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Record W3024475505 · doi:10.1149/ma2020-01153mtgabs

(Invited) Garnet-Based Hybrid Composite Electrolytes for the All-Solid-State Li-S Battery

2020· article· en· W3024475505 on OpenAlexaff
Sourav Bag, Venkataraman Thangadurai

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPolysulfideElectrolyteMaterials scienceIonic conductivityFast ion conductorDissolutionLithium (medication)Battery (electricity)ElectrochemistryComposite numberChemical engineeringConductivityInorganic chemistryChemistryComposite materialElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Lithium sulfur (Li-S) batteries have emerged as one of the most promising post LIBs technologies with a remarkably high theoretical energy density and abundance of elemental sulfur. Nonetheless, there are many problems associated with lithium sulfur batteries such as safety hazard due to lithium dendrite formation and fast capacity decay due to polysulfide dissolution effect.1 Solid electrolytes are promising to prevent lithium dendrite formation and polysulfide dissolution. Among different ceramic electrolytes garnet-type solid inorganic electrolytes are very promising because of its high ionic conductivity and stability with metallic lithium. But the high interfacial resistance with the electrode is the major bottleneck for the practical use of garnet electrolyte.2 However, polymer-based solid electrolytes possess low interfacial resistance but associated low ionic conductivity at room temperature is the biggest challenge for the utilization in solid-state-batteries.3 Recent research theme of Thangadurai group is mainly focused on garnet-type and polymer-garnet composite electrolytes for the practical utilization in all-solid-state Li batteries. Surface modifications of the garnet-type electrolytes and novel composite electrolytes developed in the laboratory have been successfully employed in all-solid-state-Li-S batteries even at room temperature. Fabrication of these electrolytes in bulk scale, characterizations, electrochemical properties and all-solid-state-Li-S battery performances will be discussed. References: 1. Manthiram, A.; Fu, Y.; Chung, S.; Zu, C.; Su, Y. Chem. Rev. 2014, 114, 11751-11787. 2. Han, X.; Gong, Y.; Fu, K.; He, X.; Hitz, G.; Dai, J.; Pearse, A.; Liu, B.; Wang, H.; Rubloff, G.; Mo, Y.; Thangadurai, V.; Wachsman, E.; Hu, L. Nat. Mater. 2016, 16, 572-579. 3. Zhou, C.; Bag, S.; Thangadurai, V. ACS Energy Lett. 2018, 3, 2181-2198. 4. Bag, S.; Zhou, C.; Kim, P.; Pol, V. G.; Thangadurai, V. Energy Storage Mater. 2019 (doi.org/10.1016/j.ensm.2019.08.019)

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.231
Teacher spread0.213 · 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
Published2020
Admission routes1
Has abstractyes

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