Impact of Viscosity on Hydration Kinetics and Setting Properties of Cementitious Materials
Bibliographic record
Abstract
Abstract The mixing process used for cement, gypsum, mortar, or concrete has a strong effect on the hydration kinetics and setting properties, so that a standard mixing protocol has to be followed, including the mixing time and the addition sequence of the different components. In order to study the effect of the mixing on the hydration kinetics, rheological and calorimetric measurements have been performed on cementitious materials prepared with different mixing designs. The product viscosity was directly measured with an instrumented mixer from the start of the mixing and during the addition of the different constituents to enable an accurate measurement of the mixing energy. The acceleration of the hydration kinetics is associated with (i) the generation of finer particles that act as nuclei for the precipitation of the hydrates and (ii) an increase of the temperature due to the friction between the grains; therefore, a longer and more intense mix tends to accelerate the hydration kinetics. We show in this work that the retardation of the hydration kinetics of cementitious materials, which appears with the addition of plasticizer, is not only chemical but also physical, as a modification of the rheological properties has an additional effect on the setting properties.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".