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Record W2418803055 · doi:10.1038/srep18199

Novel Flower-like Nickel Sulfide as an Efficient Electrocatalyst for Non-aqueous Lithium-Air Batteries

2015· article· en· W2418803055 on OpenAlexaff
Zhong Ma, Xianxia Yuan, Zhenlin Zhang, Delong Mei, Lin Li, Zi‐Feng Ma, Lei Zhang, Jun Yang, Jiujun Zhang

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsNational Research Council Canada
FundersNational Natural Science Foundation of China
KeywordsNickel sulfideElectrocatalystBifunctionalCathodeAqueous solutionNickelSulfideCatalysisChemical engineeringMaterials scienceAmmonium bromideBattery (electricity)Lithium (medication)ElectrochemistryHydrothermal circulationInorganic chemistryElectrodeChemistryMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In this paper, metal sulfide materials have been explored for the first time as a new choice of bifunctional cathode electrocatalyst materials for non-aqueous lithium-air batteries (LABs). Nickel sulfides with two different morphologies of flower-like (f-NiS) and rod-like (r-NiS) are successfully synthesized using a hydrothermal method with and without the assistance of cetyltrimethyl ammonium bromide. As LAB cathode catalysts, both f-NiS and r-NiS demonstrate excellent catalytic activities towards the formation and decomposition of Li2O2, resulting in improved specific capacity, reduced overpotentials and enhanced cycling performance when compared to those of pure Super P based electrode. Moreover, the morphology of NiS materials can greatly affect LAB performance. Particularly, the f-NiS is more favorable than r-NiS in terms of their application in LABs. When compared to both r-NiS and pure super P materials as LAB cathode materials, this f-NiS catalyst material can give the highest capacity of 6733 mA h g(-1) and the lowest charge voltage of 4.24 V at the current density of 75 mA g(-1) and also exhibit an quite stable cycling performance.

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)0.001
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 score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.238
Teacher spread0.224 · 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

Citations78
Published2015
Admission routes1
Has abstractyes

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