The Influence of CaCl2-Blended Acrylic Polymer on Steel Rebar Corrosion and Acid Attack Resistance of Mortar
Bibliographic record
Abstract
A realistic forecast of the impact of CaCl2-blended acrylic polymer (CP) emulsions on steel reinforcement corrosion is difficult without a detailed study. This is traceable to the conflicting effects of cement additives on the chloride threshold value, pore solution chemistry, and matrix resistivity. Hence, in the study reported herein, the actual influence of 0.5–1.5 wt% CP on rebar corrosion was assessed via an accelerated corrosion test. The macrocell current, half-cell potential measurements (HCP), reinforcement cover crack propagation, and rebar deterioration were monitored. The resistance of mortar specimens to acid-induced degradation was also evaluated over time. The corrosion test results indicate that steel rebar corrosion initiation in the mortar expedited as the CP dosage in mixtures increased. Consequently, the time required for the CP-modified specimens to crack shortened. Moreover, non-uniform rebar section loss and surface crack width widening were also observed in CP-modified mortar specimens. However, X-ray diffraction (XRD) analyses of the plain Ref and 1.5% CP corrosion byproduct residues indicated that key phases such as akaganeite, goethite, lepidocrocite, hematite, and magnetite were quantitatively similar. Interestingly, the addition of 1.0–1.5% CP to mixtures enhanced the acid attack resistance of mortars. For now, these results indicate that CP should only be used as a chemical admixture in unreinforced cement composites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".