Comparison of tension wood and normal wood for oxidative nanofibrillation and network characteristics
Bibliographic record
Abstract
Abstract Cellulose nanofibrils (CNFs) are top-down nanomaterials obtainable from abundant lignocelluloses. Despite recent advances in processing technologies, the effects of variations in the lignocellulose structure and composition on CNF isolation and properties are poorly understood. In this study, we compared the isolation of CNFs from tension wood (TW) and normal wood (NW) from Populus tremula (aspen). The TW has a higher cellulose content, native cellulose fibrils with a larger crystalline diameter, and less lignin than the NW, making it an interesting material for CNF isolation. The wood powders were oxidized directly by 2,2,6,6-tetramethylpiperidin-1-oxyl, and the morphology and mechanical behaviors of the nanofibril suspensions and networks were characterized. The TW was more difficult to fibrillate by both chemical and mechanical means. Larger nanofibrils (5–10 nm) composed of 1.2 nm structures were present in the TW CNFs, whereas the NW samples contained more of thin (1.6 nm) structures, which also comprised 77% of the solid yield compared to the 33% for TW. This difference was reflected in the TW CNF networks as decreased transmittance (15% vs. 50%), higher degree of crystallinity (85.9% vs. 78.0%), doubled toughness (11 MJ/m 3 ) and higher elongation at break (12%) compared to NW. The difference was ascribed to greater preservation of the hierarchical, more crystalline microfibril structure, combined with a more cellulose-rich network (84% vs. 70%). This knowledge of the processing, structure, and properties of CNFs can facilitate the breeding and design of wood feedstocks to meet the increasing demand for nanoscale renewable materials. Graphic abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".