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Record W2002072789 · doi:10.1021/ac000341v

Detection of Nine Chlorinated and Brominated Haloacetic Acids at Part-per-Trillion Levels Using ESI-FAIMS-MS

2000· article· en· W2002072789 on OpenAlexaff
Barbara Ells, David A. Barnett, Randy W. Purves, Roger Guevremont

Bibliographic record

VenueAnalytical Chemistry · 2000
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsNational Research Council CanadaUniversity of Alberta
FundersAmerican Water Works Association Research FoundationWater Research FoundationU.S. Environmental Protection Agency
KeywordsChemistryHaloacetic acidsIon-mobility spectrometryChromatographyElectrospray ionizationMass spectrometryDetection limitNitrogenAmmoniaOrganic chemistryChlorine

Abstract

fetched live from OpenAlex

A combination of electrospray ionization, high-field asymmetric waveform ion mobility spectrometry, and mass spectrometry (ESI-FAIMS-MS) was used for the analysis of a solution containing a mixture of the nine chlorinated and brominated haloacetic acids. For a carrier gas of nitrogen in the FAIMS analyzer, haloacetate anions of the mono- and dihalogenated acids and the decarboxylated anions of three of the trihalogenated acids were detected. No signal was observed for bromodichloroacetic acid (BDCAA) at a dispersion voltage of -3400 V. The addition of a small amount of carbon dioxide to the nitrogen carrier gas resulted in the detection of the pseudomolecular trihaloacetate anions, including BDCA-, and significant increases in sensitivities for the trihalogenated species. The addition of carbon dioxide to the nitrogen carrier gas had little effect on the mono- and dihalogenated anions. Quantitative analysis of the nine haloacetic acids, using flow injection, gave detection limits between 5 and 36 parts-per-trillion in 9/1 methanol/water (v/v) containing 0.2 mM ammonium acetate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.264
Teacher spread0.243 · 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 designBench or experimental
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

Citations102
Published2000
Admission routes1
Has abstractyes

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