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Record W3215972157 · doi:10.1021/acs.jpcc.1c08269

Unraveling the Mechanism of Ice Nucleation by Mica (001) Surfaces

2021· article· en· W3215972157 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 C · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicaNucleationMuscoviteChemical physicsIce nucleusMaterials scienceMolecular dynamicsCounterionCrystallographyMineralogyGeologyChemistryComposite materialIonComputational chemistryQuartz

Abstract

fetched live from OpenAlex

Heterogeneous ice nucleation is an important process in atmospheric science, food preservation, and other areas of research. Muscovite mica is a commonly occurring mineral, and although its ice nucleating ability has been widely debated, recent experiments have established that some mica (001) surfaces efficiently nucleate ice. We employ molecular dynamics simulations to investigate ice nucleation by three variations of the mica (001) surface. These are bare surfaces devoid of counterions (B-mica), surfaces with ordered arrangements of K+ counterions (K-mica), and protonated surfaces (H-mica). Our simulations show that B-mica and H-mica effectively nucleate ice, but K-mica does not. For B-mica and H-mica, the ice nucleation mechanism is unusual in that it does not occur via the basal or prism plane of Ih. The mica (001) surfaces stabilize an ice bilayer resembling (but not identical to) the pyramidal (202̅1) plane of Ih. This results in a mixed-phase ice nucleus consisting of hexagonal and cubic ice layers stacked in a particular order imposed by the surface. We discuss in detail the connections between surface composition, morphology, and ice nucleation. The influence of finite system size on ice nucleation is also investigated. Finally, we discuss our simulations in view of recent experimental results. Taken together, the experiments and simulations cast new light on ice nucleation by mica (001) surfaces.

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 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 score0.012
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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