On the Roles of Cellulose Nanocrystals in Fiber Cement: Implications for Rheology, Hydration Kinetics, and Mechanical Properties
Bibliographic record
Abstract
Fiber cement reinforced with pulp fibers is one of the key drivers for the decarbonization of nonstructural building materials, where the inclusion of sustainable pulp fibers at high proportions (i.e., > 8 wt %) renders poor workability of fiber-cement slurry with a concomitant loss in mechanical strength. Petrochemical-derived superplasticizers, i.e., polycarboxylates (PCEs), are predominantly used in fiber cement (including cement mortars) because they dramatically improve (content <0.5 wt %) the slurry rheology but reduce the rate of hydration and weaken the strength of the cured composite. Thus, it is crucial to explore renewable and bio-based superplasticizers devoid of any negative traits (if possible) of the conventional PCEs. In this study, we examined wood-derived cellulose nanocrystals (CNCs) as a multifunctional additive in fiber cement (bleached pulp fiber content: 8 wt %). In fiber cement, variation of the content (0.02-4 wt %) of CNCs resulted in improvement in the shear thinning behavior of the fiber-cement slurry and thereafter increased the hydration kinetics at high CNC contents (2-4 wt %). Notably, the flexural strength of the composite also exhibited improvement upon the addition of CNCs; the maximum strength was observed at 4 wt % of CNCs. Overall, the beneficial roles of CNCs afforded >10 wt % (in-total) bio-based content in fiber cement without compromising the mechanical strength and curing time (compared to PCEs); hence, the findings of this study could unravel new avenues in interface engineering of cement composites leveraging the multifunctional features of biomaterials, thus enhancing sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".