Wood cellulose films with different foldabilities triggered by dissolution and regeneration from concentrated H2SO4 and NaOH/urea aqueous solutions
Bibliographic record
Abstract
Forests are a major source of wealth for Canadians, and cellulose makes up the “skeleton” of wood fibers. Concentrated H2SO4 and NaOH/urea aqueous solutions are two efficient solvents that can rapidly dissolve cellulose. Our preliminary experiment obtained regenerated wood cellulose films with different mechanical properties from these two solvents. Therefore, herein, we aim to investigate the effects of aqueous solvents on the structure and properties of wood cellulose films. Regenerated cellulose (RC) films were produced by dissolving wood cellulose in either 64 wt% H2SO4 solution (RC-H4) or NaOH/urea aqueous solution (RC-N4). RC-H4 showed the higher tensile strength (109.78 ± 2.14 MPa), better folding endurance (20–28 times), and higher torsion angle (42°) than RC-N4 (62.90 ± 2.27 MPa, un-foldable, and 12°). The increased cellulose contents in the H2SO4 solutions from 3 to 5 wt% resulted in an improved tensile strength from 102.61 ± 1.99 to 132.93 ± 5.64 MPa and did not affect the foldability. RC-H4 also exhibited better water vapor barrier property (1.52 ± 0.04 × 10−7 g m−1 h−1 Pa−1), superior transparency (~90 % transmittance at 800 nm), but lower thermal stability compared to RC-N4. This work provides special insights into the regenerated wood cellulose from two aqueous solvents and is expected to facilitate the development of high-performance RC films from abundant forestry resources.
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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) | 0.001 | 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".