MétaCan
Menu
Back to cohort
Record W3024745556 · doi:10.1149/ma2020-014565mtgabs

Na Metal Batteries: Interface Design from Liquid to Solid Systems

2020· article· en· W3024745556 on OpenAlexaff
Shumin Zhang, Yang Zhao, Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAnodeElectrolyteMaterials scienceSulfideIonic liquidEnergy storageElectrochemistryChemical engineeringMetalIonic conductivityNanotechnologyMetallurgyChemistryElectrode

Abstract

fetched live from OpenAlex

With a high demand on advanced energy storage systems, sodium metal batteries (SMBs) are believed to be an emerging and competitive candidate due to the rich resource, low cost, and suitable redox potential of Na metal.1 However, the SEI layer formed in Na carbonate electrolytes is relatively unstable, which leads to nonuniform ionic flux during repetitive Na stripping/plating process as well as low columbic efficiency (CE). In addition to that, the poor SEI layer may result in the growth of mossy or dendritic Na, causing serious safety issues. 2 All-solid-state Na metal batteries (ASSSMBs) equipped with solid-state electrolytes (SSEs) are much reliable for the elimination of safety hazards that liquid electrolytes associate with. In the state-of-the-art SSEs, sulfide-based SSEs exhibit high ionic conductivity, moderate synthetic conditions, low grain boundary resistance, and malleable properties3, thus they are more promising to be actually employed in the next-generation high-performance ASSSMBs. However, these sulfide SSEs can thermodynamically react with metal anode to form a mixed ionic and electronic conducting interface, continuously depleting both the SSE and Na anode.4 Meanwhile, the resultant large polarization can lead to the growth of interfacial resistance and deterioration of battery performance. Similarly with that in liquid system, uneven Na+ deposition also occurr at the sulfide SSE/Na anode interface, causing the formation of dendrites and short circuits of the batteries.4 As such, the electrolyte/Na anode incompatibility is the priority to be solved for realizing high-performance SMBs. Even though some effective approaches have been reported to address this issue, the developments of SMBs is still in its infancy. Herein, we demonstrate the inorganic−organic coating via advanced molecular layer deposition (MLD) 5, 6 as a protective layer for metallic Na anode in SMBs. By protecting Na anode with controllable alucone layer, the dendrites and mossy Na formation are effectively suppressed and the lifetime of liquid-state battery has been significantly improved.7 Moreover, we extend this alucone film in ASSSMBs to stabilize the active Na anode/electrolyte interface, limiting the decomposition of the sulfide based electrolyte (Na3SbS4 and Na3PS4) and Na dendrite growth. Protected by this film, full battery performance is remarkably improved and Na-Na symmetric cells are stabilized nearly 500 hours at room temperature. The modified interface is further characterized by X-ray photoelectron spectroscopy (XPS) depth profiling, which provides spatially resolved evidence of the synergistic effect between the dendrite-suppressed sodiated alucone and the insulating unsodiated alucone. Such coupled layers reinforce the protection of the Na metal/electrolyte interface.8 Our works identify alucone as an effective and bi-functional coating material as its derivatives can stabilize the metal/electrolyte interface, paving the way for rapid development and wide utilization of SMBs. References: 1. Han, X. G.; Gong, Y. H.; Fu, K.; He, X. F.; Hitz, G. T.; Dai, J. Q.; Pearse, A.; Liu, B. Y.; Wang, H.; Rublo, G.; Mo, Y. F.; Thangadurai, V.; Wachsman, E. D.; Hu, L. B. Nat Mater 2017, 16, (5), 572-579. 2. Lee, B.; Paek, E.; Mitlin, D.; Lee, S. W. Chem Rev 2019, 119, (8), 5416-5460. 3. Tian, Y. S.; Sun, Y. Z.; Hannah, D. C.; Xiao, Y. H.; Liu, H.; Chapman, K. W.; Bo, S. H.; Ceder, G. Joule 2019, 3, (4), 1037-1050. 4. Wang, Y.; Richards, W. D.; Bo, S. H.; Miara, L. J.; Ceder, G. Chemistry of Materials 2017, 29, (17), 7475-7482. 5. Zhao, Y.; Adair, K. R.; Sun, X. L. Energy & Environmental Science 2018, 11, (10), 2673-2695. 6. Zhao, Y.; Sun, X. L. Acs Energy Lett 2018, 3, (4), 899-914. 7. Zhao, Y.; Goncharova, L. V.; Zhang, Q.; Kaghazchi, P.; Sun, Q.; Lushington, A.; Wang, B. Q.; Li, R. Y.; Sun, X. L. Nano Lett 2017, 17, (9), 5653-5659. 8. Zhang, S. M; Zhao, Y.; Zhao, F. P; Zhang, L.; Wang, C. H; Davis, K.; Li, X.N ; Liang, J.W ; Li, W.H ; Li, R. Y; Sham, T K.; Sun, X.L. 2019, submitted. Figure 1

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.027
GPT teacher head0.240
Teacher spread0.212 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207