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Record W2338416591 · doi:10.1063/1.4946847

Theoretical investigation of carrier transfer by an optical contacting scheme for optoelectronic application

2016· article· en· W2338416591 on OpenAlexaff
Jianfeng Yang, Rong-Chun Ge, Zhilong Zhang, Weijian Chen, Bo Wang, Yu Feng, Shujuan Huang, Santosh Shrestha, Robert Patterson, Gavin Conibeer

Bibliographic record

VenueJournal of Applied Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsQueen's University
FundersAustralian Renewable Energy AgencyChina Scholarship Council
KeywordsOptoelectronicsCommon emitterPhotonicsPhotonMaterials scienceResonatorOpacityAbsorption (acoustics)Charge carrierCoupling (piping)PhysicsOptics

Abstract

fetched live from OpenAlex

As a promising charge carrier transfer scheme, optical coupling could potentially improve the performance of an optoelectronic device for energy harvesting based on well developed nanotechnology. By extracting carriers optically, the functional features of the nano-structured material could be better used by minimizing the concerns about its electrical properties. In this paper, we present a rigorous electromagnetic model to analyze the optical carrier transfer problem. The flow of the energy is analyzed carefully by the photon transfer spectrum, and the photon emitters (electron-hole pairs) are assumed in a thermal equilibrium described by Bose-Einstein distribution. The result shows that an energy selective carrier transfer can be optically achieved at the device level by integrating the emitter and receiver into a nano-optical resonator, where both the photon emission and absorption are significantly amplified by a near-field coupling around the resonant frequency. General design and optimization schemes in practice are addressed by examining the influence of the photonic design and an energy dependent emissivity of the emitter, which can be used to develop the optical contacting concept further.

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 score0.414
Threshold uncertainty score0.227

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)0.0000.000

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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