Study on the Mechanism of Competitive Adsorption on the Surface of Potassium Carbonate During Direct Air Capture Process
SEPARATION AND PURIFICATION TECHNOLOGY（2025）
Abstract
Direct air capture carbon dioxide (DAC) technology has the potential to achieve negative carbon emissions. Alkali metal-based absorbents (the active ingredient is mainly potassium carbonate or sodium carbonate) have a broad application prospect in DAC field. There is a paucity of reports on the competitive adsorption of major components of air on adsorbent surfaces. In this paper, the competitive adsorption behaviors of CO2, H2O, N-2, and O-2 on the surface of K2CO3 were investigated based on first principles and density functional theory (DFT), the synergic competition mechanism of each gas during adsorption was revealed by analyzing the parameters of adsorption energy, charge transfer, weak interactions and so on. The results showed that the maximum adsorption energy of CO2 by K2CO3 was 0.557 eV, and the relationship between the maximum adsorption energies of the four gases was O-2 > H2O> CO2 > N-2. The interaction between H2O and CO2 during co-adsorption inhibited their adsorption on the K2CO3 surface, especially weakening the adsorption effect of the surface O atoms on the H2O. Under CO2_N-2_O-2 gas composition, CO2 chemisorption occurred due to the strong oxidizing property of O-2, the charge transfer effect commonly existed in the adsorption process of each gas, which contributed the most to O-2 adsorption. The obtained results can further deepen the decarbonization mechanism of alkali metal-based adsorbents.
MoreTranslated text
Key words
Direct air capture,Potassium carbonate,Competitive adsorption,First principles,Density functional theory
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