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Finite Element Modeling of Silicon Transport into Germanium Using a Simplified Crystal Growth Technique

2011· article· en· W2090505687 on OpenAlexaff
Farid Mechighel, Mohammed El Ganaoui, Bernard Pateyron, Mahfoud Kadja, S. Dost

Bibliographic record

VenueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSiliconGermaniumMaterials scienceFinite element methodDissolutionWork (physics)MechanicsMomentum (technical analysis)Mass transferTransport phenomenaMass transportFlow (mathematics)Crystal growthFluid dynamicsThermodynamicsChemistryMetallurgyEngineering physicsPhysics

Abstract

fetched live from OpenAlex

A numerical simulation study, using finite element method, was carried out to examine the temperature and concentration fields in the dissolution process of silicon into germanium melt. This work utilized a simplified configuration which may be considered to be similar material configuration to that used in the Vertical Bridgman growth methods. The concentration profile for the Si-Ge sample processed using this technique shows increasing transport silicon into the melt with time, moreover, a flat stable interface is observed. The mass and momentum equations for fluid flow, the energy and the solute mass transport were numerically solved. Results showed good agreements with experiments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.040
GPT teacher head0.278
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations2
Published2011
Admission routes1
Has abstractyes

Explore more

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