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Record W4390675735 · doi:10.1039/d3cp04973f

The role of helicity in PFAS resistance to degradation: DFT simulation of electron capture and defluorination

2024· article· en· W4390675735 on OpenAlexafffund
Matt McTaggart, Cecile Malardier‐Jugroot

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsElectronDegradation (telecommunications)Chemical physicsQuantum chemicalChemistryHelicitySolvated electronAlkylComputational chemistryMaterials sciencePhysicsOrganic chemistryRadicalMoleculeComputer science

Abstract

fetched live from OpenAlex

the direct capture of excess electrons poses a promising path to environmental decontamination. Herein we show that quantum-chemical model optimization methods can be adapted to simulate the changes to molecular geometry that result from electron capture. These reaction pathways demonstrate that the introduction of an additional electron causes a loss of the helical arrangement along linear carbon tail chains. Regaining helicity is sufficiently favourable to enable fluoride release in C7-C10 PFAS chains; shorter chains are enthalpically hindered from degradation while the additional charge is stabilized on longer chains by the greater entropy their flexibility permits. These results suggest that reductive PFAS treatment processes could be made more effective under high pressure or confined conditions.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.268
Teacher spread0.261 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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