Spectroscopic evidence of silica-lignin complexes: implications for treatment of non-wood pulp wastewater
Bibliographic record
Abstract
This research examined the hypothesis that lignin compounds form aqueous complexes with silica increasing its solubility, thereby inhibiting its precipitation. An experimental program using four lignin model compounds was conducted to test the hypothesis. Laser Raman spectroscopy (LRS) was used to characterize, qualitatively, the interaction between lignin and aqueous silica, and to identify the possibility of silica-lignin complexation. Solubility studies were then performed by analyzing the solubility of silica in presence and absence of lignin within the relevant pH range to confirm the results of LRS, and to obtain a quantitative assessment of the relative solubility. The findings have established the formation of silica-ferulic, silica-vanillic, and silica-4-methoxycinnamic acid complexes, but no evidence was detected for the formation of silica-veratryl alcohol complex. In fact, the black liquor undoubtedly contains much more complex lignin materials than the simple model compounds used in this work. The more complex lignin compounds are likely to have an even greater tendency to form silica complexes, thus contributing to the initial hypothesis. This finding provides a fundamental understanding as to why previous efforts to precipitate silica by lowering the pH from 10-11 (for black liquor) to less than 9 did not achieve satisfactory silica separation, and why alternative strategies need to be investigated.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".