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Record W2050631715 · doi:10.1002/cjce.20015

Theoretical modelling of the electrode thickness effect on maximum power point of dye‐sensitized solar cell

2008· article· en· W2050631715 on OpenAlexvenueno aff
Meng Ni, Michael K.H. Leung, Dennis Y.C. Leung

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
Fundersnot available
KeywordsDye-sensitized solar cellSolar cellMaximum power principleMaterials scienceEnergy conversion efficiencyPower (physics)Open-circuit voltageOptoelectronicsElectrodeShort circuitParametric statisticsVoltageCurrent (fluid)DiffusionPhotovoltaic systemElectrical engineeringPhysicsMathematicsEngineeringElectrolyteThermodynamics

Abstract

fetched live from OpenAlex

Abstract The maximum power point (MPP) of a dye‐sensitized solar cell (DSSC) is often more important than the open‐circuit voltage and the short‐circuit current as MPP better represents the DSSC power output and energy conversion efficiency. In this investigation, the DSSC J–V characteristics and MPP were studied using a simple theoretical electron diffusion model. Parametric analyses were performed to determine the particular effect of electrode thickness on the MPP output. The analytical results are well consistent with the experimental results published in the literature. In the optimization analysis, it was specially found that the optimal electrode thickness for the highest MPP is rather insensitive to the operating conditions. It implies that an optimally designed DSSC can be always operated at its highest MPP regardless of any geographical, seasonal, and solar hour factors. Such an important attribute facilitates the design and manufacture of DSSC for worldwide commercialization at competitive costs.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.159
Teacher spread0.154 · 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 designSimulation or modeling
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

Citations69
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicTiO2 Photocatalysis and Solar CellsFrench-language works237,207