Effect of dual-modified CNTs on strength and chloride resistance of cementitious systems
Bibliographic record
Abstract
This study is undertaken to explore, first, the dispersibility of dual-modified carbon nanotubes (CNTs) by way of non-covalent modification. Various non-ionic and ionic surfactants were employed and mutually combined with varying relative proportions. Then, the best few combinations from the dispersion test were used further for producing mortar mixtures reinforced with CNTs. These samples were later assessed for their mechanical strength and chloride resistance. A suite of morphological, thermal and microstructural characterisations was carried out to understand the underlying mechanisms. The results show that, compared with the single modification, the dispersibility of CNT could be improved more significantly by way of the dual modification. In particular, 70–90% of non-ionic surfactant, in proportion to the total surfactant addition, imparted the best dispersibility to CNTs in an aqueous solution. In addition, X-ray diffraction, thermogravimetric analysis, mercury intrusion porosimetry and scanning electron microscopy outputs reveal that the enhanced dispersion of CNTs by dual modification promoted the hydration process and the ensuing microstructure evolution of mortar specimens. Together, these offset the strength reduction imparted by entrained pores when introducing chemical surfactants and, more importantly, empowered the chloride resistance of CNT-reinforced mortars.
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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) | 0.000 | 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".