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Record W2305246441 · doi:10.1149/ma2016-03/2/670

Study the Interface of Cathode Material and Li-La-Zr-O Thin Film Prepared at Low Temperature for All Solid State Li Battery

2016· article· en· W2305246441 on OpenAlexaff
Mina Zarabian, M.J. Pérez-Zurita, Aligül Büyükaksoy, Pedro Pereira‐Almao, Venkataraman Thangadurai

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrolyteMaterials scienceIonic conductivityX-ray photoelectron spectroscopyConductivityDielectric spectroscopyCathodeElectrochemistryAnalytical Chemistry (journal)Chemical engineeringThin filmElectrodeNanotechnologyChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Switching from an organic polymer-based Li ion electrolyte to a ceramic Li ion electrolyte is expected to allow for the higher energy density, improve the safety and cycle lifetime. These, all solid state Li ion batteries can also be miniaturized, which will streamline the packing design. Among studied Li ion conductive solid electrolyte (1, 2) garnet family have recently gained lots of attention due to the high ionic conductivity and electrochemical stability at ambient temperature. Garnet-like structure, Li7La3Zr2O12 (LLZO) has shown comparable Li ion conductivity with the current Li ion electrolytes (3). However, the reported ionic conductivity of a thin film LLZO was three orders of magnitude lower compared to bulk phase (4). The main goal of the current research is to develop thin film solid electrolyte on the LiCoO2 electrode and investigate the interface. For this purpose, LLZO was fabricated using a liquid precursor technique. The Li-La-Zr nitrate solution was coated on the top surface of the LiCoO2 substrate for several times until the appropriate thickness (300 nm – 1 µm) is obtained. After each step of coating, the deposited precursor is dried and decomposed at 450 °C. Figure 1 shows cross section image of crack-free, flat, dense and homogenous solid electrolyte layer deposited on the surface of the LiCoO2. X-ray photoelectron spectroscopy (XPS) coupled with ion etching is used to understand the interface composition and thickness. Electrochemical impedance spectroscopy (EIS) was used to analyze and understand the electrolyte and interfacial resistance. References 1. S. Teng, J. Tan and A. Tiwari, Current Opinion in Solid State and Materials Science, 18, 29 (2014). 2. V. Thangadurai, D. Pinzaru, S. Narayanan and A. K. Baral, The Journal of Physical Chemistry Letters, 6, 292 (2015). 3. R. Murugan, V. Thangadurai and W. Weppner, Angewandte Chemie International Edition, 46, 7778 (2007). 4. K. Tadanaga, H. Egawa, A. Hayashi, M. Tatsumisago, J. Mosa, M. Aparicio and A. Duran, Journal of Power Sources, 273, 844 (2015). Figure 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 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 score0.004

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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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