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Record W2312999161 · doi:10.1021/jz2003342

Why Do Sulfuric Acid Coatings Influence the Ice Nucleation Properties of Mineral Dust Particles in the Atmosphere?

2011· article· en· W2312999161 on OpenAlexafffund
Allan K. Bertram, Keng C. Chou

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSulfuric acidMicaIce nucleusNucleationAqueous solutionAdsorptionChemistryMineral dustSaturation (graph theory)MineralInorganic chemistrySulfateChemical engineeringMineralogyMaterials scienceAerosolPhysical chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Laboratory studies with supermicrometer particles have shown that mineral particles coated with sulfuric acid are relatively poor ice nuclei. We investigated this phenomenon, which is of atmospheric relevance, by probing the structure of water at the mineral-aqueous acid interface as a function of the sulfuric acid concentration using sum frequency generation vibrational spectroscopy. We found that ordered water structures at water/mica interfaces drastically diminished at molarities of sulfuric acid equal to 0.5 M and totally disappeared when the molarities reached 5 M. The decrease in ordered water structures at the interface was caused by a combined effect of the decreased mica surface potential at low pH, the adsorption of sulfates on mica, and the lack of free water molecules in high concentrations of acidic solution. The good ice nucleation ability above liquid water saturation is correlated with the presence of structured water, suggesting that structured water at the interface may be needed for efficient heterogeneous ice nucleation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.206

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 science0.0010.000
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 head0.016
GPT teacher head0.188
Teacher spread0.172 · 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

Citations48
Published2011
Admission routes2
Has abstractyes

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