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Record W4233569701 · doi:10.5383/ijtee.11.01.010

Fe-Cu Compounds in Dye-Sensitized Solar Cells: Influence of Magnetic Field on Mesoporous Structure

2016· article· en· W4233569701 on OpenAlexvenueno aff
Abdul Hai Alami, Di Zhang, Camilia Aokal, Jehad Abed, Ideisan I. Abu-Abdoun, Hussain Alawadhi

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2016
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
Fundersnot available
KeywordsMesoporous materialDye-sensitized solar cellMaterials sciencePhotocurrentSolar cellBand gapElectrodeChemical engineeringLayer (electronics)SemiconductorOptoelectronicsNanotechnologyChemistryCatalysisElectrolytePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This paper investigates the effect of applying a static and dynamic magnetic field in the process of depositing the Fe-Cu compound on the working electrode of a dye-sensitized solar cell (DSSC). Depositing this compound on glass is especially hard due to the unfavorable layer inconsistencies that accompany the utilization of the doctor blade technique and the dissociation of the compound at a temperature of 700˚C, which prevents its ability to be evaporated or sintered beyond that temperature. The Fe-Cu compound is appreciably cheaper, relatively simple to produce and is more absorptive (>81%) in the Vis-NIR than the standard TiO2 mesoporous material normally used for DSSCs. The high diffusion of the Fe into the Cu lattice allows the compound to behave as a semiconductor and is found to have a bandgap of 1.8V. The sensitizer used in the production of a test cell consisted of a Schiff base dye with a compatible bandgap of 1.68 eV and resulted in more generated photocurrent than its TiO2 counterpart, which is a promising result for an alternative mesoporous layer in 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)0.000
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 score0.058
Threshold uncertainty score0.276

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 studies0.0000.000
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 head0.003
GPT teacher head0.169
Teacher spread0.166 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Thermal and Environmental EngineeringSame topicTiO2 Photocatalysis and Solar CellsFrench-language works237,207