CO<sub>2</sub> sequestration using calcium-silicate concrete
Bibliographic record
Abstract
The potential for using calcium silicate concrete to sequester CO2 and simultaneously develop strong and durable concrete building products is studied. It is the calcium compounds in cement that react with CO2 through the early-age carbonation curing, forming geologically stable calcium carbonates. Both type 10 and type 30 Portland cements were investigated as CO2 binders in concretes with 0%, 25%, 50%, and 75% quartz aggregates and lightweight aggregates. The sequestration took place in a chamber under 0.5 MPa pressure at ambient temperature for a duration of 2 h; a 100% concentration of CO2 was used to simulate the recovered CO2 from flue gas. The CO2 uptake was quantified by direct mass gain and by infrared-based carbon analyzer, and the performance of the carbonated concrete was evaluated by its strength. A CO2 uptake of 9%16% by binder mass was achieved in 2 h. The X-ray diffraction spectra showed the presence of strong calcite peaks and a total absence of Ca(OH)2. The 2 h carbonation strength exceeded the 7 d hydration strength. The calcium silicate concrete approach is shown to be feasible for CO2 sequestration and would result in technical, environmental, and economical benefits.Key words: CO2 sequestration, concrete, carbonation curing, calcium carbonates, strength.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".