MétaCan
Menu
Back to cohort
Record W4316192944 · doi:10.1002/anie.202216312

Low Concentration Sulfolane‐Based Electrolyte for High Voltage Lithium Metal Batteries

2023· article· en· W4316192944 on OpenAlexafffund
Pengcheng Li, Hao Zhang, Jun Lü, Ge Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCanada First Research Excellence FundUniversity of Alberta
KeywordsElectrolyteSulfolaneLithium (medication)Ionic conductivityMaterials scienceInorganic chemistryChemical engineeringLithium metalConductivitySalt (chemistry)ChemistryElectrodeOrganic chemistrySolvent

Abstract

fetched live from OpenAlex

Abstract Electrolyte engineering is crucial for the commercialization of lithium metal batteries. Here, lithium metal is stabilized in the highly reactive sulfolane‐based electrolyte under low concentration (0.25 M) for the first time. Inorganic‐polymer hybrid solid electrolyte interphase (SEI) with high ionic conductivity, low bonding with lithium and high flexibility enables dense chunky lithium deposition and high plating/stripping efficiency. Low concentration electrolyte (LCE) also enables excellent cycling stability of LiNi 0.5 Co 0.2 Mn 0.3 O 2 (NCM523)/Li cells at 1 C (90.7 % retention after 500 cycles) and 0.3 C (83.3 % retention after 1000 cycles). With a low N/P ratio (≈2), the capacity retention for NCM523/Li cells can achieve 94.3 % after 100 cycles at 0.3 C. Exploring the LCE is of paramount significance because it provides more possibilities of the lithium salt selections, especially reviving some lithium salts that are excluded before due to their low solubility. More importantly, LCE has the significant advantage of commercialization due to its cost‐effectiveness.

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 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 score0.382
Threshold uncertainty score0.570

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)0.0000.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations60
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207