MétaCan
Menu
Back to cohort
Record W3205022272 · doi:10.1002/smll.202103778

Enhancing the Reduction Kinetics of LiSF<sub>6</sub> Batteries by Dispersed Cobalt Phthalocyanines on Porous Carbon

2021· article· en· W3205022272 on OpenAlexaff
Huajin He, Ying‐Chih Liao, Wenhua Zuo, Guochang Li, Jiabao Gu, Yixiao Li, Zheng Hu, Yong Yang

Bibliographic record

VenueSmall · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNanocagesElectrochemistryCarbon fibersCobaltMaterials scienceLithium (medication)Chemical engineeringRedoxCathodePorosityKineticsNanotechnologyInorganic chemistryChemistryElectrodeCatalysisPhysical chemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract Reducing SF 6 (as gas cathode) in Li batteries is a promising concept for the double benefit of mildly converting greenhouse SF 6 and providing a high theoretical energy density of 3922 Wh kg −1 . However, the reduction process is hampered by its sluggish kinetics. Here, cobalt phthalocyanine (CoPc) molecules immobilized on porous carbon matrix are, for the first time, introduced to the LiSF 6 chemistry to deliver an enhanced energy density. It is revealed that the high redox potential of Co(II)Pc/[Co(I)Pc] − (≈2.85 V) facilitates the formation of Co(I)N 4 sites to catalyze the SF 6 electrochemical reduction. By using highly porous holey nitrogen‐doped carbon nanocages as carbon matrix, the LiSF 6 cells deliver a high discharge voltage of 2.82 V at 50 mA g C+CoPc −1 and an unprecedented areal capacity of 25 mAh cm −2 at 0.1 mA cm −2 , much superior to previous results. This work opens up new possibilities for high‐efficiency conversion of SF 6 in lithium batteries.

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 score0.003
Threshold uncertainty score0.597

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSmallSame topicAdvancements in Battery MaterialsFrench-language works237,207