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Record W4405013290 · doi:10.1016/j.jhazmat.2024.136624

Integrating kinetic modeling and experimental insights: PFAS electrochemical degradation in concentrated streams with a focus on organic and inorganic effects

2024· article· en· W4405013290 on OpenAlexafffund
Fatemeh Asadi Zeidabadi, Pezhman Abbasi, Ehsan Banayan Esfahani, Madjid Mohseni

Bibliographic record

VenueJournal of Hazardous Materials · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsChemistryBicarbonateBrineChlorideEnvironmental chemistrySulfateElectrochemistryDegradation (telecommunications)Organic matterDecompositionNitrateInorganic ionsAnodeInorganic chemistryScavengingChemical engineeringIonElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.238
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

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