Carbon dioxide sequestration through steel slag carbonation: Review of mechanisms, process parameters, and cleaner upcycling pathways
Bibliographic record
Abstract
The direct carbonation of steel slag has emerged as a promising approach for carbon dioxide (CO2) utilization and sequestration, holding potential for advancing sustainable steel production. Despite considerably high expectations for these cleaner upcycling pathways, their maturity level remains relatively low and large-scale direct carbonation of steel slag is largely untested. To facilitate steel slag carbonation on a scale necessary for a zero-carbon future economy, this article provides a comprehensive review of fundamental carbonation mechanisms and critical parameters governing the reaction process, including temperature, pressure, reaction time, liquid-to-solid ratio, and CO2 partial pressure. The study critically examines the unique interactions among these process parameters, which can either limit or enhance the process optimization. The spectrum of scientific challenges associated with this pathway, including reaction rate limitations and the carbonated product valorization, particularly as a binder or aggregate in the construction sector, are identified and addressed. These insights aim to enhance the carbonation potential of steel slag for possible cleaner upcycling implementation pathways, ultimately facilitating the development of more efficient and sustainable carbon capture utilization and sequestration (CCUS) technologies. The proposed improvements are expected to be instrumental in promoting sustainable practices, not only to foster the decarbonization of the steelmaking industry but also in aiding other hard-to-abate sectors, such as the cement and concrete industry, in achieving their own decarbonization goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".