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Record W4294031753 · doi:10.1002/aenm.202201704

Accelerated Development of High Voltage Li‐Ion Cathodes

2022· article· en· W4294031753 on OpenAlexafffund
Antranik Jonderian, Shipeng Jia, Gabin Yoon, Victor Cozea, Nooshin Zeinali Galabi, Sang Bok, Eric McCalla

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaSamsung Advanced Institute of Technology
KeywordsMaterials scienceCathodeDopingMolybdenumIonIndiumIonic bondingConductivityCarbon fibersCyclingIonic conductivityElectrodeChemical engineeringNanotechnologyPhysical chemistryOptoelectronicsComposite materialMetallurgyOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract High voltage cathodes are attractive for high energy density Li‐ion batteries. However, candidates such as LiCoPO 4 have presented numerous challenges stemming from poor electronic/ionic conductivities such that typical solutions involving nanosizing result in extremely poor cycling performance. Here, high‐throughput methods are applied to develop near‐micron sized carbon‐coated LiCoPO 4 with improved energy density and capacity retention. In total, 1300 materials with 46 different substituents are synthesized and characterized. A number of substituents show greatly improved capacity (e.g., 160 mAh g −1 for 1% indium (In) substitution vs 95 mAh g −1 for the pristine). However, co‐doping is required to improve extended cycling. Li 1–3x Co 1–2x In x Mo x PO 4 is found to be particularly effective with dramatically improved cycling (as high as 100% after 10 cycles, vs ≈50% in unsubstituted). While In improves the electronic conductivity of the carbon‐coated materials, molybdenum (Mo) co‐doping gives larger particles. DFT calculations show that Mo impedes the formation of Li/Co antisite defects.

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 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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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 teacher head, not a consensus.

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

Citations33
Published2022
Admission routes2
Has abstractyes

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