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Record W3142766588 · doi:10.1021/acsnano.0c10078

An Emerging Energy Storage System: Advanced Na–Se Batteries

2021· article· en· W3142766588 on OpenAlexaff
Xiang Huang, Chaofu Zhou, Weidong He, Shuhui Sun, Yu‐Lun Chueh, Zhiming Wang, Huan Liu, Shi Xue Dou

Bibliographic record

VenueACS Nano · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersState Administration of Foreign Experts AffairsMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of China
KeywordsMaterials scienceElectrochemistryEnergy storageBattery (electricity)CathodeNanotechnologyCommercializationSodium-ion batteryElectrochemical energy storageElectrodeSupercapacitorElectrical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Sodium–selenium (Na–Se) batteries have aroused enormous attention due to the large abundance of the element sodium as well as the high electronic conductivity and volumetric capacity of selenium. In this battery system, some primary advances in electrode materials have been achieved, mainly involving the design of Se-based cathode materials. In this Review, the electrochemical mechanism is discussed, thus revealing the main challenges in Na–Se batteries. Then, the advances in the design of Se-based cathode materials for Na-ion storage are systemically summarized, classified, and discussed, including Se/carbon composite, Se/polar material/carbon composites, and hybrid SexSy alloys. Some potential strategies enabling the improvement of crucial challenges and enhancement of electrochemical performance are also proposed to provide guidelines for the enhancements of Na-ion storage. An outlook for future valuable research directions is proposed to understand more deeply the electrochemical mechanism of Na–Se batteries and promote their further developments in full cell performance and commercialization.

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 score0.045
Threshold uncertainty score0.892

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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

Citations102
Published2021
Admission routes1
Has abstractyes

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