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Record W2315835392 · doi:10.1504/ijpse.2015.075109

Absorbance and thermal diffusion characteristics of charged ion in organic photovoltaic solar cell

2015· article· en· W2315835392 on OpenAlexaff
Manasseh B. Shitta, Emmanuel O. Ogedengbe, Marc A. Rosen

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsOrganic solar cellSolar cellPhotovoltaic systemMimosa pudicaMaterials scienceDiffusionChemistryAnalytical Chemistry (journal)ThermodynamicsOptoelectronicsChromatographyPhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

An M.pudica-based organic solar cell model is developed for experimental investigation of UV light absorptivity within ethyl acetate and ethanol mixtures in order to predict charged ion mobility within a network of microchannels. A two-dimensional simulation of energy transport with a finite volume formulation is adopted, where the back field effect on the wafering technology and the optimisation of the energy conversion within the M.pudica-based organic solar cell can be investigated. A significant contribution of this study is the inclusion of the material characterisation of M.pudica as a potential source of energy supply. The developed model is proposed for performance improvement in the design of the organic solar cell. With a variety of imposed boundary conditions, the scalar transport variables and their responses to the environment are studied. The temperature specified on the faces with Dirichlet boundary condition is 100°C. The result of the surface temperature diffusion is presented. The temperature that diffuses into the cell layer ranges from 30°C to 77°C, suggesting a temperature range for manufacturing of the organic solar cell.

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 score0.003
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.195
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

Citations3
Published2015
Admission routes1
Has abstractyes

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