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Record W2112614773 · doi:10.1002/adma.201200397

Emerging Applications of Atomic Layer Deposition for Lithium‐Ion Battery Studies

2012· review· en· W2112614773 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Materials · 2012
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
Fundersnot available
KeywordsAtomic layer depositionMaterials scienceNanotechnologyElectronicsAnodeBattery (electricity)Lithium (medication)Energy storageNanoarchitectures for lithium-ion batteriesCoatingLayer (electronics)Engineering physicsElectrodeElectrical engineeringPower (physics)EngineeringChemistryPhysics

Abstract

fetched live from OpenAlex

Lithium-ion batteries (LIBs) are used widely in today's consumer electronics and offer great potential for hybrid electric vehicles (HEVs), plug-in HEVs, pure EVs, and also in smart grids as future energy-storage devices. However, many challenges must be addressed before these future applications of LIBs are realized, such as the energy and power density of LIBs, their cycle and calendar life, safety characteristics, and costs. Recently, a technique called atomic layer deposition (ALD) attracted great interest as a novel tool and approach for resolving these issues. In this article, recent advances in using ALD for LIB studies are thoroughly reviewed, covering two technical routes: 1) ALD for designing and synthesizing new LIB components, i.e., anodes, cathodes, and solid electrolytes, and; 2) ALD used in modifying electrode properties via surface coating. This review will hopefully stimulate more extensive and insightful studies on using ALD for developing high-performance LIBs.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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 head0.054
GPT teacher head0.360
Teacher spread0.306 · 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