MétaCan
Menu
Back to cohort
Record W2316294874 · doi:10.1021/jz201113m

Heterogeneous Ice Nucleation Induced by Electric Fields

2011· article· en· W2316294874 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of British Columbia
FundersWestern Canada Research GridCompute Canada
KeywordsIce nucleusNucleationSupercoolingElectric fieldChemical physicsNucleusIce formationLiquid waterMaterials sciencePhysicsAtmospheric sciencesGeologyEarth scienceMeteorologyThermodynamicsBiologyNeuroscience

Abstract

fetched live from OpenAlex

Heterogeneous ice nucleation is an important phenomenon in the physical environment influencing atmospheric and biological processes. Despite this relevance, the mechanism of heterogeneous ice nucleation is not understood at the microscopic level, and what exactly constitutes a good ice nucleus is an open question. Employing molecular dynamics simulations, we demonstrate that an electric field, which acts very near a surface, can create an effective ice nucleus in models of supercooled liquid water. To serve as an ice nucleus, the field must polarize only a very thin water layer (∼10 Å), and the field strength required is realistic on the relevant length scale. Our results support the idea that local electric fields could play a major role in heterogeneous ice nucleation, particularly for the very rough particles with many surface structure variations, that serve as ice nuclei in environmentally realistic situations.

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.

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 score0.006
Threshold uncertainty score0.857

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)0.0010.000

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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