A review on progress made in direct air capture of CO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1035" altimg="si1.svg"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math>
Bibliographic record
Abstract
As the concentration of carbon dioxide (CO2) in the atmosphere continues to rise, and the reality of global warming challenges hits the world, global research societies are developing innovative technologies to address climate change challenges brought about by high atmospheric concentration of CO2. One of such challenges is the direct removal of CO2 from the atmosphere. Among all the currently available CO2 removal technologies, direct air capture (DAC) is positioned to deliver the needed CO2 removal from the atmosphere because it is independent of CO2 emission origin, and the capture machine can be stationed anywhere. Research efforts in the last two decades, however, have identified the system overall energy requirements as the bottleneck to the realization of DAC’s commercialization. As a result, global research community continues to seek better ways to minimize the required energy per ton of CO2 removed via DAC. In this work, the literature was comprehensively reviewed to assess the progress made in DAC, its associated technologies, and the advances made in the state-of-the-art. Thus, it is proposed to use traditional heating, ventilation, and air conditioning (HVAC) system (mainly the air conditioning system), as a preexisting technology, to capture CO2 directly from the atmosphere, such that the energy needed to capture is provided by the HVAC system of choice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".