Production and Application of Lignosulfonates and Sulfonated Lignin
Bibliographic record
Abstract
Lignin is the largest reservoir of aromatic compounds on earth and has great potential to be used in many industrial applications. Alternative methods to produce lignosulfonates from spent sulfite pulping liquors and kraft lignin from black liquor of kraft pulping process are critically reviewed herein. Furthermore, options to increase the sulfonate contents of lignin-based products are outlined and the industrial attractiveness of them is evaluated. This evaluation includes sulfonation and sulfomethylation of lignin. To increase the sulfomethylation efficiency of lignin, various scenarios, including hydrolysis, oxidation, and hydroxymethylation, were compared. The application of sulfonated lignin-based products is assessed and the impact of the properties of these products on the characteristics of their end-use application is critically evaluated. Sulfonated lignin-based products have been used as dispersants in cement admixtures and dye solutions more than other applications, and their molecular weight and degree of sulfonation were crucial in determining their efficiency. The use of lignin-based sulfonated products in composites may result in an increase in the hydrophilicity of some composites, but the sulfonated products may need to be desulfonated with an alkali and/or oxygen prior to their use in composites. To be used as a flocculant, sulfonated lignin-based products may need to be cross-linked to increase their molecular weight. The challenges associated with the use of lignin-based products in these applications are comprehensively discussed herein.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".