Locust bean gum adsorption onto softwood kraft pulp fibres: isotherms, kinetics and paper strength
Bibliographic record
Abstract
Abstract The adsorption of locust bean gum (LBG) onto Northern Bleached Softwood Kraft (NBSK) pulp improved paper tensile and burst strength and lowered refining energy by strengthening inter-fibre bonding. Adsorption kinetics and isotherms were investigated to develop a fundamental understanding of the adsorption mechanism. The adsorption rate followed pseudo-second-order kinetics and the activation energy was 99.34 kJ·mol−1, suggesting chemisorption. The adsorption rate constant increased rapidly with temperature from 25 to 45 °C (k = 1.93 to 24.03 g·mg−1·min−1), but the amount adsorbed at equilibrium decreased (qe = 1.91 to 0.48 mg·g−1 o.d. fibre). LBG adsorption to NBSK at 25 °C was consistent with the Langmuir adsorption model for LBG < 2.1 wt% of o.d. fibre, suggesting reversible, homogenous adsorption to a finite number of sites on the fibre surface. Refining to 3000 rev increased the heterogeneity of the NBSK pulp surface leading to multi-layer Freundlich adsorption with adsorption constant n = 5.00, and the equilibrium constant Kf = 2.57 mg·g−1·(mg·L−1)−1/n at 25 °C. Favorable adsorption conditions for negatively charged LBG were identified: 25 °C for 10 min, low dosage level (< 2 wt%), lightly refined (< 3000 rev) NBSK pulp at low fibre consistency (< 0.5 wt%), high agitation rate (> 150 r.p.m.), acidic or neutral conditions (pH 2–7) without salt addition. Graphic abstract
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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 | 0.000 |
| 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".