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Record W2908828937 · doi:10.1149/ma2018-02/58/2133

Structural Change of the Discharge Products in Lithium Sulfur Battery during Storage

2018· article· en· W2908828937 on OpenAlexaff
Hyungjun Noh, Yun‐Jung Kim, Jin Hong Lee, Hee-Tak Kim

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBattery (electricity)CrystallinityX-ray photoelectron spectroscopyMaterials scienceEnergy storageLithium (medication)CathodeLithium–sulfur batteryChemical engineeringChemistryComposite materialPhysical chemistryThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Lithium-Sulfur (Li-S) battery has been regarded as one of the most promising candidates to commercialize electric vehcles (EVs). Compared to Ni-Cd or commercialized Li ion battery, Li-S battery possesses many advantages, including high theoretical capacity (1,675 mAh/g) and low price of active materials. Energy density, rate capability, and cycling stabilities are regarded as key performances of any next generation secondary batteries, however, memory effect, which is history-dependent variation in battery state, is also a highly important battery chacteristic to be considered. As exampled with Ni-Cd battery, memory effect often leads to an accelerated cell deterioration [1]. In recent years, it was also reported that LiFePO4,which is a promising cathode material for EV applications, has memory effect [2] and it could result in a severe error of battery managment system (BMS). In spite of its importance, such memory effect has not been issued for Li-S battery. In this work, the structural change of discharge products during storage and its influence on the subsequent cycle was studied. Structural changes with different storage time were clearly observed with ex-situ X-ray diffraction (XRD) and X-ray photoeletron spectroscopy (XPS) analysis. The XRD results demonstrated that the discharge products with a low crystallinity is converted to a more crystalline structure during a storage at room temperature. In addition, the XPS spectra collected at different storage time suggest that the structural change observed in XRD originates from a compositional change of the solid discharge products. The structural evolution of the discharge products significantly influence the overpotential of the subsequent charging step. This small perturbation in voltage profile can induce severe error in battery management system in EVs because it can lead to mis-estimate state of charge. The mechanism for the behavior and the impact on the Li-S performances will be presented and discussed.

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 communication0.0000.000
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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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
Published2018
Admission routes1
Has abstractyes

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