Programmatic Data Analysis for Quantitative Isothermal Heat Flow Calorimetry of Cementitious Materials
Bibliographic record
Abstract
Isothermal heat flow calorimetry is a powerful method for studying chemical processes. In cement research, it has become indispensable for quantifying the heat release during cement hydration. It is used to study the reactivity of cementitious binders and the effect of admixture chemistry and dosage. Most isothermal calorimetry data on cementitious materials is analyzed qualitatively, i.e., by graphical comparison of heat flow curves. This is a missed opportunity, as the method delivers precise quantitative data with clearly defined chemical meaning. This work presents a lightweight open-source toolchain for quantitative analysis of isothermal calorimetry data. Using this toolchain, we quantify the effect of three retarders, sucrose, etidronic acid, and racemic tartaric acid, on the hydration of Portland cement. In particular, we determine characteristic times and kinetic parameters such as the maximum heat flow, the duration of the dormant period, or the maximum acceleration and the corresponding time. The results reveal that the efficiency of the retarders ranks in the order sucrose > etidronic acid > DL-tartaric acid. Further, we find that the end of the dormant period of cement hydration is exponentially dependent on the dosage of etidronic acid. In contrast, sucrose and tartaric acid show a small deviation from an exponential relationship which indicates differences in the retardation mechanism. This is also reflected in the acceleration of the main silicate reaction. Increasing amounts of etidronic acid lead to a significantly more substantial reduction of the C3S hydration acceleration than sucrose. Additionally, the Vicat set times of the cement pastes were determined, and an excellent correlation was found with the time of the maximum acceleration of the silicate reaction.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".