Utilizing alkaline solid waste for low-carbon construction material via in-situ calcium phase design
Bibliographic record
Abstract
Global cement market generates a large amount of greenhouse gases, driving a great interest in developing low-carbon construction materials for climate goals. Although free lime (f-CaO) and low hydration activity limit the applications in construction materials, steel slag, as an alkaline solid waste, is widely regarded as a sustainable alternative to cement. Here, we propose an in-situ calcium phase design strategy of steel slag and develop a high-performance cementitious material through pre-hydration. The pre-hydration effectively reduces the risk of the f-CaO expansion and prevents the occurrence of micro cracks. With the addition of fly ash and alkaline activator, a high elastic modulus Na-rich gel is generated and improves the material’s compressive strength by 133.7%. Carbon footprint analysis indicates that the global-warming potential of the high-performance cementitious material (232–265 kg CO2-eq ton−1) is only about 34-40% of that of cement, helping to reduce about 2.2–3.0 Gt CO2-eq from the global cement market. Interestingly, additional energy compensation (heat or microwave) is proven to expeditiously enhance the mechanical properties of the cementitious material and shorten production cycles without bringing excessive CO2 emissions. This work inspires the strategic utilization of alkaline solid waste in a simple way. The Cement market drives massive GHG emissions, spurring low-carbon material innovation. Here, authors propose a prehydration strategy to convert steel slag into high-performance cementitious material. Crucially, its carbon footprint is just 34–40% of cement’s, offering a positive climate solution.
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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 | 0.000 | 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 | 0.001 |
| 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".