Comparison of nanocrystalline cellulose and fumed silica in latex coatings
Bibliographic record
Abstract
Exploratory work has been undertaken to compare the performance of nanocrystalline cellulose (NCC) with fumed silica in styrene/acrylic latex coatings. NCC has emerged as a promising candidate for the reinforcement of polymeric materials because of its impressive mechanical properties and renewable nature. However, a better understanding of NCC in nanocomposites, compared to more conventional fumed silica-filled systems, is critical to identify feasible commercial applications for NCC. While the dispersion of nanomaterials in polymer matrices is often a challenge, by working with hydrophilic nanoparticles in a waterborne latex, the authors demonstrate that both NCC and fumed silica were dispersed in the latex coatings (up to 9 wt% loadings). The hardness, elastic modulus, resistance to plastic deformation and impact strength were similar for coatings with both types of nanomaterials at loadings below the percolation threshold of NCC (~3 wt%); however, the mechanical performance of NCC-filled coatings was significantly better at higher loadings. In abrasion and corrosion resistance tests, NCC-filled coatings underperformed relative to unfilled and fumed silica-filled coatings. This is the first report that directly compares NCC-filled polymeric coatings with silica-filled coatings including evaluation using industry standards like nanoindentation and corrosion resistance testing. This article contains supporting information that will be made available online once the issue is published.
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 | 0.000 | 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) | 0.001 | 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".