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Record W2035686795 · doi:10.1021/jp907504q

Tuning Photoluminescence of Ge/GeO<sub>2</sub> Core/Shell Nanoparticles by Strain

2009· article· en· W2035686795 on OpenAlexaff
Cailei Yuan, Hui Cai, Pooi See Lee, Jiyuan Guo, Jun He

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanoparticlePhotoluminescenceMaterials scienceShell (structure)Strain engineeringStrain (injury)Core (optical fiber)NanotechnologyNanocrystalExcitonSemiconductorOptoelectronicsComposite materialCondensed matter physicsSiliconPhysics

Abstract

fetched live from OpenAlex

The distribution of strain field plays an important role in deciding the physical properties of nanocrystals. The growth strain of Ge/GeO 2 core/shell nanoparticles embedded in a regular array of Al 2 O 3 nanoparticles and its resulting effect on the optical properties are investigated. Two-dimensional finite element calculations clearly demonstrate that Ge/GeO 2 nanoparticles certainly experienced greater compressive strain in Al 2 O 3 nanoparticles than in Al 2 O 3 thin film, especially at the GeO 2 shell area. This may lead to much more strain-relaxing defects produced at the GeO 2 shell in Al 2 O 3 nanoparticles. Meanwhile, the photogenerated excitons/electron−hole pairs are localized by defects located at the GeO 2 shell and are forced to recombine while being spatially confined in the Al 2 O 3 nanoparticles. These effects might contribute to the observed intensity enhancement and blue shift of the photoluminescence peaks for the sample with Ge/GeO 2 core/shell nanoparticles embedded in Al 2 O 3 nanoparticles. The findings presented here provide physical insight and offer useful guidelines to controllably modify the optical properties of semiconductor nanoparticles through strain engineering.

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 score0.001
Threshold uncertainty score0.515

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 science0.0010.000
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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