Direct Air Capture of CO2 Using Bi-Amines-functionlized Hierarchical Mesoporous Silica: Effects of Organic Amine Loading, Moisture and Temperature
SEPARATION AND PURIFICATION TECHNOLOGY(2025)
Shandong Univ Sci & Technol
Abstract
With the rapid increase of carbon dioxide (CO2) in the atmosphere, it has become urgent to reduce CO2 emissions. It is imperative to develop low-cost and high-efficiency CO2 direct air capture adsorbents. In this research, hierarchical mesoporous silica (HMS) was easily synthesized through a partitioned synergistic self-assembly method, and amine bi-functionalized HMS adsorbents were prepared using 3-[2-(2-Aminoethylamino)ethylamino]propyl-trimethoxysilane (DT) and tetraethylenepentamine (TEPA) employing various techniques. A high specific surface area (570 m(2)/g) and large pore volume (2.29 cm(3)/g) were obtained for the HMS with two types of pores (6.32 nm and 17.38 nm). The structural composition of amine-functionalized solid adsorbent affected the adsorption-desorption performance of CO2. Multi-stage pore structure with three-dimensional connection can effectively enhance the adsorption-desorption performance of adsorbent. The stable adsorption performance, the effects of organic amine loading, adsorption mechanism, relative humidity and adsorption temperature on the CO2 adsorption properties of D-HMS-T-50 were examined. The CO2 adsorption capacity of D-HMS-T-50 reached 4.99 mmol/g at 338 K, 1 bar and 30 % relative humidity. Additionally, a lower heat consumption of desorption (79.92 KJ/mol) is needed for D-HMS-T-50. The results proved that D-HMS-T-50 with high adsorption capacity and low regeneration energy can be provided as a solid amine adsorbent for direct air capture of CO2.
MoreTranslated text
Key words
Bi-functionalization,Diethylenetriaminopropyltrimethoxysilane,Tetraethylenepentamine,Hierarchical mesoporous silica,CO(2)capture
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
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