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Nucleation and growth of electrodeposited Mn oxide rods for supercapacitor electrodes

2015· article· en· W2268002218 on OpenAlexaff
M.P. Clark, Douglas G. Ivey

Bibliographic record

VenueNanotechnology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNucleationMaterials scienceRodAmorphous solidOxideDeposition (geology)Transmission electron microscopySpinelCrystallographyChemical engineeringNanotechnologyMetallurgyChemistry

Abstract

fetched live from OpenAlex

The nucleation and growth of electrodeposited Mn oxide rods has been investigated by preparing deposits on Au coated Si at varying deposition times between 0.5 s and 10 min. The deposits were investigated using high resolution scanning and transmission electron microscopy. A model for the nucleation and growth of Mn oxide rods has been proposed. Nucleation begins as thin sheets along Au grain boundaries and triple points. As these nucleation sites are consumed, nucleation spreads across the grains. Nucleation of sheets in close proximity causes agglomeration and the formation of rounded particles. Some of these rounded particles then accelerate in growth, initially in all directions and then primarily in the direction normal to the sample surface. Accelerated growth normal to the sample surface leads to the formation of rods. As rods grow, the growth of other particles accelerates and they become rods themselves. Eventually the entire sample surface is covered with rods 15-20 μm long and about 2 μm wide. The sheet-like morphology of the deposits is retained at all stages of deposition. Electron diffraction analysis of 3 s and 6 s deposits shows that the sheets are initially amorphous and then begin to crystallize into a cubic spinel Mn3O4 crystal structure. High resolution imaging of the 6 s sample shows small crystalline regions (∼5 nm in size) within an amorphous matrix.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.011
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

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 head0.017
GPT teacher head0.236
Teacher spread0.220 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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