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Record W3080573378 · doi:10.5194/amt-14-2799-2021

Elemental analysis of oxygenated organic coating on black carbon particles using a soot-particle aerosol mass spectrometer

2021· article· en· W3080573378 on OpenAlexafffund
Mutian Ma, Laura-Hélèna Rivellini, Yuxi Cui, Megan D. Willis, Rio Wilkie, Jonathan P. D. Abbatt, Manjula R. Canagaratna, Junfeng Wang, Xinlei Ge, Alex K. Y. Lee

Bibliographic record

VenueAtmospheric measurement techniques · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoNational University of Singapore
KeywordsVaporizationAerosolSootMass spectrometryChemistryCarbon blackParticle (ecology)SpectrometerTotal organic carbonAnalytical Chemistry (journal)Environmental chemistryCombustionOrganic chemistryChromatographyOpticsPhysics

Abstract

fetched live from OpenAlex

Chemical characterization of organic coatings is important to advance our understanding of the physio-chemical properties and environmental fate of black carbon (BC) particles. The soot-particle aerosol mass spectrometer (SP-AMS) has been utilized for this purpose in recent field studies. The laser vaporization (LV) scheme of the SP-AMS can heat BC cores gradually until they are completely vaporized, during which organic coatings can be vaporized at temperatures lower than that of the thermal vaporizer (TV) used in a standard high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) that employs flash vaporization. This work investigates the effects of vaporization schemes on fragmentation and elemental analysis of known oxygenated organic species using three SP-AMS instruments. We show that LV can reduce fragmentation of organic molecules. Substantial enhancement of C 2 H 3 O+/CO2+ and C 2 H 4 O2+ signals was observed for most of the tested species when the LV scheme was used, suggesting that the observational frameworks based on the use of HR-ToF-AMS field data may not be directly applicable for evaluating the chemical evolution of oxygenated organic aerosol (OOA) components coated on ambient BC particles. The uncertainties of H:C and O:C determined using the improved-ambient (I-A) method for both LV and TV approaches were similar, and scaling factors of 1.10 for H:C and 0.89 for O:C were determined to facilitate more direct comparisons between observations from the two vaporization schemes. Furthermore, the I-A method was updated based on the multilinear regression model for the LV scheme measurements. The updated parameters can reduce the relative errors of O:C from −26.3 % to 5.8 %, whereas the relative errors of H:C remain roughly the same. Applying the scaling factors and the updated parameters for the I-A method to ambient data, we found that even though the time series of OOA components determined using the LV and TV schemes are strongly correlated at the same location, OOA coatings were likely less oxygenated compared to those externally mixed with BC.

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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.234
Teacher spread0.201 · 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
GenreMethods

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

Citations7
Published2021
Admission routes2
Has abstractyes

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