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Record W2047496361 · doi:10.1021/jp0259566

The Nucleation and Freezing of Dilute Nitric Acid Aerosols

2002· article· en· W2047496361 on OpenAlexaff
D. B. Dickens, J. J. Sloan

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNitric acidSupersaturationNucleationChemistryIce nucleusAtmospheric temperature rangePhase (matter)AerosolMole fractionPhase diagramAnalytical Chemistry (journal)Inorganic chemistryNitric oxideCrystallographyThermodynamicsPhysical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

We report the kinetically constrained phase diagram for nitric acid−water aerosols over the range of nitric acid mole fractions from 0 to 0.5, measured using a temperature-programmable cryogenic flow tube. The freezing temperatures of the aerosol particles, which have radii on the order of 1 μm, are 30−90 K lower than those of the bulk, and the liquid−solid phase boundaries are modified by the differences in the activation energies for nucleation of the nitric acid hydrates. We infer from the shapes of the respective supersaturation curves that water ice nucleates in the liquid droplets at nitric acid mole fractions below about 0.15, and nitric acid trihydrate grows on these ice nuclei. At higher concentrations, nitric acid dihydrate crystals nucleate, and either ice or nitric acid trihydrate crystals grow on the nuclei. Using realistic estimates of the nucleation rate constants, we conclude that ice will nucleate homogeneously in upper tropospheric clouds having nitric acid mole fractions as high as 0.1 if the temperature decreases to 210 K.

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 score0.009
Threshold uncertainty score0.017

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)0.0000.000

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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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

Citations18
Published2002
Admission routes1
Has abstractyes

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