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Record W4385071712 · doi:10.1093/micmic/ozad067.683

Understanding the Origin of Lithiophilicity Toward Molten Li-Metal Using <i>In-situ</i> Scanning Electron Microscopy (SEM)

2023· article· en· W4385071712 on OpenAlexaff
Shirin Kaboli, Wen Zhu, Daniel Clément, Martin Dontigny, Frédéric Gendron, Kamyab Amouzegar, Ashok K. Vijh, Abdelbast Guerfi, Michel L. Trudeau, Andrea Paolella

Bibliographic record

VenueMicroscopy and Microanalysis · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectrificationExcellenceCenter of excellenceEngineering physicsLibrary sciencePolitical scienceManagementEngineeringElectrical engineeringComputer scienceLawElectricityEconomics

Abstract

fetched live from OpenAlex

Maintaining a uniform Li deposition at the anode electrode/electrolyte interface is a fundamental challenge during extended cycling of solid-state Li batteries. The reactivity of the Li metal at the current collector surface remains a principal parameter for battery optimization. It is known that a bare Cu foil current collector substrate does not have a good lithiophilic surface to deposit molten Li metal because the Cu surface’s wettability by a Li metal melt is very poor, requiring a metal or metal oxide interlayer to improve wetting [1, 2]. Zn is considered one of the most promising lithiophilic elements. In this work, we used in-situ scanning electron microscopy (SEM) to study the interfacial reactions between molten Li metal and Cu foil current collector modified by a thin Zn film (Cu@Zn) to understand the origin of lithiophilicity. We performed in-situ heating tests on a Cu foil substrate coated with a 50 nm Zn interlayer (Cu@Zn) in contact with a Li metal foil in SEM. We present experimental heating and cooling set ups for in-situ Li study inside a high vacuum SEM. We present our observations of the reaction between the molten Li metal and Cu surface during Li solidification in real time as well as post-mortem energy dispersive spectroscopy (EDS) and grazing incidence X-ray diffraction (GIXRD) on the coatings to analyze the chemistry of the reaction products. For the first time in literature, our in-situ study shows the existence of a metastable ternary Li-Cu-Zn alloy at 300 °C after the reaction of Cu@Zn with the molten Li metal. We report the segregation of Cu and Zn by formation of Cu-Zn intermetallic compounds during the cooling down step. The schematic of the experimental set up for in-situ heating tests inside the SEM in Figure 1 shows the in-situ SEM observation (top view) of Li metal wettability of a Cu@50nm (nominal thickness) Zn foil substrate in contact with a molten Li metal at 300 °C. We observed the wettability of the Cu@Zn foil near the edges of the Li metal foil where the molten Li metal contacted the substrate. Subsequently, the in-situ SEM observation (top view) of Li metastability showed formation of secondary phase particles on the Li surface during Li solidification. Figure 2A-B show the top view snapshots of the Cu@Zn and metallic Li surface observed in in-situ settings. The Li surface microstructure of the same area (indicated by the red circle in Figure 2B) at 300 °C and after cool down to RT are shown in Figure 2C-D. We will discuss these results in the context of the understanding the chemical reactivity of molten Li toward metal foils. Utilizing in-situ SEM is pivotal to clarify the interfacial reactions occurring between a lithiophilic current collector and molten Li metal and have utmost importance for designing advanced new anode materials for future solid-state battery applications. Schematic of the in-situ heating set up inside the scanning electron microscope (SEM). Top view snapshot of the Cu@Zn foil substrate (A), top view snapshot of the metallic Li foil (B), the Li surface microstructure at 300 °C (C) and after cool down to RT (D). The backscattered electron (BSE) images in (C-D) were taken from the same area indicated in red in (B) at the same magnification.

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)0.001
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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