UV-VIS SPECTRA OF LIGNIN MODEL COMPOUNDS IN THE PRESENCE OF METAL IONS AND CHELANTS
Bibliographic record
Abstract
The presence of transition metal ions, such as iron, in lignin rich pulp fibers has negative consequences on pulp brightness and brightness stability. Earlier studies showed that impregnation of lignin-rich pulps with chelants, such as DTPA and EDTA, is very effective in recovering the brightness loss and reducing both the photo-yellowing and thermal yellowing of mechanical pulps. In this study, the fundamentals associated with such a process are further studied with lignin model compounds in combination with UV-Vis spectroscopicy. Four lignin model compounds, guaiacol, veratrole, vanillin and creosol, were tested. It was found that transition metal ions and lignin model compounds by themselves did not exhibit significant absorbance in the visible light range. However, when the two components were combined, a significant visible absorbance was observed. This was explained by the formation of coloured complexes between the transition metal ion and lignin model compounds, and transition metal ioniinduced coupling reactions. Among the transition metal ion species studied Fe(III), Fe(II), Cu(II), Mn(II) and Al(III),Fe(III) showed the strongest effect. In most cases, addition of a chelant to solutions containing transition metal ions and a lignin model compound reduced absorbance in the visible light range and blocked the coupling reactions. Furthermore, the EDTA-and DTPA-metal ion complexes exhibited ultraviolet shielding properties, which could be partly responsible for reduced photo-yellowing, when lignin-rich pulps are chelated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".