Physicochemical Impacts of In-Situ Mineralized CaCO<sub>3</sub> on Very Early Hydration of Cement at Two Temperatures
Bibliographic record
Abstract
The addition of carbon dioxide to ready-mix concrete can improve its performance through in-situ mineralization of calcium carbonate. A model system of an oil-well cement paste mixed with and without additions of CO 2 gas was investigated to characterize the impacts of in-situ CO 2 mineralization on the first 15 min of hydration at 4 and 24 °C. Impacts were examined through aqueous chemistry, total inorganic carbon (TIC), stable isotope 13 C/ 12 C fractionation, loss on ignition (LOI), and scanning electron microscopy (SEM). It was demonstrated that the calcium and silicon concentrations increase immediately after the CO 2 input and declined within the first few minutes. TIC testing confirmed that the mineralization was rapid and brief with no evidence of increasing carbonate formation in the minutes after the CO 2 input. Isotope fractionation confirmed that the carbonates formed were attributable to the mineralization of gaseous CO 2 . The loss on ignition testing showed that the CO 2 -activated system had greater mass gain (bound water and bound CO 2 ) over 15 min of hydration than did the reference. SEM observations identified carbonate reaction products <200 nm immediately after the CO 2 injection. Gel products were formed over the ensuing 15 min. The temperature of the testing had little impact other than to increase the concentrations of Ca and Si in solution and increase the amount of mineralized CO 2, as associated with the higher solubility of CO 2 in water as the temperature decreases.
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) | 0.000 | 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) | 0.001 | 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".