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Record W2808530791 · doi:10.14447/jnmes.v21i2.462

Optical and Electrochemical Properties of PB-ZnO and PB-ZnO/MWCNT Nanocomposite Films Deposited by Chemical Bath

2018· article· en· W2808530791 on OpenAlexvenueno aff
Fatma Özütok, Emin Yakar

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2018
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
Fundersnot available
KeywordsNanocompositeMaterials scienceRaman spectroscopyFourier transform infrared spectroscopyCarbon nanotubeChemical engineeringElectrochemistryCoatingPrussian blueNanotechnologyElectrodeChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The first aim of this study was to investigate the ZnO modification effect on the prussian blue (PB) films. A second aim of the study was to determine the decoration effect of multi walled carbon nanotube (MWCNT) on the PB-ZnO nanocomposite films which could be deposited by chemical bath. The electrochemical behaviour of PB films was systematically studied using ZnO- or ZnO/MWCNT modification. Additionally, detailed optical properties of nanocomposite films were determined by UV-VIS, FTIR and Raman spectroscopy. Optical transparency was severely decreased by ZnO modification onto PB film but the decoration of MWCNTs did not change the optical transparency of film compared to PB film. PB, ZnO and MWCNT presence in the nanocomposite films were proven by FTIR spectrum. Stretching vibration peak of C≡N shifted to short-wavelength for PB-ZnO nanocomposite films and same peak disappeared with MWCNT coating due to the structural distortion. ZnO modification and MWCNT coating affected the electrochemical properties due to the different factors such as OH- group effect in film growth process, Zn2+ substitution with Fe2+ site and interaction between ferricyanide ions and oxygen.

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 score0.002
Threshold uncertainty score0.950

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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