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Record W3023662486

Enhancement of the Heterogeneous Ice Nucleation by the Changing Phase State of Secondary Organic Aerosols

2020· article· en· W3023662486 on OpenAlexvenueno aff
Yue Zhang

Bibliographic record

VenueNPARC · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsIce nucleusPhase (matter)NucleationEnvironmental scienceAtmospheric sciencesChemistryMaterials scienceGeologyPhysicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

Cirrus clouds and their effects on earth’s radiative balance are major sources of uncertainties in predicting future climate. These clouds also dehydrate air ascending to the tropopause, thereby reducing water content in the stratosphere. However, the formation of cirrus clouds is not well understood. Data from field sites and campaigns have shown that organic aerosols (OAs) is a major component of the non-refractory aerosols in the free troposphere where ice cirrus clouds typically form. Measurements by aerosol mass spectrometers in the free troposphere above forests indicate a high mass fraction of these OAs are derived from the atmospheric oxidation of isoprene and other volatile organic compounds (VOCs). Despite their abundance, the effects of these OAs on ice nucleation (IN) is controversial. Previously, these OAs were assumed to be homogeneously mixed liquids, which limits their INabilities. Recent studies have shown that depending on the ambient humidity and temperature, OAs can exist in semi-solid or solid phase states, which can potentially increase INactivity. This laboratory study systematically examines the effects of aerosol-phase state on IN properties of secondary organic aerosols (SOA) produced by the environmental chamber by simultaneously measuring their chemical composition and ice nucleation properties. Selected types of SOA particles were generated by reacting the respective volatile organic compounds (VOCs) with either ozone and/or OH radicals in the MIT environmental chamber and a potential aerosol mass (PAM) oxidation flow reactor. Four kinds of SOA, namely a-pinene SOA, toluene SOA, -caryophyllene SOA, and IEPOX-derived SOA were generated and passed through a temperature control apparatus, where the temperature of the aerosols can be varied between -42°C and 20°C before entering the spectrometer for ice nucleation (SPIN, Droplet Measurement Technologies, Inc.) for determining ice nucleation activity. A scanning mobility particle sizer (SMPS) and an aerosol mass spectrometer (AMS, Aerodyne Inc.) measured the number-diameter distribution and chemical composition of the particles upstream of the SPIN. An optical particle counter downstream of the SPIN measured the optical signatures of the ice particles and some of the large bare organic particles. The SPIN operating temperature was between -38°C and -46°C. Our results show that pre-cooling the aerosol particles to -25 to -42°C enhances the IN onset relative humidity (RH) and the active fraction of IN when compared with non-pre-cooling conditions only for -caryophyllene SOA and IEPOX-derived SOA. Coupled with viscosity and glass transition temperature calculations, we show that the aerosol phase state changes due to the pre-cooling explains this enhancement. By combining the ice nucleation results with chemical analysis of the SOA, our study suggests that the chemical composition influences of these organic aerosols alter the hygroscopicity and the phase state of these organic aerosols, which eventually affects their INproperties. As the phase state of the organic aerosols changes from liquid to semi-solid or solid, their INonset relative humidity decreases, suggesting certain types of SOA (including b-caryophyllene and isoprene SOA) could be potentially important ice nuclei in the free troposphere.

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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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