MétaCan
Menu
Back to cohort
Record W2785819726 · doi:10.1021/acs.est.7b05575

Unified Theory of Vapor–Wall Mass Transport in Teflon-Walled Environmental Chambers

2018· article· en· W2785819726 on OpenAlexfundno aff
Yuanlong Huang, Ran Zhao, Sophia M. Charan, Christopher M. Kenseth, Xuan Zhang, John H. Seinfeld

Bibliographic record

VenueEnvironmental Science & Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersDivision of Atmospheric and Geospace SciencesDivision of Graduate EducationNatural Sciences and Engineering Research Council of Canada
KeywordsVolatility (finance)AerosolChemical vapor depositionEnvironmental chamberChemistryWater vaporVolatile organic compoundVapor phaseMaterials scienceThermodynamicsChemical engineeringAnalytical Chemistry (journal)Environmental chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Secondary organic aerosol (SOA) formation is studied in laboratory chambers, in which volatile organic compounds (VOCs) are oxidized to produce low-volatility compounds that condense into the aerosol phase. It has been established that such oxidized low-volatility compounds can partition into the chamber walls, which traditionally consist of Teflon film. Several studies exist in which the rates of uptake of individual vapor compounds to the chamber walls have been measured, but a unified theory capable of describing the range of experimental measurements has been lacking. Here, a two-layer model of observed short and long vapor-wall interaction time scales in Teflon-walled environmental chambers is presented and shown to be consistent with experimental data on the rate of wall deposition of more than 90 compounds. Semiempirical relationships between key parameters in the model and vapor molecular properties are derived, which can be used to predict the fate of gas-phase vapor in the chamber under dry 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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.996

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 studies0.0000.007
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.178
Teacher spread0.173 · 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 designObservational
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

Citations124
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicAtmospheric chemistry and aerosolsFrench-language works237,207