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Record W2560130210 · doi:10.1021/acsenergylett.6b00612

Graphene Oxide Shells on Plasmonic Nanostructures Lead to High-Performance Photovoltaics: A Model Study Based on Dye-Sensitized Solar Cells

2016· article· en· W2560130210 on OpenAlexaff
Yoon Hee Jang, Adila Rani, Li Na Quan, Valerio Adinolfi, Pongsakorn Kanjanaboos, Olivier Ouellette, Taehwang Son, Yu Jin Jang, Kyungwha Chung, Hannah Kwon, Donghyun Kim, Dong Ha Kim, Edward H. Sargent

Bibliographic record

VenueACS Energy Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsMaterials sciencePhotocurrentGraphenePlasmonDye-sensitized solar cellPhotovoltaicsNanotechnologyEnergy conversion efficiencyOptoelectronicsPlasmonic solar cellPhotovoltaic systemSolar cellNanoparticlePlasmonic nanoparticlesHybrid solar cellNanostructurePolymer solar cellElectrodeChemistryElectrolyte

Abstract

fetched live from OpenAlex

The incorporation of plasmonic nanoparticles (NPs) into photovoltaic devices can increase light absorption and in turn improve solar cell performance. The graphene oxide-encapsulated gold NPs (Au@GO NPs) are designed and incorporated into photoanodes to demonstrate plasmonic dye-sensitized solar cells. The coupling between GO and the Au NPs has the beneficial effect of extending solar spectral utilization in the long-wavelength portion of the visible spectrum. In addition, GO encapsulation reduces charge recombination on the surface of the NPs and facilitates improved charge transport. As a result, champion devices with plasmonic photoanodes containing Au@GO NPs deliver a power conversion efficiency that reaches 9.1%. This corresponds to an enhancement in photocurrent and power conversion efficiency of 19 and 17%, respectively, compared to control devices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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