Interactions of Lignin with Optical Brightening Agents and Their Effect on Paper Optical Properties
Bibliographic record
Abstract
Optical brightening agents (OBAs) are widely used in the production of uncoated and coated paper grades to improve their optical properties. The presence of lignin in the pulp furnishes is well-known to have a significant effect on the OBA brightening efficiency, but how OBA interacts with lignin is still not well understood. In this study we used wood lignin to investigate the lignin/OBA interactions and its effect on OBA brightening. Three lignin samples isolated from spruce, pine, and aspen were used. Both di- and tetra-sulfonated OBAs were studied. It was found that the OBA addition can effectively improve the optical properties of paper, such as ISO brightness, CIE whiteness, and b *, but disulfonated OBA was found to be more effective at a lower dosage (less than 0.6%) than the tetra-sulfonated OBA. The addition of a small amount of lignin (0.4%) onto filter paper had negative effects on the optical properties, but the impact depends strongly on lignin structures (lignin samples from spruce, pine, and aspen), which explain the early results that mechanical pulps from different wood species respond very differently to OBA brightening. A modified Kubelka–Munk equation was used to predict and model the brightness and whiteness response of different lignin types and OBA, which can be used to provide guidance in determining the amount of OBA needed to reach specified optical property target.
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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 | 0.000 | 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) | 0.000 | 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".