Effects of C-S-Hs-PCE and Na2SO4 on hydration behavior of cement-lithium slag binder
Bibliographic record
Abstract
Abstract Application of C-S-Hs-PCE and sodium sulfate into Portland cement containing 20 wt% lithium slag (LS) powder was investigated, in order to strengthen early mechanical properties. Synergistic effects of C-S-Hs-PCE and sodium sulfate on hydration properties and microstructure of cement-LS system were analyzed. Results showed that C-S-Hs-PCE was advantageous for modifying fluidity of fresh LS-cement binder, while increased dosage of sodium sulfate decreased dispersibility of fresh paste. C-S-Hs-PCE and sodium sulfate exhibited a synergistic effect on strength enhancement, hydration acceleration as well as setting behavior of LS-cement binder. Sodium sulfate increased alkalinity of interstitial solution and promoted dissolution of LS. Dissolved Al and Si from LS powder reacted with dissolved sulfate ions from sodium sulfate to produce extra hydrates, and C-S-Hs-PCE accelerated pozzolanic reaction as well as hydration reaction via nucleation effect collaborated with dispersing effect. C-S-Hs-PCE accelerated reaction process of sodium sulfate via nucleation effect, and activation effect of sodium sulfate provided more newly-formed hydrates to act as nucleation seeds or crystal skeleton for induce hydration of new phases. The accelerated hydration generated more AFt and C-S-H gel in the matrix. Newly formed hydrates promoted exceedingly the appearance of network, leading to a refinement of pore structure as well as enhancement in mechanical strength. Application of LS into cement as a greener binder could be obtained by synergistic adoption of C-S-Hs-PCE and sodium sulfate.
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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 | 0.000 |
| 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".