Comparison of nanocrystalline cellulose and fumed silica in latex coatings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it