MétaCan
Menu
Back to cohort
Record W2067536854 · doi:10.1115/pvp2002-1537

A Three Dimensional Simulation Model for Liquid Phase Electroepitaxy Under Magnetic Field

2002· article· en· W2067536854 on OpenAlexaff
Yoncai Liu, Hamdi Sheibani, Susumu Sakai, Yasunori Okano, S. Dost

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMagnetic fieldIntensity (physics)Flow (mathematics)Field (mathematics)Condensed matter physicsMechanicsMaterials sciencePhase (matter)PhysicsOpticsMathematics

Abstract

fetched live from OpenAlex

A three-dimensional numerical simulation for the Liquid Phase Electroepitaxial (LPEE) growth of GaAs under a vertical stationary magnetic field was carried out. The effect of magnetic field intensity on the flow field in the liquid solution was investigated. Numerical results show that the flow patterns exhibit three distinct stability characteristics: a stable flow field up to a magnetic field level of Ha = 150, a transitional flow between Ha = 150 and Ha = 220, and an unstable flow above Ha = 220. In the stable region, the applied magnetic field suppresses the flow field, and the flow intensity decreases with increasing magnetic field. In the transitional region, the flow intensity increases dramatically with increase in the magnetic field strength. The flow patterns are significantly different from those in the stable region. The flow field is no longer axisymmetric but still stable. In the unstable region, the flow structure and intensity change with time. Under a strong magnetic field, the flow cells are confined to the vicinity of the vertical wall and exhibit significant non-uniformity near the growth interface. Such strong flow fluctuations and non-uniformities near the growth interface may have an adverse effect on the growth process and lead to an unsatisfactory growth.

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 categoriesInsufficient payload (model declined to judge)
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 score0.842
Threshold uncertainty score0.997

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)0.0040.000

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 head0.056
GPT teacher head0.299
Teacher spread0.242 · 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.

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
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicSolidification and crystal growth phenomenaFrench-language works237,207