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Record W4403829572 · doi:10.3390/ma17215249

Magnetic CuFe2O4 Spinel–Polypyrrole Pseudocapacitive Composites for Energy Storage

2024· article· en· W4403829572 on OpenAlexafffund
Mahmoud Awad, Igor Zhitomirsky

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolypyrroleMaterials scienceComposite materialEnergy storageSpinelPolymerMetallurgyPolymerization

Abstract

fetched live from OpenAlex

This investigation focused on the fabrication of ceramic ferrimagnetic CuFe2O4–conductive polypyrrole (PPy) composites for energy storage. CuFe2O4 with a crystal size of 20–30 nm and saturation magnetization of 31.4 emu g−1 was prepared by hydrothermal synthesis, and PPy was prepared by chemical polymerization. High-active-mass composite electrodes were fabricated for energy storage in supercapacitors for operation in a sodium sulfate electrolyte. The addition of PPy to CuFe2O4 resulted in a decrease in charge transfer resistance and an increase in capacitance in the range from 1.20 F cm−2 (31 F g−1) to 4.52 F cm−2 (117.4 F g−1) at a 1 mV s−1 sweep rate and from 1.17 F cm−2 (29.9 F g−1) to 4.60 F cm−2 (120.1 F g−1) at a 3 mA cm−2 current density. The composites showed higher capacitance than other magnetic ceramic composites of the same mass containing PPy in the same potential range and exhibited improved cyclic stability. The magnetic behavior of the composites was influenced by the magnetic properties of ferrimagnetic CuFe2O4 and paramagnetic PPy. The composites showed a valuable combination of capacitive and magnetic properties and enriched materials science of magnetic supercapacitors for novel applications based on magnetoelectric and magnetocapacitive properties.

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 categoriesInsufficient payload (model declined to judge)
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 score0.020
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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