MétaCan
Menu
Back to cohort
Record W4220944410 · doi:10.1021/acsami.1c22276

Dry Synthesis of Binder-Free Ruthenium Nitride-Coated Carbon Nanotubes as a Flexible Supercapacitor Electrode

2022· article· en· W4220944410 on OpenAlexafffund
Hanie Kazari, Elmira Pajootan, Pascal Hubert, Sylvain Coulombe

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSupercapacitorElectrodeCapacitanceCarbon nanotubeCoatingSubstrate (aquarium)Chemical engineeringNanotechnologyNitrideFabricationRutheniumElectrochemistryComposite materialLayer (electronics)CatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Ruthenium nitride was successfully deposited on a multiwalled carbon nanotube (MWCNT) forest grown on a stainless-steel mesh substrate by radiofrequency plasma-assisted pulsed laser deposition. This novel dry fabrication method for flexible supercapacitor electrodes eliminates toxic byproducts and the need for any binder component. Experimental results show a successful thin film coating of the individual MWCNTs with RuN x under various synthesis conditions. The electrochemical characterization demonstrates a significant improvement in capacitance of the synthesized RuN x –MWCNT electrode compared to the bare MWCNT forest, with a large potential window of 1.2 V. Capacitance values as high as 818.2 F g –1 (37.9 mF cm –2 ) have been achieved.

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Materials & InterfacesSame topicSupercapacitor Materials and FabricationFrench-language works237,207