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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".