Directional Effects of Sustainable Graphene Derivatives on the Flexural Strength of 3D-Printed Cement Composites
Bibliographic record
Abstract
This study investigates the anisotropic properties of a novel and sustainable graphene derivative, specifically a date syrupbased graphene-coated sand hybrid (D-GSH), incorporated into 3D-printed cement mortar (3DPC).The flexural strength of 3D-printed beams was determined after 7 days of curing by varying the loading directions, i.e. parallel and perpendicular to the printing direction for the mixes containing D-GSH, and the results were compared with a mix containing silica fume.The flexural strength increased when the force was applied parallel to the printing direction, which is due to better load distribution and stronger bonding of the 3DPC.In contrast, when the load was applied perpendicular to the printing direction, the strength was reduced due to weaker interlayer bonding.For example, a mixture with 5% silica fume showed a 25% increase in flexural strength when the load was applied parallel to the printing direction as opposed to perpendicular.On the other hand, mixes with 0.3 wt% D-GSH and 0.5 wt% D-GSH showed improvements of 11.6% and 9.5% respectively.As a result, adding D-GSH reinforced the layer interface and reduced the variance in flexural strength between the two loading orientations, thereby enhancing interlayer bonding in the 3D-printed structures.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Materials engineering study of graphene derivatives in 3D-printed cement.
The study examines cement composite strength, not research methods or the research system.
Materials engineering study of graphene-reinforced 3D-printed cement, a domain technical object.
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".