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Record W4321495523 · doi:10.5194/egusphere-egu23-1923

Not all types of secondary organic aerosol mix: two phases observed when mixing different secondary organic aerosol types

2023· preprint· en· W4321495523 on OpenAlexaff
Fabian Mahrt, Long Peng, Julia Zaks, Paul E. Ohno, Natalie R. Smith, Florence K. A. Gregson, Yiming Qin, Celia Faiola, Sergey A. Nizkorodov, Markus Ammann, Scot T. Martin, Allan K. Bertram

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAerosolParticle (ecology)ChemistryPhase (matter)Mixing (physics)Environmental chemistryChemical engineeringChemical physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Atmospheric aerosol particles play an important role for air quality and climate. Secondary organic aerosol (SOA) make up a significant mass fraction of these particles. SOA particles mostly forms from oxidation of gases, followed by gas-particle conversion of the oxidation products. Due to the variety of precursors and oxidation pathways involved in SOA formation, atmospheric SOA rank among the least understood aerosol types. To assess the impacts of SOA particles on air pollution and climate, knowledge of the number of phases in internal mixtures of different SOA types is critical. For example, gas-particle partitioning of organic species, and thus ultimately ambient SOA mass concentration, strongly depend on the number of phases in SOA particles. Atmospheric models traditionally assumed that different SOA types form a single condensed organic phase when internally mixed in individual particles. In case of mixed SOA particles with a single condensed phase uptake of semi-volatile vapors are enhanced, due to a lowering of the activities in the organic aerosol phase, and hence a lowering of the equilibrium partial pressure. By contrast, the equilibrium partial pressure is greater if the different SOA types form separate phases due to repulsive intermolecular forces between immiscible organic molecules. Consequently, enhancement of vapor uptake and ambient SOA mass concentrations will be smaller or absent in the case of phase-separated SOA particles.Here, using fluorescence microscopy, we directly observed the number of phase in individual particles containing mixtures of different SOA types. A total of 6 different SOA types were generated in environmental chambers from oxidation of single precursors. This included both biogenic and anthropogenic SOA types, having elemental oxygen-to-carbon (O/C) ratios between 0.34 and 1.05, covering values characteristic for aged and fresh atmospheric SOA. The number of phases of all possible internal mixtures of two different SOA types, termed SOA+SOA particles, was investigated as a function of humidity between 90% and 0% relative humidity (RH). We found that the number of phases was independent of RH within the range investigated and that 6 out of 15 SOA+SOA mixtures resulted in particles with two condensed organic phases. The observation of phase separated SOA+SOA particles challenges the approach of assuming a single condensed organic phase when representing SOA formation in atmospheric models. Specifically, we demonstrate that the difference in the average O/C ratio between the two SOA types of a mixture (ΔO/C) is a good predictor of the number of phases in particles that are internal mixtures of different SOA types: two-phase SOA+SOA particles formed for ΔO/C ≥ 0.47, while one-phase SOA+SOA particles formed for ΔO/C < 0.47. This threshold ΔO/C provides a simple, yet powerful parameter to predict whether mixtures of fresh and aged SOA particles form one- or two-phase particles in models.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.243
Teacher spread0.196 · 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
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

Citations4
Published2023
Admission routes1
Has abstractyes

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