Performance of concrete superficially treated with nano-modified coatings under sulfuric acid exposures
Bibliographic record
Abstract
Protection of concrete surface layer is crucial for preserving serviceability and durability of concrete structures during their service life. Chemical attack by sulfuric acid is an aggressive exposure, commonly causing significant damage to concrete elements such as buildings' foundations and basement walls, buildings and structures affected by acid rain, industrial facilities, and wastewater treatment facilities. Hence, this study endeavored to evaluate the performance of nano-modified composites as surface treatments for concrete under severe chemical exposures. Nano-calcium carbonate and nano-clay particles were dispersed at different dosages (0, 2.5, and 5% by mass) in the neat coatings: vinyl ester (membrane-forming polymer) and silane (hydrophobic agent). Moreover, an amorphous colloidal silica (SiO2>50%) was also used as a superficial treatment for concrete. These coatings were applied to concrete specimens with water-to-binder ratios (w/b) of 0.40 (representing good quality/low penetrability concrete treated for prohibition of deterioration) and 0.60 (representing deteriorated/high penetrability concrete in need for rehabilitation). the wettability and initial transport characteristics of the coated concrete specimens were determined. In addition, the durability of superficially treated concrete specimens was examined under two aggressive exposures: full submergence in a 5% (by volume) sulfuric acid solution, and 5% sulfuric acid solution combined with wetting-drying cycles. During both exposures, the deterioration of concrete specimens was observed by visual inspection and quantified in terms of mass change. Furthermore, the damage mechanisms and coatings’ performance were studied by microstructural, mineralogical, and thermal analyses. The overall results showed that vinyl ester and vinyl ester nanocomposites significantly improved the durability of concrete specimens by an average of around 64% relative to that of the silane and silane nanocomposites; hence, they are recommended for field applications.
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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".