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Record W4402370029 · doi:10.3390/jcs8090351

Pseudocapacitive and Magnetic Properties of SrFe12O19–Polypyrrole Composites

2024· article· en· W4402370029 on OpenAlexafffund
Michael MacDonald, Igor Zhitomirsky

Bibliographic record

VenueJournal of Composites Science · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolypyrroleComposite materialMaterials sciencePolymerizationPolymer

Abstract

fetched live from OpenAlex

This study is inspired by the importance of advanced composites, combining spontaneous magnetization with electrical charge storage properties. It is focused on the investigation of magnetically hard SrFe12O19 (SFO) material and its composites with polypyrrole (PPy). For the first time, an organic surfactant–charge transfer mediator and high-energy ball milling (HEBM) were applied to the preparation of high-active-mass SFO composite electrodes. An important finding was the ability to achieve enhanced capacitance of SFO and its composites in a negative range of electrode potentials in an electrolyte. The benefits of the sodium sulfate electrolyte and the charge storage mechanism are discussed. Another important finding was the synergy of the properties of SFO and PPy, which allowed the preparation of highly capacitive conductive composites. The effects of HEBM and the SFO content in the composites on the capacitive properties were studied. Magnetic measurements revealed the effect of HEBM on the magnetic properties and demonstrated good magnetic properties of the composites, which also exhibited advanced capacitive properties. The composites were utilized for the manufacturing of an asymmetric device, which exhibited high capacitive properties at an applied voltage of 1.5 V.

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 score0.011
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.240
Teacher spread0.227 · 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
Published2024
Admission routes2
Has abstractyes

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