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Structures and Transport Properties of CaCO<sub>3</sub> Melts under Earth’s Mantle Conditions

2017· article· en· W2769152536 on OpenAlexafffund
Xiangpo Du, Min Wu, John S. Tse, Yuanming Pan

Bibliographic record

VenueACS Earth and Space Chemistry · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Saskatchewan
FundersNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSilicateCarbonateMantle (geology)Calcium carbonateViscosityMolecular dynamicsDiffusionIonMineralogyEarth (classical element)Materials scienceChemical physicsThermodynamicsChemistryGeologyGeochemistryPhysicsComposite materialMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Carbonatitic and carbonated silicate melts generated from melting in the mantle are the chief agents for liberating carbon from the solid Earth and exert important controls on Earth’s deep carbon cycle. However, significant gaps in our knowledge about the carbonatitic melts under conditions pertinent to Earth’s deep interior remain due to experimental challenges. Here, we report on a first-principles molecular dynamics (FPMD) calculation of calcium carbonate (CaCO 3 ) melts at pressures up to 52.5 GPa. Our FPMD calculations reproduce the ultralow viscosity measured by experiments and confirm the ideal liquid behavior of calcium carbonate melts at pressures below 11.2 GPa. However, calcium carbonate melts are characterized by a pronounced nonideal liquid behavior at pressures above 11.2 GPa, arising from (1) the temporal formation of small carbonate clusters and (2) increased interactions between Ca 2+ and CO 3 2– ions. It is found that the Stokes–Einstein equation relating the viscosity with the diffusion coefficients still holds at high pressure provided that a suitable effective particle size can be chosen.

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 score0.290
Threshold uncertainty score0.426

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 head0.193
Teacher spread0.182 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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