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Record W2327538527 · doi:10.1021/jp504721y

Sulfur Refines MoO<sub>2</sub> Distribution Enabling Improved Lithium Ion Battery Performance

2014· article· en· W2327538527 on OpenAlexafffund
Zhanwei Xu, Huanlei Wang, Zhi Li, Alireza Kohandehghan, Jia Ding, Jian Chen, Kai Cui, David Mitlin

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAnodeNanoporousMaterials scienceElectrochemistryLithium (medication)Degradation (telecommunications)IonAgglomerateChemical engineeringBattery (electricity)SulfurCurrent densityDecompositionNanotechnologyElectrodeComposite materialChemistryComputer scienceMetallurgyPhysical chemistryPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

We employ a sulfur-assisted decomposition process to create agglomerates of large (200–500 nm) yet highly nanoporous three-dimensional MoO 2 single crystals partially covered with a few atomic layers of MoS 2 (“MoS 2 /MoO 2 nanonetworks”). These materials are highly promising as lithium ion battery anodes. At a current density of 100 mA g –1, the MoS 2 /MoO 2 nanonetworks exhibit a reversible discharge specific capacity of 1233 mAh g –1, with only 5% degradation after 80 full charge/discharge cycles. Moreover at the relatively fast discharging rates of 200 and 500 mA g –1, the capacities are 1158 and 826 mAh g –1, respectively. A comparison with literature shows that these are among the more promising reversible capacity, cycling capacity, and rate capability values reported for MoO 2 . The electrochemical properties are attributed to the material’s nanoporous crystal morphology that allows for facile reversible transport of Li ions without either disintegration or agglomeration of the structure.

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 score0.004
Threshold uncertainty score0.521

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 head0.206
Teacher spread0.200 · 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

Citations104
Published2014
Admission routes2
Has abstractyes

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