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Record W2082899833 · doi:10.1039/c2cp43475j

Cu nanoparticles enable plasmonic-improved silicon photovoltaic devices

2012· article· en· W2082899833 on OpenAlexafffund
Michele Lemos de Souza, Paola Cório, Alexandre G. Brolo

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaForeign Affairs and International Trade CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsPhotovoltaic systemPlasmonSiliconNanoparticleNanotechnologyPlasmonic nanoparticlesMaterials scienceOptoelectronicsEngineering physicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This work examines the effect of copper nanoparticles (Cu NPs) on the photocurrent efficiency of silicon photovoltaic (Si PV) devices. An optimized synthesis of stable Cu NPs is reported together with a procedure for their immobilization on the Si PV surface. A comprehensive analysis of the photocurrent and power dependence of the Cu NPs surface coverage and size is presented. A decrease in photoconversion was observed for wavelengths shorter than ~500 nm, due to the Cu interband absorption. In the low surface coverage limit, where the level of aggregation was found to be low, the surface plasmon resonance absorption dominates leading to a modest effect on the photocurrent response. As the number of aggregates increased with the surface coverage, the photocurrent efficiency also increased, and a maximum enhancement power conversion of 16% was found for a 54 ± 6 NPs per μm(2) PV cell. This enhancement was attributed to SPR light scattering and trapping into the Si PV device. Higher surface coverage yielded numerous aggregates which acted as a bulk coating and caused a decrease in both photocurrent and power measurements.

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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations17
Published2012
Admission routes2
Has abstractyes

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