Abstract
Amine-impregnated solid adsorbents are widely explored for point source capture and direct air capture (DAC) to address climate change. Existing literature serves as a valuable source for the investigation of amine-functionalized solid adsorbents. This study selected 52 articles from bibliographic platforms using GPT-assisted data source screening. A total of 1,336 data points were manually collected. Each data point is characterized by 28 features including the CO2 capture performance of various adsorbents from diluted to concentrated sources, resulting in 29,857 records. The methodology addresses inconsistencies in units and terminologies in the published articles and demonstrates database reliability, regularity and integrity through statistical analysis. The diverse types of amines and mesoporous solids in the database offer innovation potential for future research. In addition, two machine learning models were trained to promote dataset reuse by scientists from lab-based research and cheminformatics. This study provides opportunities to explore the use of machine learning on small databases and encourages data sharing and uniform reporting among DAC communities.
| Original language | English |
|---|---|
| Article number | 724 |
| Journal | Scientific data |
| Volume | 12 |
| Issue number | 1 |
| Early online date | 1 May 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 1 May 2025 |
Bibliographical note
Publisher Copyright:© The Author(s) 2025.
Funding
The authors would like to acknowledge the Science and Technology Commission of Shanghai Municipality (STCSM) for the financial support (no. 21DZ1206200). The authors would also like to express their appreciation for the funding support from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference Number: T32-615/24-R). The authors are deeply grateful to the Consensus project, which received funding from the European Union’s Horizon 2020 Research and Innovation program (no. 101022484).
