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Record W2278699103 · doi:10.1149/ma2014-04/3/579

Study of Morphological Changes in the Cathode Electrodes of Lithium-Sulfur Batteries

2014· article· en· W2278699103 on OpenAlexaff
Hugues Marceau, Daniel Clément, Catherine Gagnon, Pierre Hovington, Chisu Kim, Abdelbast Guerfi, Mohamed Chaker, Karim Zaghib

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSolid-state spectroscopy and crystallography
Canadian institutionsInstitut National de la Recherche ScientifiqueHydro-Québec
Fundersnot available
KeywordsCathodeScanning electron microscopeBattery (electricity)SulfurMaterials scienceComposite numberElectrodeLithium (medication)CoatingNanotechnologyChemical engineeringComposite materialChemistryMetallurgyPhysics

Abstract

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Lithium-sulfur (Li-S) battery is a promising candidate for the next-generation batteries for electric vehicles due to its high theoretical specific energy and the low cost of sulfur. However, the challenges in the battery performance like low volumetric energy density, poor cycle life, and high self-discharge rate are yet to be resolved. To achieve the better understanding on the root causes behind the numerous technical challenges and to provide the right directions to solve the problems, the development of characterization techniques to understand the reaction mechanisms and the phenomena inside the Li-S cells are highly required. In this study, the morphological changes of S-LVO (LiV3O8) composite were analysed to investigate the reason why S-LVO composite shows better battery performance over the pristine sulfur material [1]. Experimental S-LVO composite was manufactured by a mechano-fusion process using a Nobilta equipment to form the LVO coating on sulfur particles as shown in Figure 1.To analyse the cathode electrode, lithium // Celgard 3501 / LiTFSI 1M in DME/DOL (1:1) // sulfur coin cells were prepared and opened at various values of death of discharge (DOD) in the first cycle. The cathodes were rinsed and dried (at 0.65 atm, 55°C for 1h), then observed in the scanning electron microscope (SEM) using a specially designed transfer chamber to avoid any air/moisture contact between the SEM and the glove box. Secondary electrons (SE) images, backscattered electron (BSE) images and comparative X-ray elemental analysis using an energy dispersive spectrometer (EDS) were performed at 15 keV. Results and discussion Figure 2 shows the discharge profile in the 3rd cycle at 0.1C condition and the cycle performance at 0.5C condition in comparison between the S-LVO composite electrode and the reference sulfur electrode. It can be seen that the S-LVO outperforms the pristine sulfur in the initial capacity and the cycle life. Figure 3(a) demonstrates that, as the DOD increases from 0 to 100%, the sulfur-based cathode undergoes a significant densification and tend to form a severe “mud-crack” morphology as shown in Figure 3(a), while this effect is relatively smaller on the S-LVO composite cathode (Figure 3(b)). This densification process is well seen in the SE image in Figure 3(c). It is believed that the dispersed LVO particles retard the densification of electrode and contribute to preserve the mechanical integrity of electrode. Figure 4 shows the evolution of sulfur-to-carbon ratio (S/C) that was measured at constant values of electron beam current, working distance and collection. It is found that the S-LVO electrode shows a lower variation of the S/C ratio, which indicates that S-LVO electrode has relatively homogeneous sulfur distribution while the concentration of sulfur is more localized on the electrode surface in the case of the reference electrode. Reference [1] C-S Kim et al, ‘Facile dry synthesis of sulfur-LiFePO4 core-shell composite for scalable fabrication of lithium/sulfur batteries’, Electroch. Comm, 32 (2013) 35-38.

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 score0.003

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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
Published2014
Admission routes1
Has abstractyes

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