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Record W4200464185 · doi:10.1088/1361-6528/ac4064

Synthesis of MnO–Sn cubes embedding in nitrogen-doped carbon nanofibers with high lithium-ion storage performance

2021· article· en· W4200464185 on OpenAlexaff
Longgang Lu, Bin Zhang, Juanjuan Song, Haiwen Gao, Zongdeng Wu, Honglong Shen, Yujunwen Li, Wu Lei, Qingli Hao

Bibliographic record

VenueNanotechnology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMinistry of Education and Child Care
FundersNanjing University of Science and TechnologyNanjing UniversityGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceLithium (medication)AnodeCarbon nanofiberElectrochemistryNanofiberChemical engineeringCarbon fibersNanotechnologyIonElectrospinningElectrodeComposite materialComposite numberCarbon nanotubeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this paper, a carbon nanofiber (CNF) hybrid nanomaterial composed of MnO–Sn cubes embedding in nitrogen-doped CNF (MnO–Sn@CNF) is synthesized through electrospinning and post-thermal reduction processes. It exhibits good electrochemical lithium-ion storage performance as the anode, such as high reversible capacity, outstanding cycle performance (754 mAh g −1 at 1 A g −1 after 1000 cycles), and good rate capability (447 mAh g −1 at 5 A g −1 ). The excellent electrochemical properties are derived from a unique nanostructure design. MnO–Sn@CNF has a three-dimensional conductive network with a stable core–shell structure, which improves the electrical conductivity and mechanical stability of the materials. In addition, the mesopores on the surface of carbon fibers can shorten the diffusion distance of lithium ions and promote the combination of active sites of the material with lithium ions. The internal MnO and Sn form a heterostructure, which enhances the stability of the physical structure of the electrode material. This material design method provides a reference strategy for the development of high-performance lithium-ion batteries anode.

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 score0.037
Threshold uncertainty score0.840

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 spread0.203 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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