A New CO2 Sequestration Process via Concrete Products Production
Bibliographic record
Abstract
This paper investigates the possibility of using concrete building products to absorb carbon dioxide during their production and develop high early strength at the same time. Type 10 and Type 30 Portland cements were examined by their abilities to serve as CO2absorbents when exposed to carbon dioxide with 100% and 25% concentrations, the former simulating the recovered CO2and the latter representing the as-captured flue gas without processing. The reaction took place in a chamber under 0.5 MPa pressure, at ambient temperature and in a duration of two hours. CO2uptake was quantified by the direct mass gain and by infrared based carbon analyzer. The performance of carbonated concrete was evaluated by MOR and compressive strength. It was found that the CO2concentration played a critical role in CO2uptake. Two-hour carbonation using 100%, CO2enabled the Portland cement to consume up to 16%, of carbon dioxide, gain a strength equivalent to 2-month conventional curing and have an all-solid section in a 14 mm thickness. Carbonation with 25%, CO2enabled a maximum 9.7% mass gain with lower strength and a partially solid section. Without reinforcing steels, carbonated concrete products can have better strength and durability due to the depletion of calcium hydroxide, and can be fabricated faster than the conventional steam curing. If the process can be shown technically and commercially viable, concrete building products will become more environmental friendly and shall contribute directly to the reduction in global greenhouse gas emission.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".