Effect of hornification on the isolation of anionic cellulose nanofibrils from Kraft pulp via maleic anhydride esterification
Bibliographic record
Abstract
Cellulose nanofibrils (CNF) isolation based on a catalyst-free maleic anhydride esterification has proven to be effective, however, the effects of pulp hornification on CNF isolation by this strategy have yet to be explored, which could present significant impacts for CNF isolation. Herein, dried northern bleached softwood Kraft pulp (D-NBSK) and never-dried northern bleached softwood Kraft pulp (ND-NBSK) were selected as the substrates. After esterification with maleic anhydride (MA), the esterified ND-NBSK pulp (E-ND) shows a significantly smaller size and more fragmented structure than the esterified D-NBSK pulp (E-D). Meanwhile, higher degree of esterification can be realized for the never dried pulp as compared to the dried pulp, which is corroborated by the significantly stronger characteristic peaks of CO (1720 cm−1) and -COO− (1575 cm−1) from the FTIR spectra and the higher surface charge content (0.86 ± 0.04 mmol/g vs. 0.55 ± 0.05 mmol/g). A comparison of the characteristics of the resulting CNF similarly demonstrated the negative impact of hornification. Overall, this work indicates that hornification tends to reduce the accessibility of chemical reagents to the pulp, leading to insufficient deconstruction. Such negative impact of hornification should be considered when performing nanocellulose isolation, especially when using pulp as feedstock.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".