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Record W2992741529 · doi:10.1021/acsaem.9b01887

Accelerated Screening of High-Energy Lithium-Ion Battery Cathodes

2019· article· en· W2992741529 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Energy Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsElectrochemistryCathodeBattery (electricity)Lithium (medication)Materials scienceThroughputHigh energyLithium-ion batteryReproducibilityElectrochemical cellEnergy storageElectrochemical energy storageIonLithium batteryElectrodeNanotechnologyChemical engineeringComputer scienceEngineering physicsChemistryPhysical chemistryPhysicsEngineeringPower (physics)TelecommunicationsThermodynamicsChromatography

Abstract

fetched live from OpenAlex

The need for better battery materials has driven research into underexplored complex phase spaces. Herein, we perform the first high-throughput electrochemical study of the entire Li–Ni–Mn–O system of interest for next-generation high-energy cathodes. We first adapt a high-throughput electrochemical system to cycle 64 mg-scale cathodes simultaneously and demonstrate its effectiveness with two test materials: LiCoO2 and Li[Ni1/3Mn1/3Co1/3]O2. The average values for the electrochemical properties obtained for the combinatorial samples show excellent agreement with literature, and cell-to-cell reproducibility is about 7%. The results for the Li–Ni–Mn–O system deepens our understanding dramatically and will guide the rational design of high-energy cathodes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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