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Record W2980111040 · doi:10.1039/c9nr04003j

Efficient light harvesting in hybrid quantum dot–interdigitated back contact solar cells <i>via</i> resonant energy transfer and luminescent downshifting

2019· article· en· W2980111040 on OpenAlexaff
Chirenjeevi Krishnan, Thomas M. Mercier, Tasmiat Rahman, Giacomo Piana, M. Brossard, Timur Yagafarov, Alexander To, Michael Pollard, Peter J. Shaw, Darren M. Bagnall, Bram Hoex, Stuart A. Boden, Pavlos G. Lagoudakis, Martin D. B. Charlton

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsKensington Health
FundersEngineering and Physical Sciences Research Council
KeywordsQuantum dotOptoelectronicsMaterials scienceSolar cellLuminescenceEnergy transferQuantumHybrid solar cellPhysicsEngineering physicsPolymer solar cellQuantum mechanics

Abstract

fetched live from OpenAlex

spacer layers, with RET efficiencies of up to 15%. In the conventional LDS architecture, down-converters are deposited on the surface of an optimised anti-reflection layer, providing relatively narrowband enhancement, whereas the QDs in our hybrid architecture provide optical enhancement over the broadband wavelength range, by simultaneously utilising LDS, RET-mediated carrier injection, and antireflection effects, resulting in up to 40% improvement in the power conversion efficiency (PCE). Low-cost synthesis of QDs and simple device integration provide a cost-effective solution for boosting solar cell performance.

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 categoriesMeta-epidemiology (narrow)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.198
Teacher spread0.191 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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