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Record W4412558746 · doi:10.1021/acsaem.5c00530

One-Step Synthesis of Nitrogen/Sulfur Codoped Graphene/MnO<sub>2</sub> Film as a High-Performance Supercapacitor

2025· article· en· W4412558746 on OpenAlexafffund
Mohammad Javad Raei, Milana Trifkovic, Edward P.L. Roberts, Giovanniantonio Natale

Bibliographic record

VenueACS Applied Energy Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsSupercapacitorGrapheneSulfurMaterials scienceNitrogenNanotechnologyOne-StepChemical engineeringChemistryCapacitanceElectrodeMetallurgyEngineeringOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Supercapacitors have attracted tremendous attention in recent years, but the simultaneous achievement of a high volumetric energy density as well as a simple and scalable synthesis process remains challenging. Herein, a one-step electrochemical exfoliation method is utilized to prepare flexible freestanding graphene films doped and functionalized with nitrogen, sulfur, phosphor, α-MnO 2, and δ-MnO 2 . A solid-state asymmetric supercapacitor is fabricated based on a MnO 2 functionalized graphene film as the positive electrode and a nitrogen- and phosphor-codoped graphene film as the negative electrode, with a wide operating potential window of 1.6 V. The assembled asymmetric supercapacitor achieved volumetric energy and power densities of 17.4 mWh cm –3 and 4.8 W cm –3, respectively, and stable cycling, retaining 98.4% of its capacitance after 10,000 cycles. Our research reveals an approach that paves the way for a straightforward and scalable synthesis process, leading to the production of low-cost asymmetric solid-state supercapacitors.

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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