Effect of chain transfer agents in polycarboxylate superplasticizer on slump‐retention of concrete
Bibliographic record
Abstract
Abstract Two types of slump‐retention polycarboxylate superplasticizers (PC‐M and PC‐S) were synthesized by using 3‐mercaptopropionic acid or sodium hypophosphite as chain transfer agents, the effects of polycarboxylate superplasticizers on slump‐retention of cement and concrete were investigated, and the mechanism of action and the structure of polycarboxylate superplasticizers were investigated by using conductivity, complexing Ca 2+ concentration, gel permeation chromatography (GPC), nuclear magnetic resonance hydrogen spectroscopy ( 1 H‐NMR), and nuclear magnetic resonance phosphorus spectroscopy ( 31 P‐NMR). The results indicated that the slump flow of cement paste mixed with PC‐M was increased quickly within 60 min and then lost gradually, while PC‐S increased slowly and evenly within 240 min. The slump flow of concrete mixed with PC‐S reached 405 mm after 150 min, but the concrete mixed with PC‐M was almost no fluidity after 150 min. The conductivity of the PC‐M aqueous solution decreased quickly within 45 min, then slowly decreased, while the conductivity of the PC‐S aqueous solution decreased slowly within 180 min. PC‐M had a much greater complexation ability of Ca 2+ than PC‐S. The composition and structure of the two slump‐retention polycarboxylate superplasticizers were altered by different chain transfer agents, and the hydrolysis‐adsorption capacity of each component was different. The rate of hydrolyzation‐adsorption of each component was PC‐M 3 ≈ PC‐S 3 >PC‐M 1 >PC‐M 2 ≈ PC‐S 1 >PC‐S 2 . The faster the adsorption rate, the faster the slump flow loss of concrete.
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 | 0.001 |
| 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".