State-of-the-art report on use of nano-materials in concrete
Bibliographic record
Abstract
Nanotechnology application to concrete presents an innovative approach to improve concrete properties based on the ability to manipulate the cementitious material at an atomic scale. This paper presents a review of the nano-materials that have been used in concrete. The literature survey revealed that four nano-materials are most often used to modify concrete properties; these include nano-silica (nano-SiO2), nano-titanium dioxide (nano-TiO2), carbon nano-tubes (CNTs) and carbon nano-fibres (CNFs). All of these four nano-materials have shown improvement in many concrete properties. Both nano-TiO2 and nano-SiO2 reduce bleeding and segregation, and improve mechanical and transport properties. CNFs and CNTs tend to adversely affect the fresh properties due to agglomerations, which are overcome when a surfactant or ultrasonic mixer is used. However, both CNFs and CNTs significantly improve the mechanical properties of concrete. This paper also discusses how concrete durability is improved when nano-materials are added to concrete. In addition, this paper identifies several research needs based on the gaps in the current state of knowledge on using nano-materials in concrete.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".