Treatment of aqueous per- and poly-fluoroalkyl substances: A review of biochar adsorbent preparation methods
Bibliographic record
Abstract
Per- and poly-fluoroalkyl substances (PFAS) are synthetic chemicals widely used in everyday products, causing elevated concentrations in drinking water and posing a global challenge. While adsorption methods are commonly employed for PFAS removal, the substantial cost and environmental footprint of commercial adsorbents highlight the need for more cost-effective alternatives. Additionally, existing adsorbents exhibit limited effectiveness, particularly against diverse PFAS types, such as short-chain PFAS, necessitating modifications to enhance adsorption capacity. Biochar can be considered a cost-effective and eco-friendly alternative to conventional adsorbents. With abundant feedstocks and favorable physicochemical properties, biochar shows significant potential to be applied as an adsorbent for removing contaminants from water. Despite its effectiveness in adsorbing different inorganic and organic contaminants from water environments, some factors restrict its effective application for PFAS adsorption. These factors are related to the biochar properties, and characteristics of PFAS, as well as water chemistry. Therefore, some modifications have been introduced to overcome these limitations and improve biochar's adsorption capacity. This review explores the preparation conditions, including the pyrolysis process, activation, and modification techniques applied to biochar to enhance its adsorption capacity for different types of PFAS. It addresses critical questions about the adsorption performance of biochar and its composites, mechanisms governing PFAS adsorption, challenges, and future perspectives in this field. The surge in research on biochar for PFAS adsorption indicates a growing interest, making this timely review a valuable resource for future research and an in-depth exploration of biochar's potential in PFAS remediation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 teacher head, 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".