Effect of moisture content on the maleic anhydride mediated disintegration of Kraft pulp into cellulose nanofibrils
Bibliographic record
Abstract
• The 25 wt% moisture content ensures NBSK’s efficient modification and disintegration. • Produced CNF 25% demonstrated 79.2 ± 2.2 % yield and 0.63 ± 0.05 mmol/g charge density. • Produced CNFs can be assembled to be film and 3D-printing product. A catalyst-free esterification method mediated by maleic anhydride (MA) has been proposed for CNFs isolation from bleached sulfite softwood pulp, where maintaining a specific initial moisture content in pulp is crucial. The presence of moisture allows partial hydrolysis of maleic anhydride under heat conditions to produce maleic acid, which provides an acidic environment for esterification and deconstruction of the pulp and facilitates the subsequent defibrillation. However, the profound significance of moisture contents has not been thoroughly investigated. Herein, northern bleached softwood Kraft pulp (NBSK) with varying moisture contents (0, 12, 25, and 50 wt%) were chosen as the raw materials for MA-mediated esterification. To demonstrate the pivotal function of initial moisture content in NBSK for esterification and defibrillation, the chemical composition, chemical structure, charge content, water retention value, crystal structure, and morphology of esterified samples were compared. The results indicate that moisture in NBSK acts as a catalyst, enhancing the generation of maleic acid, which in turn promotes the esterification and subsequent disintegration of the pulp fibers. On this basis, the characteristics (morphology, zeta potential, charge content, etc.) of CNFs prepared by microfluidization were compared and discussed. Moreover, the prepared CNFs exhibited excellent processability, serving as building blocks for film assembly and 3D-printing product. This work contributes new insights into the pivotal role of moisture contents in the MA-mediated esterification of NBSK.
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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 | 0.002 |
| 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".