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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 Sn 2 P 2 S 6 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 Sn 2 P 2 S 6 electrode with excellent rate capability and cycle stability. Furthermore, the facile solid state reaction synthesis and common electrode fabrication make the Sn 2 P 2 S 6 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 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 score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
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 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

Citations47
Published2018
Admission routes1
Has abstractyes

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