Dynamic postpolymerization of 3D‐printed photopolymer nanocomposites: Effect of cellulose nanocrystal and postcure temperature
Bibliographic record
Abstract
ABSTRACT Cellulose nanocrystal (CNC) reinforced methacrylate (MA) resin nanocomposite was prepared by 3D stereolithography printing. A postcure process, where the printed nanocomposite was heat‐treated under different temperatures, was applied to improve the property of the printed nanocomposites. To investigate the effect of CNC and postcure temperature on the kinetic behavior of the postpolymerization of printed nanocomposites, Fourier‐transform infrared spectroscopy and differential scanning calorimetry measurement of the printed nanocomposites before and after postcure were analyzed. The postpolymerization of MA nanocomposites was promoted at a postcure temperature of 140 °C for the printed 0.5% CNC/MA nanocomposites compared to the printed MA resin. The addition of CNC retarded the polymerization of MA resin during 3D printing, resulting in poorer mechanical properties of the printed nanocomposites compared to the printed MA resin. However, after postcure, the mechanical properties of the printed nanocomposites were improved by the postpolymerization of the MA nanocomposites. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018, 56, 935–946
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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 | 0.001 |
| 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".