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Record W3082787878 · doi:10.1016/j.isci.2020.101505

Breaking Free from Cobalt Reliance in Lithium-Ion Batteries

2020· review· en· W3082787878 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

VenueiScience · 2020
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsCobaltLithium (medication)CathodeBattery (electricity)ScarcityEnergy storageWork (physics)Natural resource economicsNanotechnologyMaterials scienceEngineering physicsEnvironmental scienceElectrical engineeringEngineeringEconomicsMetallurgyMechanical engineeringPhysicsMarket economy

Abstract

fetched live from OpenAlex

The exponential growth in demand for electric vehicles (EVs) necessitates increasing supplies of low-cost and high-performance lithium-ion batteries (LIBs). Naturally, the ramp-up in LIB production raises concerns over raw material availability, where constraints can generate severe price spikes and bring the momentum and optimism of the EV market to a halt. Particularly, the reliance of cobalt in the cathode is concerning owing to its high cost, scarcity, and centralized and volatile supply chain structure. However, compositions suitable for EV applications that demonstrate high energy density and lifetime are all reliant on cobalt to some degree. In this work, we assess the necessity and feasibility of developing and commercializing cobalt-free cathode materials for LIBs. Promising cobalt-free compositions and critical areas of research are highlighted, which provide new insight into the role and contribution of cobalt.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.298
Teacher spread0.262 · 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