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Record W2325080852 · doi:10.1021/jp506898c

Changes in Secondary Organic Aerosol Composition and Mass due to Photolysis: Relative Humidity Dependence

2014· article· en· W2325080852 on OpenAlexafffund
Jenny P. S. Wong, Shouming Zhou, Jonathan P. D. Abbatt

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoCanada Foundation for Innovation
KeywordsRelative humidityPhotodissociationChemistryAerosolOzoneMass spectrometryPhotochemistryAmmonium sulfateChemical compositionEnvironmental chemistryAnalytical Chemistry (journal)ChromatographyOrganic chemistryMeteorology

Abstract

fetched live from OpenAlex

This study is focused on the relative humidity (RH) dependence of water-soluble secondary organic aerosol (SOA) aging by photolysis. Particles containing α-pinene SOA and ammonium sulfate, generated by atomization, were exposed to UV radiation in an environmental chamber at three RH conditions (5, 45, and 85%), and changes in chemical composition and mass were monitored using an aerosol mass spectrometer (AMS). Under all RH conditions, photolysis leads to substantial loss of SOA mass, where the rate of mass loss decreased with decreasing RH. For all RH conditions, the less oxidized components of SOA (e.g., carbonyls) exhibited the fastest photodegradation rates, which resulted in a more oxidized SOA after photolytic aging. The photolytic reactivity of SOA material exhibited a dependence on RH likely due to moisture-induced changes in SOA morphology or phase. The results suggest that the atmospheric lifetime of SOA with respect to photolysis is dependent on its RH cycle, and that photolysis may be an important sink for some SOA components occurring on an initial time scale of a few hours under ambient conditions.

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 score0.007
Threshold uncertainty score0.533

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 communication0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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

Citations101
Published2014
Admission routes2
Has abstractyes

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