MétaCan
Menu
Back to cohort
Record W2331165730 · doi:10.1021/jp2049684

Supercapacitive Properties of Hydrothermally Synthesized Co<sub>3</sub>O<sub>4</sub> Nanostructures

2011· article· en· W2331165730 on OpenAlexafffund
Huatao Wang, Li Zhang, Xuehai Tan, Chris Holt, Beniamin Zahiri, Brian C. Olsen, David Mitlin

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureMesoporous materialNanostructureCrystalliteHydrothermal circulationChemical engineeringPorosityOxideMass fractionYield (engineering)NanotechnologyComposite materialMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A hydrothermal process was employed to create a variety of Co 3 O 4 nanostructures. We demonstrate that nominally minor differences in the synthesis temperature (50, 70°, or 90 °C) yield profound variations in the oxide microstructure, with lath-like, necklace-like and net-like morphologies of different scales resulting. This in turn resulted in significant variations in the supercapacitive performance that ranged from mediocre to superb. Specifically, the mesoporous net-like Co 3 O 4 nanostructures that were synthesized at 50 °C exhibited very favorable electrochemical properties: The net-like Co 3 O 4 had a specific capacitance of 1090 F/g at a mass loading of 1.4 mg/cm 2 . At this high mass loading, such performance has not been previously reported. SEM and TEM analysis of these samples revealed an interconnected array of sub-10 nm crystallites interspersed with a high volume fraction of similar scale pores. The poorer performing microstructures were both coarser and much less porous.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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

Citations189
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicSupercapacitor Materials and FabricationFrench-language works237,207