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Record W3025100692 · doi:10.1149/ma2020-014555mtgabs

Rational Design of Protective Film for Long-Life and Stable Lithium Metal Anode Via Molecular Layer Deposition

2020· article· en· W3025100692 on OpenAlexaff
Yipeng Sun, Yang Zhao, Keegan R. Adair, Tsun‐Kong Sham, Andy Xueliang Sun

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAnodeElectrolyteMaterials sciencePassivationAtomic layer depositionElectrochemistryLayer (electronics)NanotechnologyChemical engineeringElectrodeCoatingLithium (medication)Conformal coatingDeposition (geology)Chemistry

Abstract

fetched live from OpenAlex

It is urgent to develop efficient energy storage system for the increasing energy demands of human society. Batteries with high energy density is one of the most promising solutions. Metallic Li anode is believed to be the most promising electrode candidate for high-energy-density batteries due to its ultrahigh capacity and low electrochemical potential [1]. However, it has still not been successfully commercialized because of several great challenges, including poor safety and dendrite growth in plating/stripping process. Highly reactive nature of Li metal anode towards organic electrolyte leads to severe side reactions and prompt the formation of solid electrolyte interphase (SEI). Although SEI can passivate the Li surface and serve as a protective layer on the electrode, it cannot prevent the dendrite growth due to its heterogeneous and unstable properties [2]. In recent years, there are large amount of reported works on stabilizing SEI for Li metal anode via in-situ or ex-situ approach. In-situ approach is realized by optimization of electrolyte using electrolyte additives or changing concentration of Li salt, while ex-situ approach is achieved by building protective layer, such as metal oxides, lithium salts, and organic molecules on Li before electrochemical cycling [3]. However, up to now it still remains very challenging to achieve conformal, stable, and ultrathin layer as ideal SEI for long-term electrochemical cycling. Our group has been dedicated to build effective protective films for high performance Li metal anodes using Atomic layer deposition (ALD) or Molecular layer deposition (MLD). ALD is a widely used coating technique for the deposition of inorganic materials with great advantages including conformal coverage and accurate control over thickness in nanoscale. MLD can be employed to produce inorganic-organic hybrid or pure polymer coatings as an analogue technique of ALD. Therefore, MLD maintained the advantages of ALD and provides more advantages such as tunable composition and improved mechanical properties. Previously reported works from our group includes inorganic metal oxide and inorganic-organic metalcones synthesized by ALD/MLD for improved stability of Li metal anodes [4-5]. In this talk, we report a novel pure polymer film of MLD Polyurea (PU) as protective layer for Li-metal anodes for the first time [6]. The depth profile of PU layer on Li was illustrated by TOF-SIMS and the detailed surface chemistry was investigated by XPS for both the anodes before and after electrochemical cycling. Compared to bare Li, the polyurea coated Li shows highly improved cycle life and stability. Our results showed this electrically nonconductive PU film can effectively suppress the dendrite growth as a protective barrier and remain stable upon the repeated Li plating/stripping process. Meanwhile, the nitrogen-containing polar groups in PU can effectively regulate the Li-ion flux and lead to a uniform Li deposition. Owing to these advantages, the Li metal anode coated with PU layer enables greatly prolonged lifetime in the symmetric cells at different current densities and capacities. The full cells were tested using LiFePO4 (LFP) as the cathode and showed improved capacity retention and rate performance. Therefore, this work sheds new light on the design of protective layer for Li-metal anodes for high-energy-density next-generation batteries. [1] D. Lin, Y. Cui, et al, Nature Nanotechnology , 2017, 12, 194-206 [2] X. Cheng, Q. Zhang, et al, Chemical Reviews, 2017, 117, 10403-10473 [3] X. Cheng, Q. Zhang, et al, Adv. Sci., 2016, 3, 1500213 [4] Y. Zhao, X. Sun et al, Small Methods, 2018, 2, 1700417 [5] K. R. Adair, X. Sun et al, Angew. Chem. Int. Ed., 2019, 58, 15797. [6] Y. Sun, X. Sun, et al, Advanced Materials , 2019, 31, 1806541 Figure 1

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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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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".

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Citations0
Published2020
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