Clear transparent cellulose nanopaper prepared from a concentrated dispersion by high-humidity drying
Bibliographic record
Abstract
Optically transparent cellulose nanopaper is a promising candidate for flexible device substrates because of its light weight, surface smoothness, and high dimensional stability with respect to temperature. Conventionally, clear transparent nanopaper has been fabricated from cellulose nanofiber dispersions with quite low concentration: less than 0.5 wt%. However, this diluteness leads to several problems, such as huge energy consumption and long operation time for drying. Therefore, nanopaper should be fabricated from a concentrated dispersion to mitigate these problems. In this study, transparent nanopaper was fabricated from cellulose nanofiber dispersions with various concentrations (0.24-1.81 wt%). Optical experiments revealed that the haze of the transparent nanopaper increased monotonically with cellulose nanofiber dispersion concentration, when the cellulose nanofiber dispersion was prepared from holocellulose pulp and conventional over-drying was applied. Based on our insight into the origin of this increase in the haze of transparent nanopaper, we developed high-humidity drying, which successfully produced clear transparent nanopaper from a concentrated dispersion without prolonged drying time.
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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".