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Record W4386305983 · doi:10.1002/smll.202304938

The Enhancement Mechanism of Different Single‐Transition Metal Atomic Catalysts/Sulfur Cathode on High‐Performance of Li–S Batteries

2023· article· en· W4386305983 on OpenAlexaff
Chao Wu, Jinggao Wu, Juan Li, Zhuo Zou, Hong Bin Yang, Xiaoshuai Wu, Qing‐Xin Zeng, Fangyin Dai, Wei Sun, Chang Ming Li

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMinistry of Agriculture
FundersSuzhou University of Science and TechnologyChongqing Postdoctoral Science Foundation
KeywordsCatalysisTransition metalDissolutionCathodeElectrolyteMaterials scienceBattery (electricity)SulfurAdsorptionMetalChemical engineeringElectrochemistryInorganic chemistryElectrodeChemistryPhysical chemistryMetallurgyThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Materials with various single‐transition metal atoms dispersed in nitrogenated carbons (M─N─C, M = Fe, Co, and Ni) are synthesized as cathodes to investigate the electrocatalytic behaviors focusing on their enhancement mechanism for performance of Li–S batteries. Results indicate that the order of both electrocatalytic activity and rate capacity for the M─N─C catalysts is Co > Ni > Fe, and the Co─N─C delivers the highest capacity of 1100 mAh g −1 at 1 C and longtime stability at a decay rate of 0.05% per cycle for 1000 cycles, demonstrating excellent battery performance. Theoretical calculations for the first time reveal that M─N─N─C catalysts enable direct conversion of Li 2 S 6 to Li 2 S rather than Li 2 S 4 to Li 2 S by stronger adsorption with Li 2 S 6 , which also has an order of Co > Ni > Fe. And Co─N─C has the strongest adsorption energy, not only rendering the highest electrocatalytic activity, but also depressing the polysulfides’ dissolution into electrolyte for the longest cycle life. This work offers an avenue to design the next generation of highly efficient sulfur cathodes for high‐performance Li–S batteries, while shedding light on the fundamental insight of single metal atomic catalytic effects on Li–S 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 score0.004
Threshold uncertainty score0.388

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 head0.194
Teacher spread0.180 · 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

Citations33
Published2023
Admission routes1
Has abstractyes

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