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Record W2316041930 · doi:10.1021/ac401084x

Interfacial Micropore Defect Formation in PEDOT:PSS-Si Hybrid Solar Cells Probed by TOF-SIMS 3D Chemical Imaging

2013· article· en· W2316041930 on OpenAlexafffund
Joseph P. Thomas, Liyan Zhao, Marwa Abd‐Ellah, Nina F. Heinig, K. T. Leung

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPEDOT:PSSSecondary ion mass spectrometryChemistrySputteringMicroporous materialPolymerFabricationLayer (electronics)Solar cellNanotechnologyOptoelectronicsChemical engineeringIonMaterials scienceThin film

Abstract

fetched live from OpenAlex

Conducting p-type polymer layers on n-type Si have been widely studied for the fabrication of cost-effective hybrid solar cells. In this work, time-of-flight secondary ion mass spectrometry (TOF-SIMS) is used to provide three-dimensional chemical imaging of the interface between poly(3,4-ethylene-dioxythiophene):polystyrenesulfonate (PEDOT:PSS) and SiOx/Si in a hybrid solar cell. To minimize structural damage to the polymer layer, an Ar cluster sputtering source is used for depth profiling. The present result shows the formation of micropore defects in the interface region of the PEDOT:PSS layer on the SiOx/Si substrate. This interfacial micropore defect formation becomes more prominent with increasing thickness of the native oxide layer, which is a key device parameter that greatly affects the hybrid solar cell performance. Three-dimensional chemical imaging coupled with Ar cluster ion sputtering has therefore been demonstrated as an emerging technique for probing the interface of this and other polymer-inorganic systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 integrity0.0000.000
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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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 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

Citations40
Published2013
Admission routes2
Has abstractyes

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