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Record W4409675593 · doi:10.1021/acssuschemeng.5c00735

Bifunctional Electrolyte Additive in Room-Temperature Sodium–Sulfur Batteries

2025· article· en· W4409675593 on OpenAlexaff
Guochao Sun, Suwan Lu, Jiangyan Xue, Shiqi Zhang, Jiawei Zhao, Yang Liu, Haifeng Tu, Shixiao Weng, Lingwang Liu, Yiwen Gao, Keyang Peng, Xin Zhang, Dejun Li, Jingjing Xu, Xiaodong Wu

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsL'Alliance Boviteq
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsBifunctionalElectrolyteSulfurSodiumInorganic chemistryChemistryMaterials scienceCatalysisOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Room-temperature sodium–sulfur (RT Na–S) batteries have been restricted by difficulties on both electrodes: the utilization of active sulfur still falls short of expectations, and the Na anode suffers from dendrite formation and poor interface stability. Although sulfide solid electrolytes can address these issues, undesirable side reactions pose difficulties in solid–solid interfacial contact. Inspired by this, a sulfide (P 2 S 5 /Na 2 S)-containing electrolyte was employed. Utilizing liquid–solid reactions not only increases battery capacity by forming a tightly connected active material with the Cu current collector but also generates a uniform passivation layer on the Na anode, stabilizing the electrode interface. This dual-functional mechanism leads to unprecedented performance, retaining a capacity of 856 mAh g –1 after 900 cycles at 1C, with an average Coulombic efficiency of 99.6%. The work introduces a subtle strategy, provides insights into the mechanism of the P 2 S 5 /Na 2 S additive, and demonstrates the feasibility of this approach for improving the performance of RT Na–S batteries.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.001

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 head0.002
GPT teacher head0.171
Teacher spread0.169 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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