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Record W2415376355 · doi:10.1021/acs.est.5b03392

Chemical Reactivity and Liquid/Nonliquid States of Secondary Organic Material

2015· article· en· W2415376355 on OpenAlexaff
Yongjie Li, Pengfei Liu, Zhaoheng Gong, Yan Wang, Adam P. Bateman, C. Bergoend, Allan K. Bertram, Scot T. Martin

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersOffice of ScienceNational Science Foundation
KeywordsChemistryReactivity (psychology)TolueneIsopreneRelative humidityOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

The reactivity of secondary organic material (SOM) of variable viscosity, ranging from nonliquid to liquid physical states, was studied. The SOM, produced in aerosol form from terpenoid and aromatic precursor species, was reacted with ammonia at variable relative humidity (RH). The ammonium-to-organic mass ratio (MNH4+/MOrg) increased monotonically from <5% RH to a limiting value at a threshold RH, implicating a transition from particle reactivity limited by diffusion at low RH to one limited by other factors at higher RH. For the studied size distributions and reaction times, the transition corresponded to a diffusivity above 10-17.5 ± 0.5 m2 s-1. The threshold RH values for the transition were <5% RH for isoprene-derived SOM, 35-45% RH for SOM derived from α-pinene, toluene, m-xylene, and 1,3,5-trimethylbenzene, and >90% for β-caryophyllene-derived SOM. The transition RH for reactivity differed in all cases from the transition RH of a nonliquid to a liquid state. For instance, for α-pinene-derived SOM the transition for chemical reactivity of 35-45% RH can be compared to the nonliquid to liquid transition of 65-90% RH. These differences imply that chemical transport models of atmospheric chemistry should not use the SOM liquid to nonliquid phase transition as one-to-one surrogates of SOM reactivity.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.181
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

Citations88
Published2015
Admission routes1
Has abstractyes

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