Evaluation of a Porous Membrane as a Mass-Transfer Efficient Structure for the Adsorption of Per- and Polyfluoroalkyl Substances from Drinking Water
Bibliographic record
Abstract
With drinking water regulations forthcoming for per- and polyfluoroalkyl substances (PFAS), the need for cost-effective treatment technologies has become urgent. Adsorption is a key process for removing or concentrating PFAS from water; however, conventional adsorbents operated in packed beds suffer from mass transfer limitations. The objective of this study was to assess the mass transfer performance of a porous polyamide adsorptive membrane for removing PFAS from drinking water under varying conditions. We conducted batch equilibrium and dynamic adsorption experiments for perfluorooctanesulfonic acid, perfluorooctanoic acid, perfluorobutanesulfonic acid, and undecafluoro-2-methyl-3-oxahexanoic acid (i.e., GenX). We assessed various operating and water quality parameters, including flow rate (pore velocity), pH, ionic strength (IS), and presence of dissolved organic carbon. Outcomes revealed that the porous adsorptive membrane was a mass transfer-efficient platform capable of achieving dynamic capacities similar to equilibrium capacities at fast interstitial velocities. The adsorption mechanism of PFAS to the membrane was a mixture of electrostatic and hydrophobic interactions, with pH and IS controlling which interaction was dominant. The adsorption capacity of the membrane was limited by its surface area, but its site density was approximately five times higher than that of granular activated carbon. With advances in molecular engineering to increase the capacity, porous adsorptive membranes are well suited as alternative adsorbent platforms for removing PFAS from drinking water.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".