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Record W4293067974 · doi:10.1149/2754-2734/ac8ab0

MoS<sub>2</sub>-Graphene Composite Electrode for High Energy Hybrid Li-Ion Capacitors

2022· article· en· W4293067974 on OpenAlexafffund
Sanoop Palakkathodi Kammampata, Mohammed Hussain Abdul Jabbar, Akhil Mammoottil Abraham, Cenk Gümeci, Nilesh Dale, Yoshihisa Furuya, Venkataraman Thangadurai

Bibliographic record

VenueECS Advances · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Calgary
FundersMitacs
KeywordsMaterials scienceElectrodeGrapheneComposite numberScanning electron microscopeBattery (electricity)Power densityCapacitorComposite materialChemical engineeringNanotechnologyElectrical engineeringChemistryPower (physics)Voltage

Abstract

fetched live from OpenAlex

A hybrid Li-ion capacitor represents an emerging class of devices, which results from the coupling of high energy density battery-type electrode materials at one side and high-power EDLC electrode at other side. Here, we develop a simple and scalable method including ball-milling, followed by heating process to synthesize MoS 2 /graphene composite material. The structural and morphological analyses were carried out by powder X-ray diffraction (PXRD) analysis and scanning electron microscopy (SEM) technique. The composite electrode delivers high specific capacity (725 mAh g −1 at 0.1 A g −1 and 265 mAh g −1 at 5 A g −1 ). The hybrid device composed of MoS 2 /graphene composite electrode as negative electrode and commercial activated carbon as the positive electrode exhibits a high energy density of 117 Wh kg −1 at 200 W kg −1 and a maximum power density of 3.9 kW kg −1 at 79 Wh kg −1 . The hybrid device showed a long cycle stable Li storage capacity (62% after 5000 cycles at 1 A g −1 ).

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 categoriesMeta-epidemiology (narrow)
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 score0.206
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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