity of 4.5 Pa & sdot;s is obtained at CMOF = 2000 ppm and Fw = 80 %, which is nearly 400 times higher than that of crude oil. Moreover, the pressure generated by HIPEs flowing in core is about 40 times more than that of water injection and can remain the highest stable value substantially. These results show that the MOF dispersions are favorable to form stable W/O emulsions with high internal phase volume fraction, which has a great potential in improving oil displacement efficiency of chemical flooding.
MoreTranslated text
Key words
Pickering high-viscosity HIPEs,MOF nanoribbons,Fluorescence performance,Oil displacement efficiency,Chemical flooding
求助PDF
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
Related Papers
2007
被引用235 | 浏览
2016
被引用118 | 浏览
2016
被引用77 | 浏览
2020
被引用417 | 浏览
2017
被引用176 | 浏览
2018
被引用28 | 浏览
2017
被引用16 | 浏览
2019
被引用117 | 浏览
2020
被引用89 | 浏览
2020
被引用18 | 浏览
2021
被引用41 | 浏览
2021
被引用42 | 浏览
2021
被引用24 | 浏览
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