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Record W2899375345 · doi:10.1039/c8ra07071g

Light-soaking free organic photovoltaic devices with sol–gel deposited ZnO and AZO electron transport layers

2018· article· en· W2899375345 on OpenAlexafffund
Zenan Jiang, S. Soltanian, Bobak Gholamkhass, Abdullah Aljaafari, Peyman Servati

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaDeanship of Scientific Research, King Faisal UniversityKing Faisal UniversityCanada Foundation for Innovation
KeywordsMaterials scienceDopingPhotovoltaic systemLayer (electronics)ZincOptoelectronicsEnergy conversion efficiencyIrradiationChemical engineeringAluminiumNanotechnologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

This paper investigates light-soaking effects in inverted organic photovoltaic (OPV) devices with zinc oxide (ZnO) and aluminum doped ZnO (AZO) electron transport layers (ETL), which is important for the development of low-cost and stable solar cells. The samples demonstrated high solar harvesting properties with power conversion efficiency up to 3.9%. Air-stability tests of up to 150 days were performed on devices with different Al doping levels. The devices maintained higher than 60% of the initial PCE after 50 days of open-air exposure. The light-soaking mechanism was investigated with experiments and simulations and shown to be eliminated when the Al fraction of the AZO is higher than 4%. The simulated band diagram of the OPV devices indicates that the low carrier density in the ZnO layer by virtue of depletion is the main reason of the light-soaking effect. Doping the ZnO layer as well as exposing the devices under UV irradiation will introduce additional free carriers into the ETL and reduce the width of the depletion region at both sides of the ETL.

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 score0.043
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 integrity0.0000.000
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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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

Citations42
Published2018
Admission routes2
Has abstractyes

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