The Use of Surface-Modified Nanocrystalline Cellulose Integrated Membranes to Remove Drugs from Waste Water and as Polymers to Clean Oil Sands Tailings Ponds
Bibliographic record
Abstract
There is an urgent environmental need to remediate waste water. In this study, the use of surface-modified nanocrystalline cellulose (CNC) to remove polluting drugs or chemicals from waste water and oil sands tailing ponds has been investigated. CNC was modified by either surface adsorbing cationic or hydrophobic species or by covalent methods and integrated into membrane water filters. The removal of either diclofenac or estradiol from water was studied. Similar non-covalently modified CNC materials were used to flocculate clays from water or to bind naphthenic acids which are contaminants in tailing ponds. Estradiol bound well to hydrophobically modified CNC membrane filter systems. Similarly, diclofenac (anionic drug) bound well to covalently cationically modified CNC membranes. Non-covalent modified CNC effectively flocculated clay particles in water and bound two naphthenic acid chemicals (negatively charged and hydrophobic). Modified CNC integrated into water filter membranes may remove drugs from waste or drinking water and contaminants from tailing ponds water. Furthermore, the ability of modified CNC to flocculate clays particles and bind naphthenic acids may allow for the addition of modified CNC directly to tailing ponds to remove both contaminants. CNC offers an environmentally friendly, easily transportable and disposable novel material for water remediation purposes.
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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".