Integrating kinetic modeling and experimental insights: PFAS electrochemical degradation in concentrated streams with a focus on organic and inorganic effects
Bibliographic record
Abstract
This study investigated the impact of organic and inorganic constituents on electrochemical degradation of per- and poly-fluoroalkyl substances (PFAS) in a sulfate-based brine from regeneration of spent ion exchange (IX) resin. The system's performance was assessed in the presence of natural organic matter (NOM) and common inorganic constituents: chloride, nitrate, and bicarbonate. Results revealed distinct outcomes based on constituent type, concentration, and specific PFAS variant. NOM hindered PFAS decomposition, especially for more hydrophobic compounds. Chloride reduced degradation and defluorination efficiencies through competitive interactions with PFAS for the anode’s active sites and scavenging effects on SO 4 •− and • OH. Nitrate and bicarbonate minimally impacted degradation but significantly reduced defluorination. Investigating the electrochemical process in real brine solutions showed higher efficiency and lower electrical energy consumption when methanol was distilled, as methanol scavenges reactive radicals and competes for active anode sites. A kinetic model was also developed to determine the direct electron transfer (DET) and mass transfer coefficients for the species present, considering both surface and bulk solution interactions. The model predicted mass transfer (mol m −2 s −1 ) and DET (m 2 mol −1 s −1 ) coefficients of 6:2 FTCA, PFOA, GenX, and PFBA to be (5.0 ×10 −10 , 3.7 ×10 11 ), (1.0 ×10 −9 , 8.0 ×10 8 ), (6.0 ×10 −8 , 7.5 ×10 8 ), and (6.2 ×10 −8 , 4.2 ×10 8 ), respectively. • NOM hindered PFAS decomposition, especially for more hydrophobic compounds. • Cl⁻ reduced degradation by competing for anode sites and scavenging radicals. • NO 3 ⁻ and HCO 3 ⁻ reduced defluorination but minimally impacted degradation. • Distillation improved PFAS decay and energy efficiency in treating IX brine solution. • We developed a kinetic model to predict mass transfer and DET coefficients of PFAS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".