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Record W2733641805 · doi:10.1002/aelm.201700115

On the Relationship Between Donor/Acceptor Interface Energy Levels and Open‐Circuit Voltages

2017· article· en· W2733641805 on OpenAlexafffund
Peicheng Li, Weiji Hong, Yiying Li, Grayson L. Ingram, Zheng‐Hong Lu

Bibliographic record

VenueAdvanced Electronic Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsHeterojunctionOpen-circuit voltageAcceptorMaterials scienceOffset (computer science)PlanarOptoelectronicsVoltagePhotoemission spectroscopyWork functionPhotovoltaic systemBand offsetUltravioletAnalytical Chemistry (journal)NanotechnologyCondensed matter physicsX-ray photoelectron spectroscopyChemistryElectrical engineeringPhysicsComputer scienceBand gapNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Energy offset ( E DA ) from a number of donor/acceptor heterojunctions is measured using ultraviolet photoemission spectroscopy. It is found that substrate work functions have little impact on the energy level alignments at donor/acceptor heterojunctions. Planar‐heterojunction organic photovoltaic cells are made to test the relationship between energy offset and open‐circuit voltage ( V OC ). V OC is found to increase linearly as a function of E DA . The V OC , however, takes a surprising turn at E DA = 1.5 eV and starts to decrease as a function of donor–acceptor energy levels. To explain this experimental observation, a theoretical model to quantify the relationship between V OC and E DA is developed. The proposed model well explains the experimental data and, in particular, the reverse trend of V OC on E DA . By grouping several material constants into one variable, a simple universal plot that well describes the experimental data for both planar‐heterojunction and bulk‐heterojunction cells is generated.

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 score0.344
Threshold uncertainty score0.812

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.032
GPT teacher head0.276
Teacher spread0.245 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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