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Record W2891856734 · doi:10.1063/1.5031106

Incorporation of Si during vapor phase epitaxy of III-V compounds: Evidence of an enthalpy-entropy compensation effect

2018· article· en· W2891856734 on OpenAlexafffund
R. A. Masut

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsEnthalpyEpitaxySiliconChemical vapor depositionDopingThermodynamicsGrowth rateDopantHomogeneousChemistryKineticsEntropy (arrow of time)Physical chemistryMaterials scienceNanotechnologyOrganic chemistryPhysicsMathematics

Abstract

fetched live from OpenAlex

The incorporation of dopants in III-V compound semiconductor epilayers during chemical vapor deposition involves complex homogeneous and surface reaction kinetics and is expected to be an activated process. In particular, silicon is an element of choice for n-type doping of various III-V compound epilayers for which there is a wealth of data involving different growth and doping precursors. Kinetic arguments such as the role of multi-excitation entropy or quasi-equilibration at the growth interface during incorporation predict an enthalpy-entropy compensation effect (EECE), which is observed from compiled data for more than 14 orders of magnitude of the prefactor describing the activated incorporation rate. For this particular observation of the EECE, an explanation involving data pre-selection imposed by restrictive growth conditions may also be invoked.

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 score0.002
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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