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Record W2242520068 · doi:10.14447/jnmes.v16i3.20

Electrodeposition of ZnO for Application in Dye-sensitized Solar Cells

2013· article· en· W2242520068 on OpenAlexvenueno aff
Francisco Iván Lizama-Tzec, M. A. Aguilar Frutis, Geonel Rodríguez‐Gattorno, Gerko Oskam

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2013
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDye-sensitized solar cellAmorphous solidCurrent densityPolyethylene glycolChemical engineeringCyclic voltammetryPEG ratioSolar cellPlating (geology)ElectrochemistryChemistryElectrodeElectrolyteOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

ZnO has been electrodeposited from 0.5 M Zn(NO3)2 at pH 4.5 with and without polyethylene glycol (PEG) as additive. Cyclic voltammetry on FTO substrates reveals two electrochemical regimes, where reduction of nitrate and water are rate determining, respectively. ZnO films were galvanostatically electrodeposited as a function of the deposition current density: at low current densities, where nitrate reduction is rate determining, highly crystalline ZnO films were obtained, whereas amorphous ZnO films were obtained at higher current densities, where water reduction dominates. The amorphous films transform to crystalline ZnO upon sintering, and SEM images show that the presence of PEG results in a homogeneous film morphology. The films were used for the fabrication of dye-sensitized solar cells (DSSCs), resulting in solar cell conversion efficiencies of up to 1.4% for non-sintered ZnO films deposited at low current density (without PEG), while the best cells were obtained with films electrodeposited from the plating bath with 0.15 mM PEG with efficiencies of up to 1.8% for sintered films prepared at higher current density. These results illustrate that the presence of PEG in the plating bath optimizes the film morphology and, hence, the performance of ZnO-based 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

Explore more

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