Temperature-regulated ammonium carbonate curing of steel slag: Enhanced carbonation, strength, and CO2 mineralization for sustainable building materials
Bibliographic record
Abstract
This study systematically examines the effects of temperature on the carbonation consolidation of steel slag in ammonium carbonate solution, aiming to develop sustainable building materials and improve CO 2 mineralisation efficiency. By exposing steel slag compacts to curing temperatures of 20°C, 40°C, and 60°C, the research shows that higher temperatures significantly enhance both the compressive strength and carbonation conversion of the material. Specifically, the 60°C curing group achieved a peak compressive strength of 118.38 MPa, which is 114% greater than at 20°C, emphasizing the vital role of temperature in speeding up reaction kinetics and fostering the formation of strong carbonation-hydration products. Comprehensive characterizations—including uniaxial compression, total carbon analysis, XRD, TG-DTG, FT-IR, and SEM—indicate that higher temperatures promote Ca 2+ dissolution, increase calcium carbonate crystallinity, and encourage the development of denser microstructures. The addition of ammonium carbonate not only aids mass transfer and Ca 2+ extraction but also introduces a new mineralisation pathway involving carbamate ions. These findings offer a theoretical and experimental basis for optimizing the carbonation process of steel slag, advancing its use as an eco-friendly construction material with significant CO 2 sequestration potential.
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".