MétaCan
Menu
Back to cohort
Record W3153518818 · doi:10.1039/d1dt00401h

A MoSe<sub>2</sub>/N-doped hollow carbon sphere host for rechargeable Na–Se batteries

2021· article· en· W3153518818 on OpenAlexaff
Fengping Xiao, Yanni Wu, Qing Tang, Nilesh Shinde, Yulong Liu

Bibliographic record

VenueDalton Transactions · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsWestern University
Fundersnot available
KeywordsDopingMaterials scienceCarbon fibersComposite numberHost (biology)SPHERESNanotechnologyPorositySodiumChemical engineeringOptoelectronicsPhysicsComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Sodium-selenium (Na-Se) batteries are promising alternatives to lithium-ion batteries for energy storage systems owing to their high energy density and natural abundance of Na resources. However, their drawbacks of low Se loading, dissolution of intermediate sodium polyselenides in the electrolyte and volumetric expansion of Se impede their real applications. To address these issues, herein, we report a multifunctional Se host with MoSe2 nanosheets coupled with nitrogen-doped porous carbon hollow spheres for the first time. The N-doped hollow carbon sphere structure could provide a large space for Se loading (Se content up to 72 wt%) and accommodate the volume expansion of Se species upon cycling. MoSe2 was chosen as a polar coupling component for the carbon matrix, owing to its low conversion reaction voltage. Based on density functional theory (DFT) calculations, the MoSe2 nanosheets coupled with hollow spheres could enhance the adsorption energy of the host to polyselenides chemically, which benefits the immobilization of polyselenides. Therefore, as a cathode for Na-Se batteries, the as-prepared composite exhibits excellent energy storage performance with long cycling life and superior rate performance. Our study of introducing transition metal selenides into Na-Se batteries may stimulate the designing of diverse Se-based cathodes.

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)
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 score0.272
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)0.0010.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDalton TransactionsSame topicAdvancements in Battery MaterialsFrench-language works237,207