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Record W3099551166 · doi:10.20964/2020.12.68

Synthesis of Polypyrrole-coated NiO/Ni(OH)2 Hybrid Flowers Composite by Pulse Electro-polymerization for supercapacitor with Improved Electrochemical Capacitance

2020· article· en· W3099551166 on OpenAlexaff
Ji Fang Yan, Serubbabel Sy, Heng Wang, Yongcai Zhang, Lizhen Wang, Liming Zhou, Aiping Yu

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSupercapacitorPolypyrroleNon-blocking I/OCapacitanceMaterials scienceElectrochemistryPolymerizationComposite numberChemical engineeringComposite materialChemistryElectrodePolymerOrganic chemistryPhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

This work describes a novel strategy to synthesize polypyrrole-coated NiO/α-Ni(OH) 2 hybrid flower with an improved capacitance performance for supercapacitor. Polypyrrole nanoparticles with a size of 80-100 nm were prepared by a pulse electro-polymerization and uniformly embedded into the petal gap of the self-assembled NiO/α-Ni(OH) 2 hybrid flower. Due to the enhancement of the electrical conductivity and the improvement of electrochemical pseudo-capacitance, the as-synthesized polypyrrole-coated NiO/α-Ni(OH) 2 hybrid flower composite showed a high reversible specific capacity of 359 F g -1 at 5 A g -1 in 2 M KOH electrolyte in a potential range of 0 - 0.45 V. Benefiting from the unique electro-polymerization design, the obtained hybrid flower composite demonstrated excellent capacitive performance and cycling stability after 1000 cycles, demonstrating the promising application as a high-performance pseudocapacitive material for supercapacitors.

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)0.001
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 score0.039
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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