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Record W2896955067 · doi:10.1039/c8nr05889j

2D molybdenum nitride nanosheets as anode materials for improved lithium storage

2018· article· en· W2896955067 on OpenAlexaff
Liangbiao Wang, Kailong Zhang, Honglin Pan, Li Wang, Di Wang, Weicheng Dai, Hengfei Qin, Gaoran Li, Junhao Zhang

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsAnodeMaterials scienceNitrideMolybdenumLithium (medication)NanotechnologyMetallurgyChemistryElectrode

Abstract

fetched live from OpenAlex

Two-dimensional (2D) molybdenum nitride (MoN) nanosheets are promising anode materials for improved lithium-ion batteries. However, the reported synthesis methods of MoN generally rely on high-temperature and complex procedures with low cost efficiency. Herein, we report a facile one-pot synthesis of 2D MoN nanosheets at a low temperature of 400 °C via a solid-state reaction of molybdenum disulfide, sulfur and sodium amide in an autoclave. When employed as the anode material for lithium ion batteries, the as-developed MoN electrode exhibits outstanding cyclability with a high capacity retention of 898 mA h g-1 over 400 cycles at a current rate of 200 mA g-1 as well as a superb rate capability with a capacity of 505 mA h g-1 at a high rate up to 2 A g-1. The excellent lithium storage performance of the MoN electrode is attributed to its advantageously high conductivity and unique 2D nanostructure.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.047
Threshold uncertainty score1.000

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)0.0010.001

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Citations75
Published2018
Admission routes1
Has abstractyes

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