Carbon dioxide sequestration through steel slag carbonation: Review of mechanisms, process parameters, and cleaner upcycling pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it