CO₂‐air‐capture - OU-Weather-and-Climate-Club/GGR-Data-Visualisation-Challenge GitHub Wiki

Use machines to capture CO₂ from the air

https://www.imperial.ac.uk/Stories/9-ways-to-remove-carbon-dioxide-from-the-air-2/

In a nutshell 👇

  • Technology called ‘Direct Air Carbon Capture and Storage’ (DACCS) uses chemical reactions to remove CO2 from the air and store it underground

  • Direct air capture machines push air over chemicals that filter out the CO2 through a chemical reaction. The captured CO2 can then be injected deep underground for long-term, geological storage.

  • Whilst there are significant challenges, there is great potential: CO2 is currently only being removed at a small scale via DACCS, but progress is being made through innovation and investment. The 'State of CDR' report found that of all patents for Carbon Dioxide Removal methods, DAC holds the biggest share.


Key report:

The State of Carbon Dioxide Removal Report: 2nd Edition | 2024

https://www.stateofcdr.org/

Recording of launch event exploring the report: https://www.youtube.com/live/LH6MxKKQ13I?si=T3G1BBxQt-En33lU

This report is a global independent scientific assessment of the state of CDR: the author team are 50 independent researchers; it covers the current state of research and development of current methods; the state of government and economic activities; an analysis of popular awareness and communication; current levels and future projections of CDR.


ClimeWorks: 'Mammoth', the world's largest direct air capture plant, begins operations in Iceland

A significant development too recent to be included in the State of CDR report

https://youtu.be/IT5NSZKjHIo?si=MmzoC29mDPK3Qlmv


Open-Source Data on Direct Air Carbon Capture and Storage

The OpenDAC project is a collaborative research project between Fundamental AI Research (FAIR) at Meta and Georgia Tech, aimed at significantly reducing the cost of Direct Air Capture (DAC)

To engage the broader research community as well as the budding DAC industry, we have released the OpenDAC 2023 (ODAC23) dataset to train ML models. ODAC23 contains nearly 40M DFT calculations from 170K DFT relaxations involving Metal Organic Frameworks (MOFs) with carbon dioxide and water adsorbates. We have also released baseline ML models trained on this dataset.