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Record W2021801381 · doi:10.1155/2011/949745

Identification of Halohydrins as Potential Disinfection By‐Products in Treated Drinking Water

2011· article· en· W2021801381 on OpenAlexaff
Karl J. Jobst, Vince Y. Taguchi, Richard D. Bowen, Moschoula A. Trikoupis, Johan K. Terlouw

Bibliographic record

VenueInternational Journal of Spectroscopy · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsMinistry of the Environment, Conservation and ParksMcMaster University
Fundersnot available
KeywordsChemistryChlorineBromideSodium thiosulfateGlycidolBromineMass spectrometryQuenching (fluorescence)Mass spectrumNuclear chemistryChromatographyOrganic chemistryFluorescence

Abstract

fetched live from OpenAlex

In 2001, two potential disinfection by‐products (DBPs) were tentatively identified as 1‐aminoxy‐1‐chlorobutan‐2‐ol (DBP‐A) and its bromo analogue (DBP‐B) (Taguchi 2001). Subsequently it became clear, by consulting an updated version of the NIST database, that their mass spectra are close to those of the halohydrins 4‐chloro‐2‐methylbutan‐2‐ol and 3‐bromo‐2‐methylbutan‐2‐ol. To establish the structures of these DBPs, additional mass spectrometric experiments, including Fourier transform ion cyclotron resonance (FTICR), were performed on treated drinking water samples and authentic halohydrin standards. It appears that DBP‐A is 3‐chloro‐2‐methylbutan‐2‐ol and that DBP‐B is its bromo analogue. DBP‐B has been detected in ozonated waters containing bromide. Our study also shows that these DBPs can be laboratory artefacts, generated by the reaction of residual chlorine in the sample with 2‐methyl‐2‐butene, the stabilizer in the CH 2 Cl 2 used for extraction. This was shown by experiments using CH 2 Cl 2 stabilized with deuterium labelled 2‐methyl‐2‐butene. Quenching any residual chlorine in the drinking water sample with sodium thiosulfate minimizes the formation of these artefacts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 teacher head, 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

Citations1
Published2011
Admission routes1
Has abstractyes

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