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Record W2900940509 · doi:10.1002/aenm.201802235

Solid‐State Lithium/Selenium–Sulfur Chemistry Enabled via a Robust Solid‐Electrolyte Interphase

2018· article· en· W2900940509 on OpenAlexfundno aff
Gui‐Liang Xu, Hui Sun, Chao Luo, Luis Estevez, Minghao Zhuang, Han Gao, Rachid Amine, Hao Wang, Xiaoyi Zhang, Cheng‐Jun Sun, Yuzi Liu, Yang Ren, Steve M. Heald, Chunsheng Wang, Zonghai Chen, Khalil Amine

Bibliographic record

VenueAdvanced Energy Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
FundersArgonne National LaboratoryBasic Energy SciencesCanadian Light SourceVehicle Technologies OfficeOffice of ScienceU.S. Department of Energy
KeywordsElectrolytePolysulfideCathodeMaterials scienceMesoporous materialSulfurChemical engineeringLithium–sulfur batteryElectrodeAnodeFast ion conductorInorganic chemistryNanotechnologyChemistryCatalysisPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lithium/selenium‐sulfur batteries have recently received considerable attention due to their relatively high specific capacities and high electronic conductivity. Different from the traditional encapsulation strategy for suppressing the shuttle effect, an alternative approach to directly bypass polysulfide/polyselenide formation via rational solid‐electrolyte interphase (SEI) design is demonstrated. It is found that the robust SEI layer that in situ forms during charge/discharge via interplay between rational cathode design and optimal electrolytes could enable solid‐state (de)lithiation chemistry for selenium‐sulfur cathodes. Hence, Se‐doped S 22.2 Se/Ketjenblack cathodes can attain a high reversible capacity with minimal shuttle effects during long‐term and high rate cycling. Moreover, the underlying solid‐state (de)lithiation mechanism, as evidenced by in situ 7 Li NMR and in operando synchrotron X‐ray probes, further extends the optimal sulfur confinement pore size to large mesopores and even macropores that have been long considered as inferior sulfur or selenium host materials, which play a crucial role in developing high volumetric energy density batteries. It is expected that the findings in this study will ignite more efforts to tailor the compositional/structure characteristics of the SEI layers and the related ionic transport across the interface by electrode structure, electrolyte solvent, and electrolyte additive screening.

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 score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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

Citations82
Published2018
Admission routes1
Has abstractyes

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