From Lab to Fab: Development and Deployment of Direct Air Capture of CO2
Bibliographic record
Abstract
Direct Air Capture (DAC) is a promising technology to fight climate change by capturing carbon dioxide (CO2) from the air. For DAC to be a negative emissions technology, the captured CO2 must be removed permanently, but can also be used as a net-zero technology to produce sustainable chemicals, fuels or other materials. This review presents a comprehensive survey of recent advancements, challenges, and potential applications of DAC technology, with an emphasis on the recent rapid increase in the number of DAC developers, the majority of them being founded in the past 4 years. Through pilot projects and recent commercial deployments, several DAC companies have made significant advances and demonstrated their scalability. Cost and energy efficiency remain significant impediments to the wide deployment of DAC. Integration with emission-free energy sources and utilization of waste heat are being researched to boost the total energy efficiency of DAC systems. Further research of electrochemical technologies for regeneration or direct capture are needed, as well as the development of new, modified, or hybrid adsorbents for improved capture efficiencies. Moreover, favorable regulations and financial incentives are crucial for enhancing the viability of DAC projects and will need to substantially increase if Paris Agreement goals are to be achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".