Properties of Alkali-Activated/Cement Paste as Coating Material
Bibliographic record
Abstract
This paper presents the properties of an alternative coating material produced from alkali-activated/cement powder (AACP) paste, including setting time, strength development, and bond strength of AACP paste-coated reinforcing steel bars in concrete.The AACP paste was prepared by activating a mixture of dry geopolymer powder (GPP), Portland cement (PC), and silica fume (SF) with tap water and 2M sodium hydroxide solution.The effect of PC replacement and sodium silicate-to-sodium hydroxide solution (SS-to-SH) ratio in the production of GPP on the properties of AACP paste were also investigated.Test results exhibited that the incorporation of FA and PC activated with an SS-to-SH ratio of 2.0 in the production of GPP decreased their setting time of fresh AACP pastes while marginally increasing their strength development.In addition, AACP paste-coated reinforcing steel bars positively affected the bond strength of reinforced concrete, especially for the GPP produced by FA without PC.The increased reaction products at the contact zone could contribute to a strengthening of their bonds.It can be recommended that the use of FA without PC activated with a low SS-to-SH ratio in the production of GPP would be beneficial for improving bond strength.
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".