Understanding the effect of depth refining on upgrading of dissolving pulp during cellulase treatment
Bibliographic record
Abstract
Reactivity is a critical parameter of dissolving pulp, which determines toxic chemical (i.e. carbon disulfide) consumption in rayon production. In this study, the depth refining was carried out to upgrade pre-hydrolysis kraft (PHK) pulp prior to cellulase treatment. The hypothesis is that the mechanical refining can not only increase reactivity by liberating additional hydroxyl groups, but also enhance cellulase efficiency by improving enzymatic accessibility. Results showed that the Fock reactivity of refined pulp (beating degree of 50°SR) was increased to 78.0 % from 54.8 % of the original (19°SR), which was mainly caused by inter- molecular hydrogen bond changes, supported by FTIR analysis. In addition, the cellulase adsorption ratio of refined pulp (30–50 °SR) was increased in a range of 39.7–71.2 %, which verified the improvement of enzymatic accessibility. As a result, the integrated process consisting of mechanical refining and cellulase treatment (at cellulase dosage of 0.5 mg/g pulp) yielded a much better result than the control (at cellulase dosage of 1 mg/g pulp) in terms of reactivity increase and viscosity decrease. Other pulp properties, such as fiber length and fines content, water retention value (WRV), specific surface area (SSA), crystallinity, and morphology, were all supported the positive effect of depth refining on activation of dissolving pulp during cellulase treatment.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".