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Record W3025303261 · doi:10.1149/ma2020-012383mtgabs

Enabling Aluminum-Silicon Electrodes As Monolithic Anodes for Lithium-Ion Batteries

2020· article· en· W3025303261 on OpenAlexaff
Mohammad H. Tahmasebi, Dominik Kramer, Tianye Zheng, Reiner Mönig, Steven T. Boles

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnodeMaterials scienceSiliconBattery (electricity)ElectrodeLithium (medication)NanotechnologyAlloyCurrent collectorOptoelectronicsElectrolyteMetallurgy

Abstract

fetched live from OpenAlex

Based on current trends in lithium-ion battery (LIB) production and forecasts for immense demand of these energy storage devices, it is evident that all cell components will need dramatic improvement in the future. Although anode materials may be considered by some to be a lower priority, compared to cathode materials, the cost of the anode (and Cu foil current collector) comprise nearly 25% of the materials cost of a LIB cell, and consume ~30% of the volume. Among the possible alternative anode materials for LIBs to replace slurry-based graphite electrodes, alloying materials such as Si, Sn, Al and Ge are promising candidates. Despite the attractive advantages of aluminum-based electrodes (such as low cost, good geographic dispersity and high abundancy), capacity fade due to a large volume change associated with the α/β (Al/LiAl) phase transformation during cycling is the key problem. Al alloy or composite electrodes are a promising strategy for improving the reliability of Al-based electrodes. Among the Al-based monolithic anode materials for LIBs, to the best of our knowledge, Al-rich Al-Si alloys or composites have rarely been investigated. The present work highlights a new achievement towards realizing monolithic, free-standing alloy anodes which can significantly reduce both the cost and processing complexity of LIB electrodes. Herein, we propose a novel strategy to enable the use of aluminum-silicon alloys as monolithic anodes for LIBs. Accordingly, the microstructural, morphological, and electrochemical evolution of Al-Si thin-films of various compositions were investigated, with a focus on understanding the process of Al-Si-Li ternary phase formation during lithiation (in the particular case of excess Al). Ex situ microscopy observations, along with comprehensive electrochemical analysis, suggests that remarkable performance can be achieved by controlling the electrochemical condition for the formation of Al-Si-Li ternary phase, such that no LiAl (β phase) forms during lithiation. In other words, Al-Si-Li ternary phase is cycled within a soft aluminum matrix, thereby avoiding the degradation associated with the de-/lithiation of the β phase [1]. [1] M. H. Tahmasebi, D. Kramer, R. Mönig, and S. T. Boles, “Insights into Phase Transformations and Degradation Mechanisms in Aluminum Anodes for Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 166, no. 3, pp. A5001–A5007, 2019.

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 score0.003

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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