Performance of carbon nanotubes in mortar using different surfactants
Bibliographic record
Abstract
The performance of carbon nanotubes (CNTs) in cement-based composites relies to a great extent on its degree of dispersion. In this work, the performance of two commonly used surfactants; sodium dodecyl sulfate (SDS) and Triton X-100, is being compared. The effect of surfactant-to-CNT ratio on dispersion efficiency is studied using ultraviolet-visible (UV-Vis) spectrometry, to determine the optimum surfactant dosage. For the optimum ultrasonication energy, Raman spectroscopy is used to assess the degree of imperfections on CNTs. CNTs-reinforced mortar specimens prepared using Triton X-100 and SDS are tested for compressive and flexural strength. Triton X-100 is shown to exhibit better dispersion efficiency than SDS, leading to greater improvement in flexural and compressive strength. An ultrasonication time of 60 min (19.4 kJ/mL) is shown to be sufficient to achieve proper dispersion, however notable degradation of CNTs was noted beyond 30 min (9.7 kJ/mL) of dispersion leading to a strength reduction.
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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".