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Record W2231942804 · doi:10.5539/mas.v9n13p133

Correlation between Theoretical and Experimental Specific Surface Area Estimation for PANI and PANI (Zr) Composite

2015· article· en· W2231942804 on OpenAlexvenueno aff
Tatiana N. Myasoedova, Nina K. Plugotarenko, Tatiana A. Moiseeva, Eugeniy V. Vorobyev, Vera V. Butova, Виктор Владимирович Петров

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
FundersHelmholtz-Zentrum Berlin für Materialien und EnergieSouthern Federal University
KeywordsPolyanilineZirconiumMaterials scienceComposite numberDopantPolymerizationSpecific surface areaFiberPolyaniline nanofibersChemical engineeringAnalytical Chemistry (journal)Polymer chemistryComposite materialChemistryPolymerDopingOrganic chemistry

Abstract

fetched live from OpenAlex

Polyaniline (PANI) and polyaniline/Zr (PANI/Zr) powders have been prepared by in situ polymerization method, and the morphology, structure and pore structure are investigated. Quantum-chemical calculations showed that the polyaniline structure formed with zirconium ions is not globular, but fiber-like, and its fiber patterns are characterized by clear orientation of individual fragments Estimation of the theoretical and experimental specific surface area of the PANI/Zr composite is done. It is shown that the most preferred is the fiber-like structure with the specific surface area about 128-162 m2/g, which stability may be improved by using zirconium (IV) containing substances as a dopant. Experimental specific surface area is 66.8 and 146.05 m2/g for PANI and PANI/Zr, respectively. Correlation between theoretical and experimental specific surface area PANI/Zr composite is observed.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.291
Teacher spread0.245 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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