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“Internal” Resistivity and Quantum Efficiency in Organic/Hybrid Solar Cells

2012· article· en· W1999933758 on OpenAlexaff
H.L. Kwok

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsElectrical resistivity and conductivityMaterials scienceQuantum efficiencyContext (archaeology)Organic solar cellQuantumWork (physics)OptoelectronicsEngineering physicsCondensed matter physicsComposite materialPhysicsMechanical engineeringElectrical engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Quantum efficiency of organic/hybrid solar cells has improved appreciably in recent years and it is useful to re-examine those parameters that reflect the device properties. This is important when there is need to distinguish between improvements associated with materials properties such as domain size and phase separations and improvements linked to external effect such as the inclusion of field enhancement layers. In this work, we reported the evaluation of the “internal” resistivity found in high performance organic/hybrid solar cells based on data reported in the literature. Our observations suggest that in general better device performance is found in devices with higher “internal” resistivity. This includes the case when a hole blocking layer is added. Exceptions to such a rule can be found in solar cells with nanowires in the n-layer and ferroelectric end layers whereby the quantum efficiencies increase beyond the values expected. A simple mathematical model has been put forward to explain the dependence of quantum efficiency on the “internal” resistivity. Overall, lowering of the “internal” resistivity correlates well with degradation in the device performance and can be put in the context of a reduction in the effective diffusion length of the photo-excited carriers. High field and polarization effects by themselves do not affect the “Internal” resistivity.

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)0.001
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 score0.052
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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