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Record W2005488709 · doi:10.1021/es400846d

Indoor Secondary Organic Aerosol Formation Initiated from Reactions between Ozone and Surface-Sorbed <scp>d</scp>-Limonene

2013· article· en· W2005488709 on OpenAlexaff
Michael S. Waring, Jeffrey A. Siegel

Bibliographic record

VenueEnvironmental Science & Technology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersUniversity of Texas at AustinNational Science Foundation
KeywordsAerosolOzoneLimoneneEnvironmental chemistryChemistryEnvironmental scienceOrganic chemistryFood science

Abstract

fetched live from OpenAlex

Reactions between ozone and terpenoids produce numerous products, some of which may form secondary organic aerosol (SOA). This work investigated the contribution to gas-phase SOA formation of ozone reactions with surface-sorbed D-limonene, which is common indoors. A model framework was developed to predict SOA mass formation because of ozone/terpenoid surface reactions, and it was used with steady state experiments in a 283 L chamber to determine the aerosol mass fraction of SOA resulting from surface reactions, ξs (the ratio of mass of SOA formed and mass of ozone consumed by ozone/terpenoid surface reactions), for ozone/D-limonene reactions on stainless steel. The ξs = 0.70-0.91, with lower relative humidity leading to both higher mass and number formation. Also, surface reactions promoted nucleation more than gas-phase reactions, and number formation due to surface reactions and gas-phase reactions were 126-339 and 51.1-60.2 no./cm(3) per μg/m(3) of formed SOA, respectively. We also used the model framework to predict that indoor spaces in which ozone/D-limonene surface reactions would likely lead to meaningful gas-phase SOA formation are those with surfaces that have low original reactivity with ozone, such as glass, sealed materials, or smooth metals.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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.

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

Citations91
Published2013
Admission routes1
Has abstractyes

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