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Record W3116489737 · doi:10.1002/hlca.202000203

Pulsed Laser Deposition as a Tool for the Development of All Solid‐State Microbatteries

2020· article· en· W3116489737 on OpenAlexaff
Luca Indrizzi, Natacha Ohannessian, Daniele Pergolesi, Thomas Lippert, Elisa Gilardi

Bibliographic record

VenueHelvetica Chimica Acta · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNovelis (Canada)
FundersNational Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel MaterialsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPulsed laser depositionDeposition (geology)FabricationThin filmNanotechnologyLithium (medication)ChemistryEnergy storageLaserDegradation (telecommunications)Atomic layer depositionEngineering physicsMaterials scienceComputer scienceOptics

Abstract

fetched live from OpenAlex

Abstract All‐solid‐state lithium ion batteries (LIB) are currently the most promising technology for next generation electrochemical energy storage. Many efforts have been devoted in the past years to improve performance and safety of these devices. Nevertheless, issues regarding chemical and mechanical stability of the different components still hinder substantial improvements. Pulsed laser deposition (PLD) has proved to be an outstanding technique for the deposition of thin films of materials of interest for the fabrication of LIB. Thanks to its versatility and possible fine tuning of the thin film properties, PLD promises to be a very powerful tool for the fabrication of model systems which would allow to study in detail material properties and mechanisms contributing to LIB degradation. Nevertheless, PLD presents difficulties in the deposition of LIB components, mainly due to the presence of elements with large difference of atomic mass in their chemical composition. In this review, we report the main challenges and solution strategies used for the deposition through PLD of complex oxides thin films for LIB.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.019
GPT teacher head0.254
Teacher spread0.234 · 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 teacher head, 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

Citations29
Published2020
Admission routes1
Has abstractyes

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