Nano-Modified Coatings for Concrete Protection against Magnesium Sulfate
Bibliographic record
Abstract
Concrete structures exposed to sulfate-rich environments in coastal regions are susceptible to durability issues caused by magnesium sulfate (MgSO4) attack. The existing guidelines/codes recommendations are to use sulfate-resistant cement with/without supplementary cementitious materials and a low water-to-binder ratio (w/b). In general, this strategy is highly effective at protecting concrete from sodium sulfate attack. However, any cementitious matrix containing calcium-silicate-hydrate and calcium hydroxide remains vulnerable to decomposition by magnesium sulfate, highlighting the need for additional protective measures. Hence, this study aimed to evaluate the effectiveness of nano-modified coatings in protecting concrete from magnesium sulfate attack. Silane, functioning as a pore liner and waterproofing agent, and vinyl ester, serving as a membrane coating, were utilized as dispersing media for nanoparticles (nano-clay and nano-calcium carbonate at 0, 2.5, and 5% by mass) to develop nano-modified coatings. A colloidal nano-silica solution was also examined as another coating. They were applied to high-quality (w/b=0.4) and low-quality (w/b=0.6) concrete surfaces, which were then exposed to a 10% magnesium sulfate solution under full immersion and combined with cyclic exposures of wet-dry and freeze-thaw conditions. Besides the initial fluid transport properties, the performance of the coated concrete was evaluated by monitoring mass change, modulus of elasticity, and length change of concrete prisms, and the trends were elucidated by thermal, mineralogical, and microscopy analyses. The colloidal silica treatment resisted the initial fluid penetration but was inadequate to protect concrete against magnesium sulfate exposure. Silane/nanocomposites enhanced the performance of concrete under such exposures due to the hydrophobicity of silane, along with the barrier/filling/chemical effects imparted by nanoparticles. Vinyl ester and its nanocomposites exhibited superior performance, which was attributed to the polymeric membrane’s strong bonding and chemical resistance, further enhanced by nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".