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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.009 | 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 teacher head, 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".