Growth of Cement Hydration Products on Single‐Walled Carbon Nanotubes
Bibliographic record
Abstract
Single‐walled carbon nanotubes (SWCNT) were distributed on the surface of ordinary Portland cement (OPC) grains. The OPC/SWCNT composite was then hydrated at a 0.5 w/c ratio. The effects of the SWCNT on the early hydration process were studied using isothermal conduction calorimetry, high‐resolution scanning electron microscopy and thermogravimetric analysis. The observed behavior of the composite samples was compared with both OPC sonicated without SWCNT and previously published data on as‐delivered OPC. The SWCNT were found to accelerate the hydration reaction of the C 3 S in the OPC. The morphology of both the initial C 3 A and the C 3 S hydration products were found to be affected by the presence of the SWCNT. In particular, the nanotubes appeared to act as nucleating sites for the C 3 S hydration products, with the nanotubes becoming rapidly coated with C–S–H. The resulting structures remained on the surface of the cement grains while those in the sonicated and as‐delivered OPC samples grew out from the grain surfaces to form typical C–S–H clusters. Classical evidence of reinforcing behavior, in the form of fiber pullout of the SWCNT bundles, was observed by 24 h of hydration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".