Effects Of Graphene Oxide On The Early-Age Hydration Of SCMModified Low-Carbon Cementitious Composites
Bibliographic record
Abstract
While graphene oxide (GO) has been shown to significantly improve the early-age hydration and mechanical properties of clinker-based cementitious materials, additional supplementary cementitious materials (SCMs) such as ground granulated blast-furnace slag (GGBS) can slow the early age hydration rate, while the opposite can occur with addition of limestone fines (LF).These SCMs in combination with GO can cause interdependent interactions which could potentially offset their individual drawbacks, however the nature of the GO-, GGBS-and LF-cement interactions remains unclear.Towards understanding this, the present study investigates the effects of GO on the early-age hydration of GGBS and LF modified low-carbon cementitious composites.The heat of hydration rate, cumulative heat of hydration, and relative setting time of cement pastes incorporating GO (0.08% by weight of binder), GGBS (0-60% by weight of binder), and LF (0-20% by weight of binder) were evaluated using an isothermal calorimeter measurement method.The results show that GO can significantly reduce the dormant period of cement hydration, shift the hydration heat rate peaks upwards and earlier, and improve the overall hydration degree by accelerating the hydration process in cement.The combination of GO and supplemental cementitious materials offers a promising avenue for SCMs-modified binders with enhanced performance and reduced carbon footprint.
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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".