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Record W4400040946 · doi:10.1103/physrevb.109.235207

Absorption spectrum of doped highly mismatched alloys

2024· article· en· W4400040946 on OpenAlexafffund
Hassan Allami, Jacob J. Krich

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDopingMaterials scienceSpectrum (functional analysis)Absorption (acoustics)Absorption spectroscopyOptoelectronicsOpticsPhysicsComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

Highly mismatched alloys are a class of semiconductor alloys with large electronegativity differences between the alloying elements. We predict that the absorption spectrum due to transitions between the split bands of a doped highly mismatched alloy with a conduction band anticrossing shows qualitative features revealing the fractional distribution of states in the split bands and providing valuable insight into their electronic structure. Our prediction is based on the analysis of the joint densities of states for both direct and indirect transitions between the split bands. In particular, we predict a peak near the absorption edge, which arises due to the suppression of direct transitions at large momenta. As a result of the suppression of direct transitions, indirect transitions dominate the spectrum away from the edge of absorption. We present analytic forms of the near-absorption-edge and large-energy behaviors of the spectra, comparing them with the asymptotic forms of absorption from a single deep impurity level.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.018
GPT teacher head0.352
Teacher spread0.334 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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