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Record W3114863494 · doi:10.1149/ma2020-022406mtgabs

Quantifying Slow Diffusion in High Capacity, Multi-Phase Electrode Materials

2020· article· en· W3114863494 on OpenAlexaff
Sara Early, Eliana Feygin, Mohammedreza Zamanzad Ghavidel, Shaochen Ding, Oles Sendetskyi, Michael D. Fleischauer

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of AlbertaNational Research Council CanadaUniversity of Waterloo
Fundersnot available
KeywordsNucleationDiffusionLithium (medication)Materials scienceElectrochemistryElectrodeCyclic voltammetryPhase (matter)FOIL methodAlloyChemical engineeringAnalytical Chemistry (journal)ThermodynamicsChemistryMetallurgyComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

The use of metal alloys as negative electrode materials in lithium-ion batteries offers the potential for high lithium storage capacities at low cost, two key aspects for competitive energy storage technologies. Recent work shows that a single piece of aluminum foil can be used as both the negative electrode active material and current collector, and in a full cell configuration, support over 100 cycles without appreciable capacity fade.1 This breakthrough is due to control of the nucleation and growth of the β-AlLi phase. Many other (higher capacity) Al-Li phases are predicted and can be detected when formed via thermal methods, but are generally not accessible via electrochemical methods. We demonstrated the reversible electrochemical formation of four Li-Al phases (β-AlLi, Al2Li3, AlLi2-x, Al4Li9) by cycling at slightly elevated temperatures (>= 40°C) to overcome nucleation and diffusion barriers.2 There is a strong need to decouple kinetic (e.g. diffusion) and thermodynamic limits to understand and improve the practical performance of metal alloy electrodes. Reliable quantification of diffusion rates can be challenging. Many of the techniques commonly used (e.g. potentio-or-galvanostatic titration, electrochemical impedance, cyclic voltammetry) depend on questionable assumptions or are not always applicable to materials with co-existing phases, as found in most lithium-metal alloys.3 We will report on our use of the lithiation of thin aluminum foil as a model (i.e. diffusion limited) system to compare and contrast apparent (i.e. technique-dependent) lithium diffusion rates in the various Al-Li phases. In addition, as the samples are in effect planar, experimental diffusion measurements will be complemented with numerical simulations of one dimensional Fickian diffusion in the same conditions, to validate the calculated diffusion values against experimentally measured galvanostatic potential-capacity data sets. All electrochemical experimental data was collected using Conflat-style electrochemical cells, which we designed for repeatable electrochemical testing over a wide temperature range (30 - 150°C).4 Repeatability is enabled by engineered alignment of electrodes, adjustable control of stack pressure, and ultra-high vacuum tight Conflat seals. Cell-to-cell variability (including all factors) is <5% at intermediate temperatures (<90°C) and <10% at higher temperatures (120-150°C) as we approach the limits of liquid electrolyte stability and the melting point of Li (181°C). Galvanostatic data was collected over a wide range of current densities (from dynamic to near-equilibrium rates, e.g. C/9 to C/280) and a wide range of tightly controlled temperatures (30 ± 1 to 150 ± 3°C). Examples of the dramatic effects of current density and temperature on phase formation / cell potential and final capacity are provided in the attached figure. Nucleation of β-AlLi did not occur at low temperatures and moderate current densities. Nucleation of Al2Li3 only occurred at temperatures above 40°C, even at very low rates. Measured potentials of the lithiation of β-AlLi (to form Al2Li3) varied by almost an order of magnitude, presumably due to very slow diffusion at moderate temperatures. Insight in to technique-dependent diffusion measurements and robust estimates of diffusion coefficients validated against a broad experimental data set will improve our analysis of, and the potential for, metal alloy electrode materials. [1] H. Li et al., Nature Communications, 11, 1584 (2020). [2] M.Z. Ghavidel et al., Journal of The Electrochemical Society, 166, A4034-A4040 (2019). [3] Y. Xu et al., International Journal of Hydrogen Energy, 35, 6366 (2010). [4] M.D. Fleischauer et al., Journal of The Electrochemical Society, 166, A398-A402 (2019). Figure 1

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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)0.001
metaresearch head score (Gemma)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.284
Teacher spread0.236 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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