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Record W2033412982 · doi:10.1149/05825.0067ecst

The Development of Pseudocapacitive Molybdenum Oxynitride Electrodes for Supercapacitors

2014· article· en· W2033412982 on OpenAlexafffund
Haoran Wu, Keryn Lian

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
FundersMcMaster University
KeywordsMaterials scienceSupercapacitorElectrodeOxideElectrochemistryMolybdenumX-ray photoelectron spectroscopySilicon oxynitrideChemical engineeringSubstrate (aquarium)NanotechnologyLayer (electronics)MetallurgyChemistry

Abstract

fetched live from OpenAlex

Mo oxynitride was developed as pseudocapacitive electrode material for supercapacitors. A thin film Mo oxynitride was produced by first electrodeposition of Mo oxide on a Ti substrate followed by a low-temperature (400 oC) heat treatment in N2 environment. XPS analyses showed that the surface of the Mo oxynitride film was composed of a mixture of MoO3, MoO2 and less than 20 at.% Mo2N. However, the electrochemical behavior of the Mo oxynitride film was significantly different from that of Mo oxide but much similar to the behavior of pure Mo2N, a known pseudocapacitive material. The cycle life and stability of the Mo oxynitride were much improved over the Mo oxide. A two-electrode symmetric cell using the developed Mo oxynitride electrodes was demonstrated and showed high rate performance. An asymmetric cell using a Mo oxynitride as negative electrode and a carbon as positive electrode was also established and showed an extended voltage window and, thus, an increased energy density.

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 score0.001

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.239
Teacher spread0.223 · 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

Citations66
Published2014
Admission routes2
Has abstractyes

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