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Record W2751304872 · doi:10.14447/jnmes.v20i3.325

TiO2-BaTiO3 Composite Films as Photoanode for Dye Sensitized Solar Cell: Effect of BaTiO3 Content

2017· article· en· W2751304872 on OpenAlexvenueno aff
Siti Naqiyah Sadikin, M. Y. A. Rahman, Akrajas Ali Umar

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2017
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceAnataseDye-sensitized solar cellComposite numberSubstrate (aquarium)Spin coatingPorosityPhase (matter)Solar cellChemical engineeringCoatingComposite materialOptoelectronicsChemistryElectrodePhotocatalysis

Abstract

fetched live from OpenAlex

This manuscript reports the use of TiO2-BaTiO3 composite films as a photoanode in dye-sensitized solar cell (DSSC). The influence of BaTiO3 content on the performance parameters of the DSSC has been investigated. The composite has been prepared on ITO glass substrate via sol-gel assisted with spin coating technique. The XRD analysis reveals that the sample is crystalline with the phase of BaTiO3 and anatase TiO2. From the FESEM observation, it was found that the pure sample contains bigger pores compared with the other samples prepared with various BaTiO3 contents. The samples become more compact as the content of BaTiO3 increases. The samples absorb more light in ultraviolet (UV) region than visible region. The area of absorption window varies with BaTiO3 content. The device utilizing the sample with 0 and 6 wt.% BaTiO3 demonstrated the lowest leak current. The device utilizing pure sample produced the highest η of 0.18%. This is due to this device utilized the sample with highest porosity, lowest leak current and charge transfer resistance, Rct.

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 score0.001
Threshold uncertainty score0.002

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 science0.0000.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.264
Teacher spread0.246 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of New Materials for Electrochemical SystemsSame topicTiO2 Photocatalysis and Solar CellsFrench-language works237,207