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Record W2178841509 · doi:10.1039/c5dt04082e

Morphology-controlled MnO<sub>2</sub>–graphene oxide–diatomaceous earth 3-dimensional (3D) composites for high-performance supercapacitors

2015· article· en· W2178841509 on OpenAlexaff
Zhong Wen, Min Li, Fei Li, Shi Jin Zhu, Xiaoying Liu, Yuxin Zhang, Tushar Kumeria, Dušan Lošić, Yang Gao, Wei Zhang, Shi He

Bibliographic record

VenueDalton Transactions · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsSupercapacitorMaterials scienceNanosheetGrapheneCapacitanceOxideNanostructureNanowireComposite materialConductivityHorizontal scan ratePorositySpecific surface areaChemical engineeringNanotechnologyElectrodeElectrochemistryMetallurgyChemistry

Abstract

fetched live from OpenAlex

3-Dimensional (3D) composites based on a unique combination of MnO2-nanostructures, graphene oxide nanosheets and porous Diatomaceous Earth (DE) microparticles (GO-DE@MnO2) were synthesized and explored for application in high-performance supercapacitors. To explore the influence of the structural properties of MnO2 nanostructures on supercapacitor performances, several MnO2 structures with nanosheet and nanowire morphologies were synthesized and characterized. The prepared GO-DE@MnO2 composites with MnO2 nanosheets due to their higher conductivity and higher surface area showed a larger specific capacitance of 152.5 F g(-1) and a relatively better cycle stability (83.3% capacitance retention after 2000 cycles at a scan rate of 2 A g(-1)), indicating great potential for application in 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 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)0.000
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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.016
GPT teacher head0.222
Teacher spread0.206 · 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

Citations47
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueDalton TransactionsSame topicSupercapacitor Materials and FabricationFrench-language works237,207