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Record W2964658775 · doi:10.11159/iccpe19.122

Free-Standing S-CNT-rGO Nanocomposite Paper Cathodes for Li-S Batteries

2019· article· en· W2964658775 on OpenAlexvenueno aff
Büşra Şahin, Hilal Köse, Şeyma Dombaycıoğlu, Ali Osman Aydın

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsNanocompositeCathodeMaterials scienceNanotechnologyCarbon nanotubeNanoparticleGrapheneComposite materialElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

For electric vehicles (EVs), hybrid electric vehicles (HEVs), and smart electric grids, it is important to develop advanced energy storage systems due to the ever-increasing demands for high energy density and long-life energy sources.With a high theoretical gravimetric energy density of 2500 Wh kg -1 , Lithium-sulfur (Li-S) batteries are considered to be one of the most promising candidates in this respect [1].However, the practical electrochemical performance of Li-S battery have been restricted by the low conductivity of sulfur and the insoluble and insulating lithium sulfides, Li2S2/Li2S on the cathode surface [2].To resolve these problems, especially carbon materials are used to increase the conductivity.Due to their high electrical conductivity, porous carbon, carbon nanotubes (CNTs) and reduced graphene oxide (rGO) are generally used to enhance the conductivity of sulfur and Li2S / Li2S2 during charge-discharge process [3].In this work, we produced S-CNT-rGO nanocomposites as binderless free-standing paper.Firstly, functionalized MWCNTs were prepared with H2SO4/HNO3 mixture (3:1, v/v) and graphite oxide was produced by modified Hummers method.Then, Na2S2O3.5H2Oprecursor was added into the mixture of graphite oxide and CNTs.After ultrasonication of 2h, HCl solution was added dropwise to resulting homogeneous suspension.During this process, the sulfur anions reduced and deposited on the surface of the graphene oxide and CNTs as sulfur nanoparticles.The resulting suspension was filtered through a 0.22 µm porous PVDF membrane filter (Millipore, Durapore Membrane) with a vacuum filtration system and washed three times with deionized water and ethanol.After the paper dried, it was peeled off the membrane and flexible S-CNT-GO paper was obtained.To obtain S-CNT-rGO from this structure, the S-CNT-GO paper was treated with a dilute solution of hydrazine as reducing agent.Obtained papers were characterized by field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectrometer (EDS), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) analyses.Electrochemical analysis was performed using battery tester device.Charge-discharge capabilities, specific capacity and capacity retention parameters were investigated of the cathode electrodes assembled in the type of CR2032 cells.When the results have been evaluated, it has seen that the aimed structure of S-CNT-rGO has been obtained for advanced Li battery applications.

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 score0.005

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 science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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