Synthesis, working mechanism, and effectiveness of a novel corrosion‐inhibiting polycarboxylate superplasticizer for concrete
Bibliographic record
Abstract
ABSTRACT In this work, a novel corrosion‐inhibiting polycarboxylate superplasticizer for concrete was synthesized through two‐step esterification reaction and one‐step polymerization. Firstly, arginine‐polyethylene glycol monoester was synthesized by p‐toluenesulphonic acid‐catalyzed esterification reaction of one hydroxyl of polyethylene glycol and carboxylic of arginine containing the main functional group of corrosion‐inhibiting polycarboxylate superplasticizer. Secondly, under the condition of hydroquinone as a polymerization inhibitor, polycarboxylate macromonomer was also synthesized by p‐toluenesulphonic acid‐catalyzed esterification reaction of another hydroxyl of as‐prepared monoester and carboxylic of methyl acrylic acid (96 % esterification rate). Finally, a corrosion‐inhibiting polycarboxylate superplasticizer for concrete was synthesized by ammonium peroxydisulphat‐initiated polymerization, polycarboxylate macromonomer, methyl acrylic acid, and sodium methyl acrylic acid. The structures of monoester, macromonomer, and superplasticizer were characterized by FTIR. Moreover, the fluidity of cement paste, the water reduction, and the compressive strength ratio of as‐prepared polycarboxylate macromonomer (0.5 %) as a corrosion‐inhibiting superplasticizer were more than 290 mm, 25 %, and 130 % respectively.
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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) | 0.001 | 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".