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Record W2220051347 · doi:10.1115/imece2002-33819

Numerical Modeling of Si0.15Ge0.85 by the Traveling Solvent Method

2002· article· en· W2220051347 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.

Bibliographic record

VenueFluids Engineering · 2002
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsDalhousie UniversityToronto Metropolitan University
Fundersnot available
KeywordsBuoyancyConvectionMaterials scienceCrystal growthCrystal (programming language)SolventSiliconHeat transferThermodynamicsMechanicsSemiconductorChemistryPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

The traveling solvent method (TSM) is a relatively new and promising technique for the production of high quality semiconductors. TSM has been tested on many alloys producing pure and homogeneous crystals. In the present study the effect of buoyancy convection on the growth of the Si0.15Ge0.85 crystal grown by the traveling solvent method is investigated under different heating conditions. The full Navier-Stokes equations together with the energy and solutal equations were solved numerically using the finite element technique. The model take into consideration the losses of heat by radiation and the use of the phase diagram to determine the silicon concentration at the growth interface. Results revealed a strong convection in the solvent, which in turn is detrimental to the growth uniformity in the crystal rod. Additional numerical results showed that the convective heat transfer significantly influences the solute distribution in the liquid zone and the growth rate increases substantially.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.467

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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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