MétaCan
Menu
Back to cohort
Record W4285399344 · doi:10.1149/ma2022-01185mtgabs

Practical Considerations in the Development of 3 and 15 Ah Rechargeable Lithium Pouch Cells

2022· article· en· W4285399344 on OpenAlexaff
Owen Crowther

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsEaglePicher (Canada)
Fundersnot available
KeywordsAnodeLithium (medication)Lithium metalPouchElectrolyteMaterials scienceGraphiteNanotechnologyChemical engineeringChemistryElectrodeComposite materialEngineeringSurgeryBiologyPhysical chemistry

Abstract

fetched live from OpenAlex

Commercially available Li-ion batteries using graphite or graphite-silicon blended anodes are currently approaching a cell level specific energy of 350 Wh kg −1 . EaglePicher previously demonstrated a 2 Ah rechargeable pouch with a lithium metal anode that delivered 375 Wh kg −1 (Crowther, Owen. "Solving Barriers to Commercialization of Cells with Lithium Metal Anodes." 236th ECS Meeting (October 13-17, 2019) . ECS, 2019). Further improvements to chemistry and cell design resulted in a 3 Ah pouch with longer cycle life and specific energies above 400 Wh kg −1 (Crowther, Owen. "Rechargeable Lithium Metal Pouch Cell Development." ECS Meeting Abstracts . No. 1. IOP Publishing, 2021). This paper will discuss several new areas that resulted in improved cell performance such as increasing the tab size, optimizing the electrolyte type and amount, and introducing excess lithium metal into the anode. Figure 1 demonstrates the rate capability of the cell with larger tabs to minimize the ohmic resistance. The cell with delivers ~425 Wh kg −1 at low rate and >300 Wh kg −1 at a 6.6C rate. The maximum continuous rate for the cell with the original tabs was 1C. Finally, the initial prototype performance of a 15 Ah rechargeable lithium pouch cell with a specific energy >500 Wh kg −1 will be highlighted. 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.050
GPT teacher head0.304
Teacher spread0.254 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207