Constructing pristine and modified cellulose nanocrystals based cured polychloroprene nanocomposite films for dipped goods application
Bibliographic record
Abstract
In this work, polychloroprene rubber (CR) nanocomposite films reinforced with native and modified cellulose nanocrystals (CNCs) were evaluated for dipped goods applications. The CNC modification, with a goal of enhancing the interaction between the CNC and CR, was conducted by surface graft polymerization of lactic acid. The films were then prepared by latex blending, casting, and curing (with ZnO/MgO). TEM studies displayed that the CNCs formed a partially structured network while modified CNCs (mCNCs) tend to disperse mostly individually in the polychloroprene and show percolation at 3 wt%. Tensile tests of the films showed a substantial increase in the modulus, tensile strength, and tear resistance for both CNC, and mCNC reinforced films while the elongation at break remained above 600%. The films made with CNCs and mCNCs exhibited similar acetone vapor permeability at different loadings of the filler. However, their permeability towards water and 2-propanol vapor increased steadily with an increase in CNCs loading. In contrast, the mCNC-based films displayed steady permeability and a surge at 3 wt%, which could be attributed to percolation. Overall, the fabrication of CNC and mCNC reinforced CR films demonstrated appealing physical properties for a range of dipped goods applications.
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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".