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Record W2784161276 · doi:10.1063/1.5001521

The influence of ion hydration on nucleation and growth of LiF crystals in aqueous solution

2018· article· en· W2784161276 on OpenAlexafffund
G. Lanaro, G. N. Patey

Bibliographic record

VenueThe Journal of Chemical Physics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCompute Canada
KeywordsNucleationCrystal (programming language)MetastabilityChemical physicsIonMolecular dynamicsAqueous solutionCrystal growthClassical nucleation theoryChemistryCrystallographyMaterials sciencePhysical chemistryComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Molecular dynamics (MD) simulations are employed to investigate crystal nucleation and growth in oversaturated aqueous LiF solutions. Results obtained for a range of temperatures provide evidence that the rate of crystal growth is determined by a substantial energy barrier (∼49 kJ mol−1) related to the loss of water from the ion hydration shells. Employing direct MD simulations, we do not observe spontaneous nucleation of LiF crystals at 300 K, but nucleation is easily observable in NVT simulations at 500 K. This contrasts with the NaCl case, where crystal nucleation is directly observed in similar simulations at 300 K. Based on these observations, together with a detailed analysis of ion clustering in metastable LiF solutions, we argue that the ion dehydration barrier also plays a key role in crystal nucleation. The hydration of the relatively small Li+ and F− ions strongly influences the probability of forming large, crystal-like ion clusters, which are a necessary precursor to nucleation. This important factor is not accounted for in classical nucleation theory.

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 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 score0.140
Threshold uncertainty score0.097

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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.

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

Citations9
Published2018
Admission routes2
Has abstractyes

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