Cellulose Nanocrystals‐Based Polyacrylamide as Flocculating Agent of Mature Fine Tailings
Bibliographic record
Abstract
Abstract This study compares the performance of neat polyacrylamide (PAM) and their nanocomposites produced with cellulose nanocrystals (CNCs) on the flocculation of mature fine tailings (MFT). The surface of CNCs is chemically modified using three different bifunctional organosilanes containing vinyl functional groups (7‐octenyl trimethoxysilane, methacryloxypropyl trimethoxysilane, vinyl trimethoxysilane). Acrylamide is polymerized in the absence and presence of pristine and silylated CNCs (5 and 10%w/w) to in situ produce PAM‐CNC nanocomposites. Concentrations of 1000 and 2000 ppm of the produced polymers are used to flocculate MFT. FTIR spectra showed that the CNCs are successfully modified, regardless of the silane used. The dynamics of floc size evolution is assessed by focused beam reflectance measurements (FBRMs), and the results demonstrated that smaller flocs are produced by increasing the CNCs content in the polymerization. Capillary suction times (CSTs) data showed that the use of CNC‐based PAM nanocomposites enhances flocs dewaterability in comparison to neat PAM. Moreover, the type of silane used in the chemical modification of CNCs influenced the flocculation performance of PAM‐CNC nanocomposites. The obtained results are promising and encourage the development of a more detailed investigation regarding the production and performance assessment of hybrid flocculating polymers composed by PAM and CNCs.
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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) | 0.000 | 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 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".