MétaCan
Menu
← Back to cohort
Record W2103590323 · doi:10.1021/es505743d

Detection, Identification, and Quantification of Hydroxylated Bis(2-ethylhexyl)-Tetrabromophthalate Isomers in House Dust

2015· article· en· W2103590323 on OpenAlexafffundabout
Hui Peng, David Saunders, Jianxian Sun, Garry Codling, Steve Wiseman, Paul D. Jones, John P. Giesy

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Saskatchewan
FundersState Administration of Foreign Experts AffairsChinese Academy of SciencesWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMass spectrometryChemistryDetection limitMass spectrumHigh resolutionResolution (logic)Chromatography

Abstract

fetched live from OpenAlex

Ultra-High Resolution LC/mass spectrometry (LC-UHRMS; Thermo Fisher Q-Exactive) was used to identify two novel isomers of hydroxylated bis(2-ethylhexyl)-tetrabromophthalate (OH-TBPH) which were unexpectedly observed in a commercial standard of TBPH. By combining ultra-high resolution (UHR) mass spectra (MS(1)), mass errors to theoretical [TBPH-Br+O](-) were 2.1 and 1.0 ppm for the two isomers, UHR-MS(2) spectra and NMR analysis; the structures of the two compounds were identified as hydroxylated TBPH with a hydroxyl group on the aromatic ring. Relatively great proportions of the two isomers of OH-TBPH were detected in two technical products, Firemaster 550 (FM-550; 0.1% and 6.2%, respectively) and Firemaster BZ 54 (BZ-54; 0.1% and 7.9%), compared to a commercial standard (0.4% and 0.9%). To simultaneously analyze OH-TBPH isomers and TBPH in samples of dust, a method based on LC-UHRMS was developed to quantify the two compounds, using negative and positive ion modes, respectively. The instrumental limit of detection for TBPH was 0.01 μg/L, which was 200-300 times better than traditional methods (2.5 μg/L) based on gas chromatography-mass spectrometry. The analytical method combined with a Florisil cleanup was successfully applied to analyze TBPH and OH-TBPH in 23 indoor dust samples from Saskatoon, Saskatchewan, Canada. Two OH-TBPH isomers, OH-TBPH1 and OH-TBPH2, were detected in 52% and 91% of dust samples, respectively. Concentrations of OH-TBPH2 (0.35 ± 1.0 ng/g) were 10-fold greater than those of OH-TBPH1 (0.04 ± 0.88 ng/g) in dust, which was similar to profiles in FM-550 and BZ-54. TBPH was also detected in 100% of dust samples with a mean concentration of 733 ± 0.87 ng/g. A significant (p < 0.001) log-linear relationship was observed between TBPH and OH-TBPH isomers, further supporting the hypothesis of a common source of emission. Relatively small proportions of OH-TBPH isomers were detected in dust (0.01% ± 0.67 OH-TBPH1 and 0.1% ± 0.60 OH-TBPH2), which were significantly less than those in technical products (p < 0.001). This result indicated different environmental behaviors of OH-TBPH and TBPH. Detection of isomers of OH-TBPH is important, since compounds with phenolic groups have often shown relatively greater toxicities than nonhydroxylated analogues. Further study is warranted to clarify the environmental behaviors and potential toxicities of OH-TBPH isomers.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations19
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueEnvironmental Science & Technology→Same topicToxic Organic Pollutants Impact→French-language works237,207→