Functionalized biochar for the removal of poly- and perfluoroalkyl substances in aqueous media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biochar has gained attention as a promising adsorbent for removing various environmental pollutants due to its availability, cost-effectiveness, eco-friendly nature, and high adsorption capacity. This review focuses on using biochar to remove poly- and perfluoroalkyl substances (PFAS), emerging contaminants that pose significant environmental and health risks due to their toxicity, persistence, and bioaccumulation potential. The classification of biochar and using pristine and functionalized biochar for pollutant removal are addressed, along with an overview of the various functionalization techniques employed to enhance biochar's adsorption capacity. Different factors influencing the removal of poly- and perfluoroalkyl substances (PFAS), such as pH, the molecular chain length of PFAS, and biochar characteristics like pyrolysis temperature, particle size, and dosage, are investigated. Long-chain PFAS, such as perfluoro octane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are more effectively adsorbed than short-chain PFAS, with competitive sorption effects observed in mixed-solution environments. A decrease in pH, smaller biochar particle sizes, and optimized pyrolysis temperatures have been found to enhance biochar's sorption capacity. Furthermore, biochar demonstrates higher efficiency in single-solution systems compared to mixed solutions when removing PFAS.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it