Effect of pectin extraction method on properties of cellulose nanofibers isolated from sugar beet pulp
Bibliographic record
Abstract
Abstract In this study, the effect of pectin extraction method on the properties of cellulose nanofibers (CNFs) isolated from sugar beet pulp (SBP) was studied. Pectin was extracted by the industrially practiced method by sulfuric acid hydrolysis or by enzymatic hydrolysis using a cellulase/xylanase enzymes mixture. The CNFs were then isolated by high-pressure homogenization and investigated in terms of their chemical composition, crystallinity, size, degree of polymerization, and re-dispersion in water after freeze-drying. The mechanical properties and surface characteristics of CNF films were also studied. The results showed that fibrillation of the de-pectinated SBP was more efficient for the acid hydrolyzed SBP. CNFs from the acid-hydrolyzed SBP had a slightly wider diameter, higher crystallinity, viscosity, and α-cellulose content but a lower degree of polymerization than CNFs from the enzyme-hydrolyzed SBP. Owing to the presence of more residual hemicelluloses in the CNFs from the enzyme-hydrolyzed SBP, the CNFs had higher re-dispersion ability in water. CNF films from enzyme-hydrolyzed SBP displayed slightly better mechanical properties and higher water contact angle than acid-hydrolyzed CNF films. Graphic abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".