Boosting cement hydration with boron nitride nanotubes
Bibliographic record
Abstract
In recent years, there has been a growing interest in the use of nanomaterials as additives in various industries, including cement production. Among these materials, carbon-based nanomaterials, such as graphene and graphene oxide, have been extensively studied for their potential applications in cementitious materials. However, recent research has shown that boron nitride nanotubes (BNNT) can offer superior properties compared to their carbon-based counterparts. This study compared the properties of BNNT with those of graphene and graphene oxide when used as additives in cementitious materials. The hydration process of the nanomodified cementitious composite was studied using in situ calorimetry measurements over a period of seven days, and thermogravimetric analysis (TGA), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), and Field Emission Scanning Electron Microscopy (FESEM) over a period of 28 days. These techniques provide insights into the mechanisms of cement hydration and the impact of boron nitride nanotubes on cementitious composites. The results demonstrate that the addition of BNNT significantly reduced the induction period during cement hydration, indicating that BNNT can enhance the reactivity of cement. Furthermore, BNNT accelerate the hydration process because of their high surface area. Phase identification by XRD peaks showed that the BNNT reinforcement could regulate the microstructure of the cementitious composites. These findings suggest that BNNT has the potential to be a more effective and efficient additive in cementitious materials than graphene and graphene oxide. The use of BNNT in cement production can lead to the development of high-performance, durable, and sustainable materials for various construction applications. • Cement exhibit good compatibility with boron nitride nanotubes (BNNT). • With BNNT, a significant acceleration was observed in the induction period. • The portlandite and ettringite content increases with BNNT. • Elongation of the chain length with BNNT observed by NMR.
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 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".