Synergistic Impact of Sustainable Graphene Derivative and Dune Sandon Cement Mortars
Bibliographic record
Abstract
This study investigates the impact of a sustainable graphene derivative, designated as D-GSH, on the compressive strength development of cement mortars.The research evaluates the effects of D-GSH addition by 0.25%, by weight of cement, and dune sandto-cement ratios on compressive strength at 1, 7, and 28 days.Compressive strength tests reveal that the addition of 0.25% D-GSH significantly enhances strength across all age intervals, with increases of 13%, 55%, and nearly 50% at days 1, 7, and 28, respectively.Conversely, increasing the dune sand-to-cement ratio from 1:1 to 1:3 adversely affects early strength development.While the higher sand content initially boosts strength, its long-term performance is hindered, highlighting the necessity for optimal dune sand content in cement mortars.The findings demonstrate that D-GSH effectively accelerates early strength gain, making it a viable alternative for improving the performance of cement-based composites.
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".