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Record W2991368679 · doi:10.5539/ijc.v12n1p49

Optical and Photoelectrochemical Investigation of Mixed Photoactive Poly 2,2’,5,2’’ ter-thiophene and Poly 2,2 bithiophene. Role of Intermixed Phases Created By the Co-electro-polymerization Process

2019· article· en· W2991368679 on OpenAlexvenueno aff
Kasem K. Kasem, Monica Schultz, Sarah H. Osman

Bibliographic record

VenueInternational Journal of Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMonomerDielectric spectroscopyPhotocurrentPolymerizationTerthiopheneChemical engineeringThiophenePolymer chemistryPolymerElectrochemistryElectrodePhysical chemistryOrganic chemistryMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

Fluorine-doped tin oxide (FTO) electrodes modified with polymeric films of poly 2,2 bithiophene (PBth) and/or poly 2,2’,5,2’’-terthiophene (PTerth) were subjected to optical, photoelectrochemical (PEC), and electrochemical impedance spectroscopy (EIS) studies. Electropolymerization of mixed monomers containing bi-thiophene (Bth) and ter-thiophene (Terth) with different ratios resulted in the formation of intermixed phases. The recorded optical and PEC and EIS outcome data show that these intermixed polymer networks do not follow a monotonic relationship with the monomer ratios used to generate them. Optical studies indicate the formation of indirect and direct band gaps in the intermixed phases. Films generated in mixed monomers have greater energy-band tails than those generated from pure monomers. PEC studies indicated that these intermixed phases possess p-p type hole accumulations, evident from the initial sharp rise in photocurrent. EIS results did not support linear relationship between the percent of Bth in monomer mixture and the dielectric-related properties such as barrier energy Wm, hopping frequency (ω hopping), electrical conductivity (σ), and density of state at Fermi level N (EF).

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 score0.001
Threshold uncertainty score0.408

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 head0.005
GPT teacher head0.247
Teacher spread0.242 · 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
Published2019
Admission routes1
Has abstractyes

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