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Record W3116355650 · doi:10.1149/ma2020-02623177mtgabs

Decoupling Electrolyte and Electrode Reactions Using in-Operando Electrochemical X-Ray Powder Diffraction

2020· article· en· W3116355650 on OpenAlexaff
Oles Sendetskyi, Mark Salomons, Steve Launspach, Michael D. Fleischauer

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Research Council CanadaUniversity of Alberta
Fundersnot available
KeywordsElectrolyteMaterials scienceElectrochemistryElectrodeSeparator (oil production)Lithium (medication)NucleationElectrochemical cellBattery (electricity)Chemical engineeringNanotechnologyChemistry

Abstract

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Lithium-ion batteries are the dominant energy storage technology – steady and significant progress toward lower costs, better safety, and better performance is being realized by an improved understanding and control of new materials and system engineering. Various novel materials for lithium and lithium-ion battery electrodes are emerging, many of which depend on alkali-metal alloys. Among them, Aluminum electrodes offer the potential for high lithium-ion capacities at low costs, for example, capacities of 2000 mAh/g (six times higher than graphite, the current commercial standard) can be accessed at moderate temperatures (above 40°C),1 and recent studies show that Aluminum foil electrodes can act as both the active material and current collector.2 Nevertheless, challenges with capacity fade, electrolyte breakdown, slow diffusion and nucleation barriers still exist and alkali-metal electrochemical reactivity is not fully understood. Elevated temperature operation can be used to overcome nucleation barriers and slow diffusion, but at the cost of increased rates of liquid electrolyte breakdown. Here, we combine controlled temperature electrochemical lithiation and delithiation with x-ray powder diffraction for in-situ studies of electrode and electrolyte reactions to shed light on these problems. Our in-operando cell is based on commercially available ultra-high vacuum compatible Conflat flanges with Beryllium windows and electrical feedthroughs to maintain commercially-relevant stack pressure and cell sealing.3 We will report on cell design and mechanical, electrochemical, and x-ray diffraction performance, including full-powder-pattern refinement of the Li-Al electrode material during electrochemical lithiation and delithiation. Such refinement of the diffraction patterns allows us to quantify the fractions of the phases formed as well as details of the crystal structure (e.g. solid solution vs. new phase). The evolution of the measured cell potential (red line) and the powder diffraction pattern (heat map) are shown in sections a) and b), respectively, of the attached figure. We confirm the formation of AlLi, Al2Li3 and AlLi2 and follow their fractions in-operando during charge and discharge of the cell. In section a) of the attached figure, electrode composition determined from pattern refinement (black dots) is compared to the composition predicted from electrochemical methods (blue solid lines) to separate the effects of electrode phase transitions and electrolyte breakdown. Electrode composition determined from XRD matches well the predicted composition at the beginning of the lithiation process, however it starts to deviate significantly by the time the delithiation is finished. In-operando XRD allows us to quantify this deviation and calculate the rate of liquid electrolyte breakdown. Better understanding of phase formation and electrolyte breakdown in Li-ion battery electrode materials and electrolytes will enable more robust production and improved performance of the batteries on the market. [1] M.Z. Ghavidel et al., Journal of The Electrochemical Society, 166 (16) A4034-A4040 (2019). [2] H. Li et al., Nature Communications, 11, 1584 (2020). [3] M.D. Fleischauer et al, Journal of The Electrochemical Society, 166 (2) 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.244
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 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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