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Record W4401785899 · doi:10.1002/cssc.202400451

Carbon Nanotube Supported Fluorine Substituted Iron Phthalocyanine Enabling Boosted Polysulfide Redox Conversion Kinetics and Cyclic Stability

2024· article· en· W4401785899 on OpenAlexaff
Jiaqi Zhao, Zhanwei Xu, Yujiao Zhang, Jin QingZhu, Longhua Guo, Siyu Chen, Xuetao Shen, Jiayin Li, Zhiguo Li

Bibliographic record

VenueChemSusChem · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Institute for Nanotechnology
FundersNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsRedoxCarbon nanotubeKineticsNucleationChemical engineeringChemistryMaterials sciencePolysulfideNanotechnologyInorganic chemistryOrganic chemistryPhysical chemistryElectrodeElectrolyte

Abstract

fetched live from OpenAlex

Abstract The sluggish transition and shuttle of polysulfides (LiPS) significantly hinder the application and commercialization of Li−S batteries. Herein, carbon nanotubes (CNTs) supported 10 nm sized iron Hexadecafluorophthalocyanine (FePcF 16 /CNTs) are prepared using a solid synthesis approach. The well‐exposed FePcF 16 molecular improve the LiPS capture efficiency and redox kinetics by its central Fe−N 4 units and F functional groups. The strong electron withdraw F groups significantly promote the conjugate effect and decrease the steric hindrance during mass migration procedure. Distribution of relaxation time (DRT) analysis shows that the Fe−N 4 units exhibit strong affinity towards LiPS and the F groups further improve the Li + diffusion rate in Li 2 S nucleation and oxidation procedure, accomplishing a porous surface on cathode. As a result, the FePcF 16 /CNTs separator exhibits a high initial capacity of 1136.2 mAh g −1 at 0.2 C, outstanding rate capacity of 624.9 mAh g −1 at 5 C and superior long‐term stability at 2 C surviving 300 cycles with a low capacity decay of 0.43 ‰ per cycle.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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