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Record W2921132430 · doi:10.1002/anie.201901840

Hierarchical Metal Sulfide/Carbon Spheres: A Generalized Synthesis and High Sodium‐Storage Performance

2019· article· en· W2921132430 on OpenAlexfundno aff
Laifa Shen, Yi Wang, Feixiang Wu, Igor Moudrakovski, Peter A. van Aken, Joachim Maier, Yan Yu

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersArctic Goose Joint VentureNational Natural Science Foundation of China
KeywordsSulfideAnodeVanadiumMaterials scienceCarbon fibersSPHERESMetalSodium-ion batterySodiumChemical engineeringBattery (electricity)Sodium sulfideNanotechnologyElectrodeInorganic chemistryChemistryMetallurgyComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract The development of suitable anode materials is far from satisfactory and is a major scientific challenge for a competitive sodium‐ion battery technology. Metal sulfides have demonstrated encouraging results, but still suffer from sluggish kinetics and severe capacity decay associated with the phase change. Herein we show that rational electrode design, that is, building efficient electron/ion mixed‐conducting networks, can overcome the problems resulting from conversion reactions. A general strategy for the preparation of hierarchical carbon‐coated metal sulfide (MS⊂C) spheres through thermal sulfurization of metal glycerate has been developed. We demonstrate the concept by synthesizing highly uniform hierarchical carbon coated vanadium sulfide (V 2 S 3 ⊂C) spheres, which exhibit a highly reversibly sodium storage capacity of 777 mAh g −1 at 100 mA g −1 , excellent rate capability (410 mAh g −1 at 4000 mA g −1 ), and impressive cycling ability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations93
Published2019
Admission routes1
Has abstractyes

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