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Record W3163535783 · doi:10.1021/acs.jpcc.1c01740

How Microscopic Features of Mineral Surfaces Critically Influence Heterogeneous Ice Nucleation

2021· article· en· W3163535783 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
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of Canada
KeywordsNucleationIce nucleusIce crystalsGibbsiteChemical physicsKaoliniteMineralHematiteMicaAmorphous iceMaterials scienceMineralogyMuscoviteCrystallographyChemistryQuartzPhysicsComposite materialMeteorology

Abstract

fetched live from OpenAlex

Heterogeneous ice nucleation is an important process in environmental, biological, and atmospheric science. A variety of mineral, organic, and biological materials function as ice nucleating particles (INPs). A major aim of the current research is to determine exactly why and how ice nucleates on certain surfaces but not on others. The lattice match to ice and the atomistic surface morphology have both emerged as important factors, but how these factors interact, and which aspects play a vital role in ice nucleation, is not yet understood. This is the central question addressed in the present paper. We focus on four atmospherically relevant minerals: kaolinite, α-alumina, gibbsite, and hematite. These minerals are structurally similar, but the former pair are excellent INPs, while the latter pair are not. These four minerals provide an opportunity to systematically examine how details of lattice match and/or surface morphology favor or inhibit ice nucleation. We use molecular simulations to examine the interaction of a realistic water model with protonated (001) mineral surfaces. As expected from earlier simulations, ice nucleates via the basal plane for α-alumina, but via the primary prism face for kaolinite. Ice nucleation was not observed for gibbsite and hematite, consistent with experiment. To analyze water structure in the surface layers, we introduce a two-dimensional (2D) lattice perspective. Basal and prism face ice bilayers can be decomposed into two, 2D lattices (triangular for basal, rectangular for prism), and a surface must stabilize both 2D lattices of a bilayer to initiate ice nucleation. We define a 2D lattice mismatch parameter, which, unlike the conventional lattice mismatch criterion, is sensitive to the atomistic structure of a surface. Combining this approach with simulations involving scaled lattices, we clearly show how lattice match, and, more importantly, details of the surface morphology determine the wide range of ice nucleating activity displayed by the four minerals. We believe that the approach followed in this paper will contribute to the general understanding and prediction of ice nucleation (or lack thereof) by other surfaces. Ice nucleation by β-AgI and PbI2, and the absence of nucleation by the mineral boehmite are briefly discussed from a 2D lattice perspective.

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 score0.048
Threshold uncertainty score0.228

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)0.0000.000

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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