Effect of Polymer/Nano-Clay Coatings on the Performance of Concrete with High-Content Supplementary Cementitious Materials under Harsh Exposures
Bibliographic record
Abstract
In recent concrete research, a novel category of coatings has emerged: polymers/nanoparticles blends. The efficacy of such coatings warrants extensive examination across various concrete mixtures, particularly those incorporating high-volume supplementary cementitious materials (SCMs) to mitigate carbon footprints, an industry imperative. This study used three vulnerable concrete mixtures to assess the effectiveness of ethyl silicate and high-molecular-weight methyl methacrylate blended with 2.5% and 5% halloysite and montmorillonite nano-clay. Findings from physical, thermal, and microstructural analyses confirmed vulnerabilities in concretes with a high water-to-binder ratio (0.6) under severe exposure conditions, notably with high SCM content (40% and 60% fly ash and slag, respectively). Neat ethyl silicate or high-molecular-weight methyl methacrylate coatings inadequately protected those concretes against physical salt attacks and salt-frost scaling exposures. However, the incorporation of halloysite nano-clay or montmorillonite nano-clay in these polymers yielded moderate-to-superior concrete protection compared to neat coatings. Ethyl silicate-based nanocomposites provided full protection, achieving up to 100% improvement (no or limited surface scaling) against both exposures, particularly when incorporating halloysite-based nano-clay at a 2.5% dosage by mass. In contrast, high-molecular-weight methyl methacrylate-based nano-clay composites effectively mitigated physical salt attacks but exhibited insufficient protection throughout the entire salt-frost scaling exposure, peeling off at 15 cycles.
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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".