Durability of Concrete Superficially Treated with Nano-Silica and Silane/Nano-Clay Coatings
Bibliographic record
Abstract
Protection of the surface layer of concrete is essential for achieving durability and functionality of concrete elements during their service life. In this paper, an effort is made to utilize colloidal nano-silica (5%–50%) and a synthesized nanocomposite as superficial treatments for concrete; silane was used as the neat resin to disperse nano-montmorillonite particles at different dosages (5% and 10%). The coatings were applied to a typical concrete mixture used for residential concrete in North America. The transport properties of the treated concrete were characterized using the rapid chloride penetrability test and the absorption/desorption percentages. Moreover, concrete was evaluated under severe durability exposure involving physical salt attack (PSA), which is a wetting/drying regime responsible for surface damage of concrete elements subjected to continuous salt supply along with cyclic ambient conditions. Deterioration was visually assessed and quantified using mass change. In addition, thermal and microscopy analyses were performed on concrete specimens to elucidate the mechanisms of enhancement by surface treatment. The results showed that increasing the concentration of nano-silica particles in the colloid led to an improved performance of concrete, with the 50% loading ratio achieving the least penetration depth, absorption/desorption percentage, and mass loss of concrete under aggravated PSA. For the silane/nano-clay composite, the low dosage of nano-clay was adequate to mitigate the damage caused by PSA on 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 | 0.000 | 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".