Recycling carbon dioxide into concrete: a feasibility study
Bibliographic record
Abstract
The feasibility of recycling carbon dioxide into concretes through their curing processes was investigated. The CO2 uptake capacities of four commonly used concrete building products: masonry block, paving stone, cement board and fiberboard, were evaluated. It was found that, based on the cement content used in the product, the uptake capacities of the four products ranged from 9.8% to 18.9% when recovered CO2 was used in a static system and from 6.3% to 8.1% when as-captured cement kiln flue gas was used in a quasi-dynamic system. In the United States and Canada, annual cement consumption by these four products is approximately 14 million tonnes (300 billion lb). If all of these products were carbonation treated, the net annual sequestration of CO2 in concrete could reach 1.8 million tonnes (4 billion lb) using recovered CO2 (at a net efficiency of 87.1%) and 0.98 million tonnes (2.1 billion lb) using flue gas (at a net efficiency of 84.0%). The proposed process offers a feasible method of safe and permanent sequestration of carbon dioxide in manufactured concrete products. Either recovered carbon dioxide or flue gas could be used, though the direct use of flue gas would save considerable energy and offset the costs associated with the CO2 recovery process. Reaction efficiency is nevertheless lower since there is a smaller fraction of carbon dioxide available for reaction at a given pressure. The direct use of flue gas as a carbonation curing agent is thus only appropriate for situations in which curing is performed adjacent to a CO2 point source. On the other hand, recovered CO2 is expected to become available at low cost in the near future and could be shipped to concrete plants as a curing agent to replace steam in precast concrete production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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 teacher head, 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".