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Record W3091145295 · doi:10.1002/cssc.202002023

Exploring the Reversibility of Phase Transformations in Aluminum Anodes through Operando Light Microscopy and Stress Analysis

2020· article· en· W3091145295 on OpenAlexaff
Tianye Zheng, Dominik Kramer, Mohammad H. Tahmasebi, Reiner Mönig, Steven T. Boles

Bibliographic record

VenueChemSusChem · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials sciencePhase (matter)Stress (linguistics)AluminiumMicroscopyChemical engineeringOptical microscopeAnodeAnodic Aluminum OxideNanotechnologyChemistryMetallurgyOpticsScanning electron microscopeComposite materialPhysical chemistryElectrodePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Aluminum is well-known to possess attractive properties for possible use as an anode material in Li-ion batteries (LIBs), but effort is still needed to understand how and why it degrades. Herein, investigations of the delithiation and the re-lithiation processes in Al thin films using an established operando light microscopic platform are pursued. Operando videos highlight that the extraction of Li from the β phase (LiAl) is accompanied by fracture and crack formation leading to the detachment of the α phase (Al) from the rest of the electrode. The evolution of mechanical stress in Al thin film electrodes is tracked and shows severe stress asymmetry as phase transformations progress. Combining with the observations from light and electron microscopy, the mechanical stress during dealloying can be explained by Li solubility with the β phase, formation of cracks and of a highly porous Al nanostructure. Although the results pave a difficult path for utilization of the Al/LiAl/Al (α/β/α) phase transformations in future LIBs, they also suggest excellent opportunities when structural changes can be prevented, which otherwise impact the stability of Al-based electrodes.

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 score0.016
Threshold uncertainty score0.371

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 head0.057
GPT teacher head0.287
Teacher spread0.230 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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