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Record W3209087364 · doi:10.1088/1361-6528/ac3703

MgCo layered double hydroxide-based yolk shell polyhedrons as multifunctional sulfur mediator for lithium–sulfur batteries

2021· article· en· W3209087364 on OpenAlexaff
Kai Zhang, You Li, Hongyu Wang, Zisheng Zhang, Guihua Liu, Yongguang Zhang

Bibliographic record

VenueNanotechnology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceSulfurChemical engineeringMesoporous materialChemisorptionCathodeElectrolyteAdsorptionInorganic chemistryElectrodeChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The development of efficient sulfur host materials to address the shuttle effect issues of lithium polysulfides (LiPSs) is crucial in the lithium–sulfur (Li–S) batteries but still challenging. In the present study, a novel yolk shell structured MgCo-LDH/ZIF-67 composite is designed as Li–S battery cathode. In this composite, the shell layer is MgCo layered double hydroxide constructed by partially etching ZIF-67 nanoparticle by Mg 2+ , and the core is the unreacted ZIF-67 particle. The unique yolk shell structure not only provides abundant pores for sulfur accommodation, but also facilitates the electrolyte penetration and ion transport. The ZIF-67 core exhibits strong polar adsorption to LiPSs through the Lewis acid-base interactions, and the micropores/mesoporous can further trap LiPSs. Meanwhile, the MgCo-LDH shell exposes enough sulfur-philic sites for enhancing chemisorption and catalyzes LiPSs conversion. As a result, when MgCo-LDH/ZIF-67 is used as sulfur host in the cathode, the cell achieves a high discharge capacity of 1121 mAh g −1 at 0.2 C, and an areal capacity of 5.0 mAh cm −2 under high sulfur loading of 5.8 mg cm −2 . The S/MgCo-LDH/ZIF-67 electrode holds a promising potential for the development of 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 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 score0.088
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 integrity0.0010.000
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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