Preparation and benchmarking of novel cellulose nanopaper
Bibliographic record
Abstract
Abstract Synthetic polymers and plastics which are currently used as barrier materials in packaging applications are neither renewable nor biodegradable. Nanopaper, which is obtained by breaking down cellulose fibers into nanoscale particles, have unique properties with the potential to replace synthetic packaging materials, but requires very high energy to mechanically process the fibers into nanopaper. This research investigates whether refining alone can be used to produce nanopaper with sufficient quality for packaging applications. Nanopaper was produced from Bleached Eucalyptus Kraft (BEK) refined with a PFI mill and from Northern Bleached Softwood Kraft (NBSK) refined in a pilot disc refiner. Both trials found a plateau for oxygen permeability and water vapour permeability that was reached after 1800 kWh/t and 12,000 kWh/t for refining in the pilot disc refiner and PFI mill, respectively. Refining beyond these optima produced either little or no reduction in permeability, while increasing the drainage time to form a sheet. However, elastic modulus, strain at break and sheet light transmittance did continue to increase. The plateau oxygen permeability of ~ 1.24 (cc µm)/(m2 day kPa) is 1–3 orders of magnitude lower than the oxygen permeability for PET and LDPE, respectively, while the plateau water vapour permeability ~ 3 × 10–11 g/m.s. Pa was 1–2 orders of magnitude higher than for PET and LDPE. The improved strength and barrier properties of nanopaper achieved at lab and pilot scale mechanical refining process promises a sustainable alternative to conventional packaging. Graphical abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".