Effect of nano-graphene on the geomechanical and microstructural properties of cemented fine sand
Bibliographic record
Abstract
This study examines the influence of nano-graphene on the mechanical and microstructural characteristics of cement-stabilized sand. The samples are stabilized with 3 %, 6 %, and 9 % cement, and nano-graphene is added in varying amounts, ranging from 0 % to 1.2 % to investigate its impact. A comprehensive experimental program is conducted, including compaction tests, unconfined compressive strength (UCS) tests, one-dimensional settlement, direct shear tests, and microstructural evaluations using SEM, XRF, and XRD to examine the strength and stiffness of cemented sand in different confinement conditions. The findings indicate that nano-graphene significantly enhances soil behavior. The UCS results revealed that incorporating 1 % nano-graphene led to a 17 %, 21 %, and 20 % increase in strength for samples containing 3 %, 6 %, and 9 % cement, respectively. The secant modulus (E 50 ) showed a 38 % rise at optimal nano-graphene content, highlighting its stiffening effect. Improvements in strength parameters were most notable in the 6 % cement and 0.8 % nano-graphene mixture, which was identified as the optimum combination. At this dosage, the one-dimensional settlement decreased by approximately 11 %, while cohesion and internal friction angle increased by about 72 % and 17 %, respectively, compared to the control cemented sample. However, at higher nano-graphene concentrations, particle agglomeration limited further improvements. The microstructural analysis provided additional insights, with SEM images showing enhanced interparticle bonding and matrix densification. The XRF results confirmed increased CaO content and intensified hydration reactions, while the XRD analysis identified the formation of supplementary cementitious phases, reinforcing mechanical improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".