MétaCan
Menu
← Back to cohort
Record W4403257876 · doi:10.26434/chemrxiv-2024-xvmbg

The Effect of Silver Particle Distribution in a Carbon Nanocomposite Interlayer on Lithium Plating in Anode-Free All-Solid-State Batteries

2024· preprint· en· W4403257876 on OpenAlexaff
Michael Metzler, Christopher Doerrer, Yige Sun, Guillaume Matthews, Enzo Liotti, Patrick S. Grant

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research CouncilFaraday InstitutionHenry Royce Institute
KeywordsAnodeFaraday efficiencyMaterials sciencePlating (geology)CathodeElectrolyteChemical engineeringLithium (medication)Current collectorCarbon fibersNanocompositeComposite materialElectrodeChemistryComposite number

Abstract

fetched live from OpenAlex

Solid-state batteries can outperform lithium-ion batteries in energy per unit mass and per unit volume when operating with a Li metal anode. However, metallic Li anodes pose significant manufacturing challenges. Anode-free cells avoid these challenges by plating metallic Li at the anode on the first charge, but subsequent non-uniform cyclic Li stripping and plating encourages unwanted Li dendrite growth, decreased coulombic efficiency, and early cell failure. We report a new spray-printed nanocomposite bilayer of silver/carbon black (Ag/CB) between the anodic current collector and Li6PS5Cl solid electrolyte with Ag concentrated at the current collector. Compared with previous Ag/CB mixtures, the bilayer ensured more uniform Li anode formation and improved cycling performance. Cells with a high-Ni oxide cathode had initial discharge capacity > 190 mAh/g and coulombic efficiency > 98% over 100 cycles. The Li plating uniformity with the structured Ag/CB interlayer was confirmed using secondary-ion mass spectrometry (SIMS) imaging.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueChemRxiv→Same topicAdvancements in Battery Materials→French-language works237,207→