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Record W2766681287 · doi:10.1021/acsnano.7b06296

Tuning Shell Numbers of Transition Metal Oxide Hollow Microspheres toward Durable and Superior Lithium Storage

2017· article· en· W2766681287 on OpenAlexafffund
Dan Luo, Ya‐Ping Deng, Xiaolei Wang, Gaoran Li, Juan Wu, Jing Fu, Wen Lei, Ruilin Liang, Yangshuai Liu, Yuan‐Li Ding, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueACS Nano · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsConcordia UniversityMcMaster UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooUniversity of Toledo
KeywordsMaterials scienceMicrosphereNanotechnologyLithium (medication)Transition metalOxideShell (structure)Chemical engineeringComposite materialMetallurgyChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Multishelled hollow structured transition metal oxides (TMOs) are highly potential materials for high energy density energy storage due to their high volumetric energy density, reduced aggregation of nanosized subunits, and excellent capacity and durability. However, traditional synthetic methods of TMOs generally require complicated steps and lack compositional/morphological adjustability. Herein, a general and straightforward strategy is developed to synthesize multishelled porous hollow microspheres, which is constituted of nanosize primary TMO particles, using metal acetate polysaccharide microspheres as the precursor. This universal method can be applied to design TMOs’ hollow spheres with tunable shell numbers and composition. The hierarchical porous quadruple-shelled hollow microspheres with nanosized Ni–Co–Mn oxide demonstrate an increased number of active sites, boosted rate capability, enhanced volumetric energy density, and showed great tolerance toward volume expansion upon cycling, thus exhibiting excellent Li + storage capability with high specific capacity (1470 mAh g –1 at 0.2 A g –1 and 1073.6 mAh g –1 at 5.0 A g –1 ) and excellent cycle retention (1097 mAh g –1 after 250 cycles at 0.2 A g –1 ) among TMO anode materials for lithium-ion 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 score0.003
Threshold uncertainty score0.567

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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

Citations108
Published2017
Admission routes2
Has abstractyes

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