How Do Novel PFAS Sorbents Fit into Current Engineering Paradigm?
Bibliographic record
Abstract
As the emergence of novel sorbents brings new possibilities for treatment of per- and polyfluoroalkyl substances (PFAS), drinking water and wastewater utilities face critical decisions in selecting effective, future-ready technologies. With regulatory pressures to address PFAS contamination mounting, however, many utilities may not be in a position to fully evaluate the potential of these novel sorbents and are instead preparing to adopt established technologies that are currently available, such as granular activated carbon (GAC) and ion exchange (IX) resins. Given the expected long life spans of any chosen system, it is important to consider all options, including future treatment innovations. This perspective provides insights into their potential advantages and challenges by exploring the current state of novel sorbents within the broader context of existing technologies. Novel sorbents bring promising benefits, including enhanced selectivity, rapid kinetics, and flexibility for different PFAS chemistries, particularly in challenging matrices such as wastewater. Despite their advantages, significant work remains to refine these materials for large-scale application, including addressing scalability, cost-effectiveness, fouling resistance, and regulatory certification hurdles. By examining key factors for both utilities and novel sorbent developers, this perspective aims to guide informed decisions that balance immediate regulatory compliance with long-term adaptability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".