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Record W2046348955 · doi:10.1149/1.3700406

(Invited) Ultrafast Carrier Dynamics in Silicon Nanocrystal Films

2012· article· en· W2046348955 on OpenAlexafffund
Lyubov V. Titova, Tyler L. Cocker, Xiongyao Wang, A. Meldrum, Frank A. Hegmann

Bibliographic record

VenueECS Transactions · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocrystalMaterials sciencePhotoexcitationPicosecondPhotoluminescencePercolation (cognitive psychology)Percolation thresholdConductivitySiliconDrude modelScatteringPhotoconductivityOptoelectronicsUltrashort pulseNanotechnologyChemical physicsCondensed matter physicsElectrical resistivity and conductivityOpticsChemistryExcited stateAtomic physicsPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

We have applied time-resolved THz spectroscopy to probe the transient photoexcited carrier dynamics and THz conductivity in Si nanocrystal films with varying silicon volume filling fractions and nanocrystal sizes on picosecond time scales. The THz conductivity reveals microscopic carrier motion with significant interface scattering within nanocrystals as well as percolative transport between nanocrystals. The time variation of the THz conductivity is analyzed within the framework of the Drude-Smith model, an extension of the Drude model that characterizes carrier localization in nanostructured materials. Below the percolation threshold, transport between nanocrystals is inhibited and photoexcited carriers are localized within individual nanocrystals. These films also exhibit efficient optical emission. In films with Si filling fractions above the percolation threshold, photoluminescence is suppressed and a transition from long-range inter-nanocrystal transport immediately after photoexcitation to increased carrier localization over a 50 ps time scale due to accumulation of charges at interface defect sites is observed.

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 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 score0.021
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)0.0020.000

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 head0.226
Teacher spread0.217 · 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.

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

Citations6
Published2012
Admission routes2
Has abstractyes

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