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Record W2907127037 · doi:10.1149/ma2018-02/4/192

High-Throughput Electrochemistry of Li-Mn-Co-Ni-O Positive Electrodes

2018· article· en· W2907127037 on OpenAlexaff
Eric McCalla

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrochemistryPhase diagramMaterials scienceElectrodeAnnealing (glass)ThroughputPhase (matter)Electrochemical energy conversionNanotechnologyComputer scienceChemistryMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

In the search for high-energy Li-ion positive electrodes, the layered lithium NMC oxides have been studied extensively. Despite wide interest in this system, until recently, the phase diagram remained poorly understood resulting in many conflicting reports in the literature. Combinatorial synthesis, coupled with X-ray diffraction, was used to determine the phase diagrams under various synthesis 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 shift during cooling. Despite the deepended understanding of the structural phase diagram, there now remains a very limited exploration 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 the mg-scale powder combinatorial samples. The methodology involves using a solution-dispensing robot is used 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. Given that the combinatorial samples here are powders, made using synthesis methods comparable to those used commercially, the results scale-up very well. Beyond the proof-of-concept of the novel high-throughput measurements, results on hundreds of samples in the Li-Mn-Ni-Co-O system of extreme interest as next-generation positive electrodes will be presented. Consequences for further studies in this popular composition space will be discussed. References: [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.

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 score0.004

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.243
Teacher spread0.235 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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