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Record W4293214649 · doi:10.32604/fdmp.2022.022091

Performance Enhancement of Dye-Sensitized Solar Cells: Solvation Model

2022· article· en· W4293214649 on OpenAlexafffund
Adel Daoud, Ali Cheknane, Jean‐Michel Nunzi, Afak Meftah

Bibliographic record

VenueFluid dynamics & materials processing · 2022
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolvationDye-sensitized solar cellDensity functional theoryPhotochemistrySolubilityAbsorption spectroscopySolventElectron transferAtomic orbitalMerocyanineAbsorption (acoustics)ChemistryMaterials scienceAnalytical Chemistry (journal)Chemical physicsElectronPhysical chemistryComputational chemistryOpticsOrganic chemistryPhysicsElectrolyte

Abstract

fetched live from OpenAlex

A solubility model for Merocyanine-540 dye together with the interface's electron transfer kinetics of MC-540/TiO2 has been investigated (Merocyanine 540-based dye has been used effectively in dye-sensitized solar cells). The highest absorption peaks were recorded at 489 nm and 493 nm in Water and Ethanol solvent, versus the vacuum phase which yielded 495 nm (associated with a modest electron injection-free energy value (ΔGinj) of -2.34 eV for both Water and Ethanol solvents). The time-dependent density functional theory (TD-DFT) method approach has been applied in this simulation. Additionally, the electronic structure and simulated UV-Vis spectra of the dye in different solvents have been determined, and the alignment with the solar spectrum has been discussed to a certain extent. The energy level diagrams and electron density of the primary molecular orbitals are shown, and the major issues that have an impact on our new interface's performance are examined. It is concluded that the proposed Solvation Model (SM) can improve the performance of Dye-Sensitized Solar Cells.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.940

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 head0.010
GPT teacher head0.214
Teacher spread0.205 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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