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Record W2313722053 · doi:10.1021/nl400975u

Looking Inside a Working SiLED

2013· article· en· W2313722053 on OpenAlexaff
Florian Maier‐Flaig, Christian Kübel, Julia Rinck, Tobias Bocksrocker, Torsten Scherer, Robby Prang, Annie K. Powell, Geoffrey A. Ozin, Uli Lemmer

Bibliographic record

VenueNano Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransmission electron microscopyDegradation (telecommunications)ElectroluminescenceMaterials scienceSiliconSpectroscopyDiodeOptoelectronicsElectron energy loss spectroscopyChemical engineeringNanotechnologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this study, we investigate for the first time morphological and compositional changes of silicon quantum dot (SiQD) light-emitting diodes (SiLEDs) upon device operation. By means of advanced transmission electron microscopy (TEM) analysis including energy filtered TEM (EFTEM) and energy dispersive X-ray (EDX) spectroscopy, we observe drastic morphological changes and degradation for SiLEDs operated under high applied voltage ultimately leading to device failure. However, SiLEDs built from size-separated SiQDs operating under normal conditions show no morphological and compositional changes and the biexponential loss in electroluminescence seems to be correlated to chemical and physical degradation of the SiQDs. By contrast, we found that, for SiLEDs fabricated from polydisperse SiQDs, device degradation is more pronounced with three main modes of failure contributing to the reduced overall lifetime compared to those prepared from size-separated SiQDs. With this newfound knowledge, it is possible to devise ways to increase the lifetimes of SiLEDs.

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 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 score0.012
Threshold uncertainty score0.999

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)0.0020.003

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 head0.019
GPT teacher head0.206
Teacher spread0.187 · 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

Citations32
Published2013
Admission routes1
Has abstractyes

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