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Record W4365150191 · doi:10.34133/energymatadv.0032

Hollow and Hierarchical CuCo-LDH Nanocatalyst for Boosting Sulfur Electrochemistry in Li-S Batteries

2023· article· en· W4365150191 on OpenAlexaff
Min Li, Yebao Li, Qiao Cu, Yan Li, Hongyang Li, Zihao Li, Ming Li, Hua Liao, Ge Li, Gaoran Li, Xin Wang

Bibliographic record

VenueEnergy Material Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolysulfideSulfurElectrochemistryMaterials scienceHydroxideElectrocatalystChemical engineeringCatalysisNanotechnologyElectrodeChemistryInorganic chemistryElectrolyteMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Lithium-sulfur batteries (LSBs) are promising candidates for next-generation high-efficiency energy storage, yet their practical implementation is seriously impeded by the parasitic shuttle effect and sluggish reaction kinetics. Herein, we develop a unique Cu, Co layered double hydroxide (CuCo-LDH) with a hollow and hierarchical structure as an advanced electrocatalyst to tackle these challenges. Combining the compositional, architectural, and chemical advantages, the as-developed CuCo-LDH enables facile charge transfer, fully exposed active interfaces, and strong interactions with polysulfides via metal–sulfur bonding. When employed in the functional separator, a reliable polysulfide barrier can be established against the shuttling behavior, while the excellent catalytic activity realizes fast and efficient sulfur electrochemistry. As a result, the CuCo-LDH-based LSBs achieve a well-restrained capacity decay of 0.049% per cycle over 500 cycles together with a good rate capability up to 5 C. Moreover, a favorable areal capacity of 4.39 mAh cm −2 and decent cyclability are still attainable even under a high sulfur loading of 4.2 mg cm −2 and a low E/S ratio of 6 ml g −1 . This work affords a feasible and instructive pathway toward advanced sulfur electrocatalysts as well as high-performance LSBs.

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 score0.019
Threshold uncertainty score0.850

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 head0.006
GPT teacher head0.209
Teacher spread0.204 · 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

Citations37
Published2023
Admission routes1
Has abstractyes

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