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Record W2333368472 · doi:10.1021/ac504722z

Application of Direct Analysis in Real Time-Mass Spectrometry (DART-MS) to the Study of Gas–Surface Heterogeneous Reactions: Focus on Ozone and PAHs

2015· article· en· W2333368472 on OpenAlexaff
Shouming Zhou, Matthew W. Forbes, Jonathan P. D. Abbatt

Bibliographic record

VenueAnalytical Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsChemistryDART ion sourceDartMass spectrometryOzoneKineticsReagentReactions on surfacesAnalytical Chemistry (journal)Chemical kineticsEnvironmental chemistryIonChromatographyPhysical chemistryOrganic chemistryElectron ionizationAdsorptionIonization

Abstract

fetched live from OpenAlex

A novel analytical method is presented whereby Direct Analysis in Real Time-Mass Spectrometry (DART-MS) is applied to the study of gas-surface heterogeneous reactions. To illustrate the capabilities of the approach, the kinetics of a well-studied reaction of surface-bound polycyclic aromatic hydrocarbons with ozone are presented. Specifically, using helium as the reagent gas and with the DART heater temperature of 500 °C, nanogram quantities of benzo[e]pyrene (BeP) deposited on the outside of glass melting point capillary tubes were analyzed in positive ion mode with a limit of detection of 40 pg. Using bis(2-ethylhexyl) sebacate as an internal standard, the kinetics of the ozone-BeP reaction were assessed by determining the surface-bound BeP decays, after oxidation in an off-line reaction cell. The reaction is demonstrated to follow the Langmuir-Hinshelwood mechanism, known to prevail for heterogeneous reactions of this type. In addition, a wide array of oxygenated, condensed-phase products has been observed. The present work demonstrates the capability of the DART-MS technique to investigate the heterogeneous chemistry taking place on a wide range of surfaces, such as those that form in both outdoor and indoor environments.

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 score0.048
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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

Citations53
Published2015
Admission routes1
Has abstractyes

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