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Record W2991641518 · doi:10.1021/acs.jpcb.9b09775

Cluster Formation at Extreme Supersaturations: Nucleation versus Barrierless Transition

2019· article· en· W2991641518 on OpenAlexaboutno aff
V. I. Kalikmanov, Rob Hagmeijer

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCluster (spacecraft)NucleationMaterials scienceThermodynamicsPhysicsComputer science

Abstract

fetched live from OpenAlex

We study the process of cluster formation at extreme supersaturations and identify the temperature-supersaturation domain where evaporation can be neglected resulting in a barrierless process with kinetics dominated by the dimer formation. The cluster size distribution obeys the coalescence equation with the pressure–temperature-dependent association rate coefficients ki, j. In view of the crucial role played by kinetics under these extreme conditions, the values of these coefficients calculated within the free molecular collision model are insufficient for the prediction of the nucleation rate and cluster distribution. An alternative is the use of ki, j obtained from the ab initio calculations. We apply these considerations to the analysis of recent water nucleation experiments in the postnozzle flow of a Laval nozzle. Theoretical predictions of nucleation rate are in an excellent agreement with experiment. At the same time, there is a discrepancy in the densities of small clusters. The latter can be attributed to the difference in ki, j extracted from the experimental data and those resulted from the ab initio calculations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.360
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.221
Teacher spread0.204 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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