Effects of Nano-Materials on Key Properties of Cementitious Composites: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cement production industry is one of the largest industries, which is becoming one of the biggest environmental concerns of the world. About 6% of the global CO2 emission comes from this industry. An improvement in cement quality and application process can result in a lot of savings in terms of resources and environmental pollution without hindering developmental needs. A detailed knowledge of the crystal structure of cement hydration products and advanced instrumentations to observe its nano-structure enables researchers and practitioners to undertake nano-modification of cementitious composites. To enhance binder characteristics, improving the nano-level fault is the new trend among the cement and concrete researchers. In this quest, many studies have been conducted by incorporating nano-scaled materials with cement, and observing their effects on the hydration products. To conduct meaningful research in cement and concrete technology, the researchers first need to know the existing knowledge gap and the current developments in this field. This study is an effort to bring the major recent findings together, identifying the gaps and providing the directions for future studies on the application of nano-materials in cementitious composites. This will help the experts to get an outline of this field and also set their goals according to their requirements.
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.
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) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it