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Record W2339687670 · doi:10.1149/ma2014-02/15/849

Electrodeposition of Nanoscale Manganese Oxide for Electrochemical Energy Storage Devices

2014· article· en· W2339687670 on OpenAlexaff
M.P. Clark, Douglas G. Ivey, Wei Qu

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Research Council CanadaUniversity of Alberta
Fundersnot available
KeywordsManganeseMaterials scienceChemical engineeringOxideSupercapacitorNickel oxideScanning electron microscopeNickelInorganic chemistryElectrochemistryNanotechnologyChemistryElectrodeMetallurgyComposite material

Abstract

fetched live from OpenAlex

As society moves away from fossil fuels towards renewable energy sources, energy storage is becoming increasingly important. Manganese oxide is a good candidate material for multiple energy storage applications (e.g., rechargeable batteries and supercapacitors) because it is environmentally benign and has good catalytic and electrochemical properties. When used as a supercapacitor, manganese oxide stores charge through electric double layer and redox effects. To maximize these effects, the structure of manganese oxide deposits needs to be optimized; this means increasing the effective surface area of the deposit. In addition to the effective surface area, the crystal structure of the deposit also greatly affects both catalytic and electrochemical performance. Manganese oxide is polymorphic and it is important to understand the crystallography of the manganese oxide within a device, in order to optimize its properties. In this work, nickel foam is coated with high surface area manganese oxide through a simple anodic electrodeposition procedure. Nickel foam is an excellent substrate for many energy storage devices because in addition to nickel’s good conductivity, the porosity of the foam increases surface area and also facilitates fluid flow through the substrate. The latter is important for applications where manganese oxide is being used as a catalyst. The electrolyte used for deposition of manganese oxide contains manganese acetate, ammonium acetate and dimethyl sulfoxide. By varying deposition and post deposition annealing conditions, different microstructures are obtained. The microstructure, crystallography and chemical state of the manganese oxide deposits are characterized through scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). XPS analysis has been used to determine that manganese oxide is in the form of MnO2. Preliminary XRD and TEM results indicate that the deposits obtained in this work are nanocrystalline with grains less than 20 nm in size. Electrochemical tests such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) are used to determine performance (e.g., capacitance and cyclability) and to optimize the morphology and structure of the manganese oxide deposits. Preliminary CV measurements suggest that the manganese oxide deposits on nickel foam exhibit good areal and specific capacitance.

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 score0.003

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 head0.009
GPT teacher head0.221
Teacher spread0.213 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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