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Record W2800095135 · doi:10.1002/smll.201704367

Pseudocapacitive Sodium Storage by Ferroelectric Sn<sub>2</sub>P<sub>2</sub>S<sub>6</sub> with Layered Nanostructure

2018· article· en· W2800095135 on OpenAlexaff
Sheng Huang, Chao Meng, Min Xiao, Shan Ren, Shuanjin Wang, Dongmei Han, Yuning Li, Yuezhong Meng

Bibliographic record

VenueSmall · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsPseudocapacitanceMaterials scienceElectrochemistryAnodeSodiumLithium (medication)NanostructureNanomaterialsNanotechnologyEnergy storageIntercalation (chemistry)IonFerroelectricityElectrodeChemical engineeringInorganic chemistrySupercapacitorOptoelectronicsMetallurgyChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Sodium ion batteries (SIB) are considered promising alternative candidates for lithium ion batteries (LIB) because of the wide availability and low cost of sodium, therefore the development of alternative sodium storage materials with comparable performance to LIB is urgently desired. The sodium ions with larger sizes resist intercalation or alloying because of slow reaction kinetics. Most pseudocapacitive sodium storage materials are based on subtle nanomaterial engineering, which is difficult for large‐scale production. Here, ferroelectric Sn2P2S6 with layered nanostructure is developed as sodium ion storage material. The ferroelectricity‐enhanced pseudocapacitance of sodium ion in the interlayer spacing makes the electrochemical reaction easier and faster, endowing the Sn2P2S6 electrode with excellent rate capability and cycle stability. Furthermore, the facile solid state reaction synthesis and common electrode fabrication make the Sn2P2S6 that becomes a promising anode material of SIB.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.193
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2018
Admission routes1
Has abstractyes

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