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Record W2892560419 · doi:10.1038/s41467-018-06622-2

Resolving the mechanisms of hygroscopic growth and cloud condensation nuclei activity for organic particulate matter

2018· article· en· W2892560419 on OpenAlexaff
Pengfei Liu, Mijung Song, Tianning Zhao, Sachin S. Gunthe, Suhan Ham, Yipeng He, Yiming Qin, Zhaoheng Gong, Juliana Carolina Amorim, Allan K. Bertram, Scot T. Martin

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersOffice of ScienceDirectorate for GeosciencesNational Research Foundation of KoreaU.S. Department of EnergyHansjörg Wyss Institute for Biologically Inspired Engineering, Harvard UniversityHarvard UniversityNational Science Foundation
KeywordsParticulatesCloud condensation nucleiCondensationCloud computingEnvironmental scienceEnvironmental chemistryAtmospheric sciencesChemistryAerosolMeteorologyPhysicsComputer science

Abstract

fetched live from OpenAlex

Hygroscopic growth and cloud condensation nuclei activation are key processes for accurately modeling the climate impacts of organic particulate matter. Nevertheless, the microphysical mechanisms of these processes remain unresolved. Here we report complex thermodynamic behaviors, including humidity-dependent hygroscopicity, diameter-dependent cloud condensation nuclei activity, and liquid-liquid phase separation in the laboratory for biogenically derived secondary organic material representative of similar atmospheric organic particulate matter. These behaviors can be explained by the non-ideal mixing of water with hydrophobic and hydrophilic organic components. The non-ideality-driven liquid-liquid phase separation further enhances water uptake and induces lowered surface tension at high relative humidity, which result in a lower barrier to cloud condensation nuclei activation. By comparison, secondary organic material representing anthropogenic sources does not exhibit complex thermodynamic behavior. The combined results highlight the importance of detailed thermodynamic representations of the hygroscopicity and cloud condensation nuclei activity in models of the Earth's climate system.

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 categoriesnone
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 score0.313
Threshold uncertainty score0.289

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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.

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

Citations208
Published2018
Admission routes1
Has abstractyes

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