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Record W2469197693 · doi:10.1063/1.4954902

PEDOT:PSS with embedded TiO2 nanoparticles as light trapping electrode for organic photovoltaics

2016· article· en· W2469197693 on OpenAlexafffund
Yoonseok Park, Lars Müller‐Meskamp, Koen Vandewal, Karl Leo

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsCanadian Institute for Advanced Research
FundersBundesministerium für Bildung und ForschungCanadian Institute for Advanced Research
KeywordsPEDOT:PSSMaterials scienceOptoelectronicsIndium tin oxideElectrodeOrganic solar cellNanoparticleLight scatteringAnodeLayer (electronics)Photoactive layerPhotovoltaicsEnergy conversion efficiencyThin filmScatteringNanotechnologyPhotovoltaic systemPolymer solar cellOpticsComposite materialPolymer

Abstract

fetched live from OpenAlex

The performance of organic optoelectronic devices can be improved by employing a suitable optical cavity design beyond the standard plane layer approach, e.g., by the inclusion of periodically or randomly textured structures which increase light incoupling or extraction. One of the simplest approaches is to add an additional layer containing light scattering particles into the device stack. Solution processed poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) thin films are promising for replacing the brittle and expensive indium tin oxide transparent electrode. We use a blend of 100 nm TiO2 scattering particles in PEDOT:PSS solution to fabricate transparent electrode films which also functions as a scattering layer. When utilized in an organic photovoltaic device, a power conversion efficiency of 7.92% is achieved, which is an 8.6% relative improvement compared to a device with a neat PEDOT:PSS electrode without the nanoparticles. This improvement is caused by an increase in short-circuit current due to an improved photon harvesting in the 320 nm–700 nm spectral wavelength range.

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 score0.026
Threshold uncertainty score0.548

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 head0.012
GPT teacher head0.225
Teacher spread0.214 · 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

Citations33
Published2016
Admission routes2
Has abstractyes

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