Upgrading of paper-grade pulps to dissolving pulps by nitren extraction: Optimisation of extraction parameters and application to different pulps
Bibliographic record
Abstract
Abstract Xylans were selectively removed from paper-grade pulps by nitren extraction to produce dissolving pulps. Extraction parameters were optimised for a birch kraft pulp regarding time, temperature, liquor/pulp ratio, and total nitren charge. Furthermore, the applicability of the method was investigated for two other kraft pulps obtained from eucalyptus and mixed softwood, and for one beech sulfite pulp. Extracted pulps were characterised regarding their carbohydrate content and Cuen viscosity. The nitren charge was a decisive factor for xylan removal and pulp purity. The combination of a high nitren concentration and low liquor/pulp ratio was most effective for xylan removal. However, a high liquor/pulp ratio with a lower nitren concentration proved to be more selective and minimised cellulose degradation as well. Glucomannans were almost insoluble under the extraction conditions investigated. Therefore, softwood pulps were not suitable for the upgrading of chemical pulps to dissolving pulps by nitren extraction. On the other hand, hardwood pulps obtained by kraft and sulfite processes contained 96–97% cellulose after nitren extraction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".