Influence of various additives on the early age compressive strength of sodium carbonate activated slag composites: An overview
Bibliographic record
Abstract
Abstract The use of sodium carbonate as an alkali activator for slag to produce alkali-activated slag is promising due to its sustainable, economic and user-friendly properties. However, the lower early age performance of composites made with such binder has limited its use especially in applications where higher early age is required. Hence, in order to propel the application of this sustainable binder, it is imperative to find ways in which the early age performance can be enhanced without having a detrimental effect on later age performance. One of the effective and sustainable ways to enhance the early age strength of sodium carbonate activated slag is by incorporation of various additives as partial replacement of sodium carbonate on/and slag. In order to propel more application of sodium carbonate slag for various applications, this current study was undertaken. In this paper, an overview of the types of various additives that can be used to enhance the early age compressive strength of sodium carbonate activated slag composites was discussed. The mechanism and dosage of each of the additives were briefly discussed alongside the limitation and advantages of the additives. Findings from this overview showed that the early age compressive strength of sodium carbonate activated slag can be enhanced with the use of additives such as calcium oxide, calcium hydroxide, Portland cement, sodium hydroxide and sodium silicate.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".