Isolation and characterization of cellulose nanofibers from aspen wood using derivatizing and non-derivatizing pretreatments
Bibliographic record
Abstract
Abstract The link between wood and corresponding cellulose nanofiber (CNF) behavior is complex owing the multiple chemical pretreatments required for successful preparation. In this study we apply a few pretreatments on aspen wood and compare the final CNF behavior in order to rationalize quantitative studies of CNFs derived from aspen wood with variable properties. This is relevant for efforts to improve the properties of woody biomass through tree breeding. Three different types of pretreatments were applied prior to disintegration (microfluidizer) after a mild pulping step; derivatizing TEMPO-oxidation, carboxymethylation and non-derivatizing soaking in deep-eutectic solvents. TEMPO-oxidation was also performed directly on the plain wood powder without pulping. Obtained CNFs (44–55% yield) had hemicellulose content between 8 and 26 wt% and were characterized primarily by fine (height ≈ 2 nm) and coarser (2 nm < height < 100 nm) grade CNFs from the derivatizing and non-derivatizing treatments, respectively. Nanopapers from non-derivatized CNFs had higher thermal stability (280 °C) compared to carboxymethylated (260 °C) and TEMPO-oxidized (220 °C). Stiffness of nanopapers made from non-derivatized treatments was higher whilst having less tensile strength and elongation-at-break than those made from derivatized CNFs. The direct TEMPO-oxidized CNFs and nanopapers were furthermore morphologically and mechanically indistinguishable from those that also underwent a pulping step. The results show that utilizing both derivatizing and non-derivatizing pretreatments can facilitate studies of the relationship between wood properties and final CNF behavior. This can be valuable when studying engineered trees for the purpose of decreasing resource consumption when isolation cellulose nanomaterials. Graphic 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 | 0.000 | 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) | 0.001 | 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".