Preparation and Characterization of Lignin-Containing Cellulose Nanofibril from Poplar High-Yield Pulp via TEMPO-Mediated Oxidation and Homogenization
Bibliographic record
Abstract
In this study, a poplar high-yield pulp [preconditioning refiner alkaline peroxide mechanical pulp (P-RC APMP)] was used to produce lignin-containing cellulose nanofibril (LCCNF) dispersions through a sequential process of 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation followed by high pressure homogenization. To produce LCCNF with different lignin contents, sodium hypochlorite loadings of 4–12 mmol/g fiber during TEMPO-mediated oxidation step were explored. The effect of lignin content on morphology, thermal stability, crystallinity, and rheological properties of the produced LCCNFs was investigated. The results showed that the TEMPO-mediated oxidation of cellulose was largely limited to the fiber surface. The residual lignin on the surface of LCCNF was presented as small particles. The increase of lignin content increased the thermal stability and decreased the viscosity of the LCCNF. Moreover, at higher lignin content, greater flocculation and aggregation of fibrils took place, which resulted in lower gel-like characteristics of the resultant LCCNF. The results of water contact angle determination also demonstrated that the increase of lignin content significantly increased the hydrophobicity of the LCCNF.
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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) | 0.000 | 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 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".