Fine-tuning the microstructure of concrete composite: Impact of surface-coated nanocellulose on the increase of strength and decrease of strength variation in concrete
Bibliographic record
Abstract
Nanocellulose is recognized for its ability to enhance the cement hydration process by promoting the growth of hydration products and acting as a conduit for water molecules. However, its tendency to agglomerate due to interfibrillar hydrogen bonding limits its dispersibility in cementitious mixes. This study investigates the surface modification of cellulose nanofibrils (CNFs) with polyethylene glycol (PEG) chains to enhance dispersion and minimize agglomeration, enabling uniform incorporation into a concrete matrix using conventional mixing methods without the need for specialized dispersive techniques. Dynamic Light Scattering revealed that coating CNFs with 2 wt% PEG reduced the hydrodynamic radius by 27 %, resulting in a more uniform size distribution. The incorporation of 0.1 wt% PEG-modified CNFs in concrete led to notable improvements in compressive and tensile strengths by 37 % and 72 %, respectively, while also yielding highly consistent mechanical properties, as reflected by coefficients of variation of 0.7 % for compressive strength and 1.4 % for split tensile strength. Thermogravimetric and structural investigation of the cement specimens across various aging days revealed that the hygroscopic nature of PEG-modified CNFs, initially, delayed the hydration process but promoted the nucleation and growth of hydration products such as C-S-H and Ca(OH) 2 at later aging stages. Furthermore, micro-computed tomography scans showed a substantial reduction in pore volume and size, contributing to a denser, more homogeneous concrete microstructure.
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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) | 0.000 | 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) | 0.001 | 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".