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II–VI nanowire radial heterostructures

2013· article· en· W2020426301 on OpenAlexaff
K. B. Kahen, Irene A. Goldthorpe, M. Holland

Bibliographic record

VenueNanotechnology · 2013
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanowireMaterials sciencePhotoluminescenceHeterojunctionOptoelectronicsBand gapExcitonLuminescencePassivationDopantNanotechnologyDopingCondensed matter physics

Abstract

fetched live from OpenAlex

There are many reports of ZnSe nanowire synthesis, but photoluminescence measurements on these nanowires indicate weak band-edge and high sub-bandgap defect emission. The two main contributors to the non-optimal photoluminescence are nanowire growth at high temperatures and unpassivated surface states. In this paper, the synthesis of II-VI core-shell nanowires by metal organic vapor phase epitaxy is reported. We demonstrate that larger bandgap shells that passivate the nanowire surface states can be deposited around the nanowires by increasing the partial pressures of the shell reactants without a large increase in growth temperature, allowing high quality material to be obtained. The deposition of nearly lattice-matched ZnMgSSe shells on the ZnSe nanowires increases the band-edge luminescent intensity of the ZnSe nanowires by more than four orders of magnitude and improves the band-edge to defect photoluminescence intensity ratio to 12,000:1. The corresponding full widths at half maximum of the band-edge exciton peaks of the core-shell nanowires can be as narrow as 2.8 nm. It is also shown that magnesium and chlorine can be incorporated into the ZnSe nanowire cores, which shortens the emission wavelength and is known to act as an n-type dopant, respectively.

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 score0.284
Threshold uncertainty score0.658

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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
Published2013
Admission routes1
Has abstractyes

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