Influence of ageing of graphene oxide on the properties and morphology of cement mortar
Bibliographic record
Abstract
Past studies show that Graphene Oxide (GO) enhances the structural properties of cement composites. However, GO reduces its chemical characteristics with ageing. This study determines the effects of the age of commercial and laboratory-produced GO on cementitious composites. The study considered GO of up to 35 weeks of age, and specimens were chemically characterised using various techniques. The ageing effects were evaluated using consistency, initial setting time, compressive strength, splitting tensile strength, and water absorption. The composite's thermal resistance was also tested. GO was found to have a shelf life of 13 weeks from production to achieve favourable results. The morphology of the cement mortar was studied to determine the reason for the change in performance with GO age. This study confirms that the carbon-to-oxygen ratio (C/O) and the disorder of graphene oxide sheets (ID/IG ratio), along with the number of GO layers, govern the performance of GO-incorporated cement composites. Both ratios increase with GO age. Aged GOs in mortar increased the mean pore radius and reduced the surface area. Mortar samples with aged GOs have ettringite peaks, while early-age GO-containing samples lack ettringite peaks. Despite reduced mechanical performance with age, all mortar samples remained thermally stable at higher temperatures.
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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".