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Record W2048489088 · doi:10.1103/physrevb.81.235301

Epitaxial growth in dislocation-free strained asymmetric alloy films

2010· article· en· W2048489088 on OpenAlexaff
Rashmi C. Desai, HoKwon Kim, A. C. Chatterji, Darryl Ngai, Si Chen, Nan Yang

Bibliographic record

VenuePhysical Review B · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpinodal decompositionMaterials scienceSpinodalEpitaxyCondensed matter physicsAlloyInstabilityLattice (music)AsymmetryDislocationLattice constantThermodynamicsPhase (matter)Composite materialOpticsLayer (electronics)DiffractionPhysics

Abstract

fetched live from OpenAlex

Epitaxial growth in strained asymmetric, dislocation-free, coherent, alloy films is explored. Linear-stability analysis is used to theoretically analyze the coupled instability arising jointly from the substrate-film lattice mismatch (morphological instability) and the spinodal decomposition mechanism. Both the static and growing films are considered. Role of various parameters in determining stability regions for a coherent growing alloy film is investigated. In addition to the usual parameters: lattice mismatch $ϵ$, solute-expansion coefficient $\ensuremath{\eta}$, growth velocity $V$, and growth temperature $T$, we consider the alloy asymmetry arising from its mean composition. The dependence of elastic moduli on composition fluctuations and the coupling between top surface and underlying bulk of the film also play important roles. The theory is applied to group III-V films such as GaAsN, InGaN, and InGaP and to group IV Si-Ge films at temperatures below the bare critical temperature ${T}_{c}$ for strain-free spinodal decomposition. The dependences of various material parameters on mean concentration and temperature lead to significant qualitative changes.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

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)0.0010.000

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 head0.010
GPT teacher head0.285
Teacher spread0.275 · 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 designTheoretical or conceptual
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

Citations13
Published2010
Admission routes1
Has abstractyes

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