Cellulose-based dispersants and flocculants
Bibliographic record
Abstract
Natural dispersants and flocculants, often referred to as dispersion stabilizers and liquid-solid separators, respectively, have secured a promising role in the bioprocessing community. They have various applications, including in biomedicine and in environmental remediation. A large fraction of existing dispersants and flocculants are synthesized from non-safe chemical compounds such as polyacrylamide and surfactants. Despite numerous advantages of synthetic dispersants and flocculants, issues such as renewability, sustainability, biocompatibility, and cost efficiency have shifted attention towards natural homologues, in particular, cellulose-based ones. Within the past decade, cellulose derivatives, obtained via chemical and mechanical treatments of cellulose fibrils, have successfully been used for these purposes. In this review article, by dividing the functional cellulosic compounds into "polymeric" and "nanoscale" categories, we provide insight into the engineering pathways, the structural frameworks, and surface chemistry of these "green" types of dispersants and flocculants. A summary of their efficiency and the controlling parameters is also accompanied by recent advances in their applications in each section. We are confident that the emergence of cellulose-based dispersing and flocculating agents will extend the boundaries of sustainable green technology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".