Synergic Effects of Corrosive Ions on Concrete and Nano Additives Situated in Nuclear Power Plants in Arid Climatic Conditions
Bibliographic record
Abstract
In arid climates, where temperatures, salt concentrations, and humidity levels are elevated, one significant issue is the increased risk of external sulfate attack (ESA) on the concrete.While the effects of ESA on concrete durability have been widely studied, this work introduces a new experimental approach to improve the understanding of the effects of temperature, thermal and humidity gradients, and sulfate-chloride, and sodium-magnesium interactions.To achieve our objective, both ordinary, SG, and nano-enhanced mortar beams and cubes are used.Samples are exposed to 10% w/v solutions for accelerated testing at room temperature or 50°C.Preliminary findings indicate that chloride mitigates sulfate attack in early stages, but later accelerates sulfate attack, while magnesium delays expansion in the initial stages.Nano-infused samples show better resistance to corrosion when compared to other samples.Further investigation is recommended to elucidate the multiple ion interaction mechanisms and evaluate the performance of nano-infused samples under diverse exposure conditions.
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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".