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Record W2937707568 · doi:10.1149/ma2019-03/2/221

High-Throughput Electrochemical Testing of Positive Electrode for Li-Ion Batteries

2019· article· en· W2937707568 on OpenAlexaff
Karlie P. Potts, Eric McCalla

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrochemistryPhase diagramMaterials scienceElectrodeAnnealing (glass)Phase (matter)Electrochemical energy conversionLithium (medication)PrecipitationNanotechnologyChemistryPhysical chemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

In the search for high-energy Li-ion positive electrodes, the layered lithium NMC oxides have been extensively studied. Despite wide interest in this system, the phase diagram remained poorly understood until recently, which resulted in many conflicting reports in the literature. Combinatorial synthesis, coupled with X-ray diffraction was used to determine the phase diagrams under various synthetic conditions [1-2]. The unexpected complexity seen in this system included multiple 3-phase regions that transform during cooling along with boundaries to single phase regions which also shift during cooling. Even with a deepened understanding of the structural phase diagram, there is still minimal knowledge of how the electrochemical properties evolve across these complex phase spaces. Herein, we adapt a high-throughput electrochemical testing system wherein 64 samples are cycled simultaneously in order to measure the cycling of mg-scale powder combinatorial samples. The methodology involves using a solution-dispensing robot to make the samples by co-precipitation synthesis, followed by high temperature annealing. The samples are subsequently mounted in the combinatorial electrochemical cell and cycled simultaneously. Figure 1 shows cyclic voltammograms obtained for identical LiCoO2 samples (weighing at most 2.5 mg) in the combinatorial cell. A remarkable level of consistency is achieved in comparison to published cyclic voltammograms for LiCoO2 [3]. The described methodology allows for the determination of specific capacity, with an RSD approaching 10%. Given that these combinatorial samples are powders, synthesized using methods comparable to those used commercially, the results scale-up very well. The proof-of-concept of this novel high-throughput electrochemical technique will be presented, exploring the level of precision that can be achieved for important electrochemical metrics (redox potential, specific capacity, irreversible capacity, voltage during storage experiments, etc.). Preliminary results from this combinatorial electrochemistry methodology using the Li-Mn-Ni-O system will be displayed, and the ramifications of electrochemically probing this critical composition space will be examined. [1] McCalla, E., Rowe, A. W., Shunmugasundaram, R., & Dahn, J. R. (2013). Structural study of the Li–Mn–Ni oxide Pseudoternary system of interest for positive electrodes of Li-ion batteries. Chemistry of Materials, 25(6), 989-999. [2] Brown, C. R., McCalla, E., Watson, C., & Dahn, J. R. (2015). Combinatorial study of the Li–Ni–Mn–Co oxide pseudoquaternary system for use in Li–Ion battery materials research. ACS combinatorial science, 17(6), 381-391. [3] Cho, J., Kim, Y.J., Park, P. (2001). LiCoO2 cathode material that does not show a phase transition from hexagonal to monoclinic phase. Journal of the Electrochemical Society, 148(10), A1110-A1115 Figure 1: Cyclic voltammetry of LiCoO2 performed in the high-throughput electrochemical testing system Figure 1

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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
Published2019
Admission routes1
Has abstractyes

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