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Record W2902945213 · doi:10.1149/ma2018-02/5/304

Thin Film Surface Coatings for Improved Lithium Metal Cycling Efficiency: A Combinatorial Approach

2018· article· en· W2902945213 on OpenAlexaff
Matthew Genovese, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLithium (medication)AnodeMaterials scienceElectrochemistryElectrolyteElectrodePlating (geology)NanotechnologySpecific energyDeposition (geology)Chemical engineeringMetalMetallurgyChemistry

Abstract

fetched live from OpenAlex

There is a continual push to improve the energy density of lithium-ion batteries, but this is becoming increasingly difficult as it is believed that many of the best electrode materials have been found and optimized. In order to achieve higher energy densities to meet future storage demands it may be necessary to move “beyond lithium-ion”. Recent research efforts have focused on reviving the lithium metal negative electrode as a potential path to higher energy density.[1] With its high theoretical capacity and lowest reduction potential among metals, using lithium as a negative electrode can certainly increase the energy density of a cell provided the large volume changes of the lithium electrode between the charged and discharged states can be handled. In addition, lithium metal anodes often demonstrate dendrite formation during electrodeposition instead of plating as a smooth film. Since conventional organic electrolytes are thermodynamically unstable to reduction by lithium, dendrite growth leads to continual loss of active lithium, resulting in poor cycling efficiencies (< 90%) and short cell lifetime.[2] These problems are exaggerated as the thickness of the plated lithium increases, making cells with industrially relevant material loadings a significant challenge. Recent studies have shown that the morphology and plating efficiency of lithium can be greatly improved with nanostructured surface coatings. [3],[4] Herein we report on the use of combinatorial materials analysis to investigate the effect of different nanostructured surface coatings on the electrochemical deposition of lithium metal. Custom 64-channel electrochemical cell plates were used along with a combinatorial electrochemical cell to provide an efficient means of screening different electrode coatings. Figure 1 shows a schematic of the 64-channel cell plate as well as the output cyclic voltammograms (CVs) from potentiodynamic lithium plating and stripping on 64 unmodified copper electrodes. All 64 channels show consistent output which was validated against conventional coin type lithium half cells. High throughput sputtering was used to make libraries of thin films with different composition, structure, and thickness. These sputtered libraries were deposited directly on the 64-channel cell plates and used to analyze the morphology and coulombic efficiency of lithium plating as a function of the 64 individual surface coatings. The influence of different electrolyte compositions with these surface coatings was also investigated. These results provide perspective regarding the value of different thin film anode surface coatings as part of a broad strategy for achieving high (>98 %) lithium metal cycling efficiencies. [1] X. Cheng et al., Toward safe lithium metal anode in rechargeable batteries: a review, Chemical reviews, 117 (2015), 10403-10473. [2] Q. Pang, et al. "An In Vivo Formed Solid Electrolyte Surface Layer Enables Stable Plating of Li Metal." Joule 1, (2017): 871-886. [3] Adam P. Cohn et al., Anode-Free Sodium Battery through in Situ plating of Sodium Metal, Nano Letters, 17, (2017) 1296-1301. [4] G. Zheng et al., Interconnected hollow carbon nanospheres for stable lithium metal anodes, Nature Nanotechnology 9, (2014), 618-623. 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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.230
Teacher spread0.218 · 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
Published2018
Admission routes1
Has abstractyes

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