Nanocellulose-based systems for the removal of perfluoroalkyl compounds from water
Bibliographic record
Abstract
The contamination of drinking water sources with poly- and perfluoroalkyl substances (PFAS) is a cause for concern. These compounds have been linked to several health problems in humans, and thus there is an urgent need to develop sustainable removal technologies for PFAS remediation. Cellulose nanocrystals (CNCs) are derived from plants and offer a sustainable, green approach for the development of adsorbents due to their ease of modification. The modification of CNCs with glycidyltrimethylammonium chloride (GTMAC) and the lignin-based flocculant, Tanfloc, was used to prepare positively charged CNCs, while beta cyclodextrin (β-CD) was grafted onto CNCs to promote hydrophobic interactions between PFAS and β-CD. TF-CNC was incorporated into sodium alginate hydrogel beads for ease of adsorption and removal. \nTreatment of an anionic dye, methyl orange (MO), with the adsorbents resulted in a maximum adsorption capacity of 125.7 mg/g for β-CD-CNC, and up to 80% removal of 1000 ppm MO by GTMAC-CNC. TFSH-CNC and TFSG-CNC resulted in qmax values of 917.8 mg/g and 247.5 mg/g, respectively, while the qmax values of ALG beads impregnated with TF-CNC increased from 2.2 mg/g to 4.9 mg/g. Treatment of KPFBS with the adsorbents resulted in removal percentages ranging from 26.1 to 56.5%, indicating that chemical and physical modifications improve the adsorption capacity of CNCs for PFAS. Together, these results indicate that modified CNCs offer a promising template for the development of adsorbents for PFAS remediation from water sources.
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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 | 0.001 | 0.000 |
| 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".