Enhancing hydration and internal curing in cementitious mixes: The impact of pre-treated milkweed fibres
Bibliographic record
Abstract
This study investigates the internal curing effects of milkweed fibres (MW) in Portland cement mixes, exploring various dosage levels and pre-treatment methods. The study first assessed the impact of MW fibres on the effective water-to-cement (w/c) ratio through rheological measurements, enabling accurate comparisons of samples with identical effective w/c ratios. Both MW fibre pre-treatments and dosages were found to impact the effective w/c ratio in the mixes, leading to the categorization of MW fibre-incorporated samples into three groups based on their effective w/c ratio. In the subsequent phase, the internal curing effect and hydration characteristics of cementitious mixes were evaluated with varying dosages of MW fibres. Optimal internal curing performance was achieved with 0.1% pre-treated MW fibres compared to 0.2%, resulting in up to a 17% improvement in the degree of hydration compared to the reference sample with the same effective w/c ratio. This showed the adverse effect of higher percentage due to the reduction in the free water availability and higher leaching of extractives, which further intensified the hindrance of the hydration process. Furthermore, microstructure analyses, including TGA/DTG, XRD, and SEM-EDS, of cementitious mixes incorporating MW fibres confirmed that the inclusion of 0.1% hybrid and hydrothermally-treated MW fibres resulted in a higher formation of hydration products. This is achieved by providing an adequate amount of free water for the initial reaction of cement hydration and entrained water in the lumen for the subsequent internal curing. These findings show the effectiveness and suitability of using pre-treated MW fibres for internal curing applications, as validated by the comprehensive analyses conducted in this study.
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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 | 0.001 |
| 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".