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Record W2985993443 · doi:10.1039/c9fd00095j

Silicon photosensitisation using molecular layers

2019· article· en· W2985993443 on OpenAlexaff
Lefteris Danos, Nathan R. Halcovitch, Ben P. Wood, Henry Banks, Michael P. Coogan, Nicholas P. Alderman, Liping Fang, Branislav Dzurňák, Tom Markvart

Bibliographic record

VenueFaraday Discussions · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of ChinaMinisterstvo Školství, Mládeže a TělovýchovyLeverhulme TrustLancaster UniversityNational Science Foundation
KeywordsSiliconPhotovoltaicsEnergy transferNanotechnologyMaterials scienceEngineering physicsOptoelectronicsPhysicsPhotovoltaic systemEngineering

Abstract

fetched live from OpenAlex

Silicon photosensitisation via energy transfer from molecular dye layers is a promising area of research for excitonic silicon photovoltaics. We present the synthesis and photophysical characterisation of vinyl and allyl terminated Si(111) surfaces decorated with perylene molecules. The functionalised silicon surfaces together with Langmuir-Blodgett (LB) films based on perylene derivatives were studied using a wide range of steady-state and time resolved spectroscopic techniques. Fluorescence lifetime quenching experiments performed on the perylene modified monolayers revealed energy transfer efficiencies to silicon of up to 90 per cent. We present a simple model to account for the near field interaction of a dipole emitter with the silicon surface and distinguish between the 'true' FRET region (<5 nm) and a different process, photon tunnelling, occurring for distances between 10-50 nm. The requirements for a future ultra-thin crystalline solar cell paradigm include efficient surface passivation and keeping a close distance between the emitter dipole and the surface. These are discussed in the context of existing limitations and questions raised about the finer details of the emitter-silicon interaction.

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 score0.008
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 head0.242
Teacher spread0.234 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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