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Record W3196962603 · doi:10.1002/ente.202100455

Abrasive Blasting of Lithium Metal Surfaces Yields Clean and 3D‐Structured Lithium Metal Anodes with Superior Properties

2021· article· en· W3196962603 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

VenueEnergy Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-Québec
FundersBayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie
KeywordsPassivationMaterials scienceLithium (medication)AnodeMetalAbrasiveElectrodeDielectric spectroscopyMetallurgyElectrochemistryLayer (electronics)Composite materialChemistry

Abstract

fetched live from OpenAlex

Avoiding harmful passivation of the lithium metal surface during its implementation as an anode material is a challenge to its use in rechargeable lithium metal batteries. It is critical to control the chemical composition and the morphology of the native passivation layer and to avoid contamination by lubricants or other substances involved in the processing. Herein, abrasive blasting is used as a physical method to achieve clean and 3D‐structured lithium metal electrodes. The careful choice of the abrasive agent and the blasting parameters results in well‐controlled surface properties. The blasted lithium electrodes exhibit significantly lower overvoltages with values as low as 10 mV at 0.1 mA cm −2 . Electrochemical impedance spectroscopy shows that blasted lithium has interface resistances that are up to five times smaller than those of untreated lithium. The effectiveness of blasting as a cleaning method is clear even in the case of thicker and highly resistive passivation layers occurring after exposure to ambient air.

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 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 score0.011
Threshold uncertainty score0.852

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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