Probing a battery electrolyte drop with ambient pressure photoelectron spectroscopy

Maibach, Julia; Källquist, Ida; Andersson, Margaretha; Urpelainen, Samuli; Edström, Kristina; Rensmo, Håkan; Siegbahn, Hans; Hahlin, Maria

doi:10.1038/s41467-019-10803-y

Cited by 44 publications

(64 citation statements)

References 45 publications

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“…High-resolution P 2p and F 1s spectra (cf. Figure S1, Supplementary Material) have only single peaks at 687 eV and 137 eV corresponding to LixPFy species as reported previously for LiPF6 [16,27,36,37]. The absence of other F 1s and P 2p peaks at lower binding energies, typically related to decomposition products like LiF, LixPOyFz, or other phosphates, indicate mostly intact LiPF6-anions in solution.…”

Section: Lipf 6 (Commercial) Electrolyte-v 2 O 5 Single Crystal In Sosupporting

confidence: 71%

“…Binding O 1s spectra of the gas phase are more complex due to additional contributions from residual molecular oxygen and water next to the oxygen atoms of EC and DMC. As shown in Figure 2a the O 1s core-level spectrum can be fitted very well with 5 components with three components at lower biding energy that can be assigned to H-O-H (g) from water vapor and the oxygen atoms O=C (g) and O-C (g) from EC/DMC [16,[27][28][29], these atoms are labeled with 1 and 2 in their chemical structures (cf. insets in Figure 2a).…”

Section: Peak Componentmentioning

confidence: 82%

“…Figure 2a shows the corresponding C 1s and O 1s core-level spectra of the gas phase. The C 1s consist of peaks that can be assigned to C-O (g) and CO 3 (g) moieties of ethylene carbonate (EC) and dimethyl carbonate (DMC) [16,[27][28][29]. Both moieties are labeled with 1 and 2 in their chemical structures respectively (cf.…”

Section: Lipf 6 (Commercial) Electrolyte-vapor Only and Vapor-liquid MIXmentioning

confidence: 99%

“…After these encouraging experiments, we were able to relate the results of the LiPF 6 electrolyte with data of the LiTFSI model electrolyte, which was previously analyzed with synchrotron and in-house NAP-XPS [16]. As we used the same setup for both liquids a direct comparison of the high-resolution core-level spectra from the LiTFSI and LiPF 6 electrolyte is possible.…”

Section: Comparison Of Litfsi (Model) and Lipf 6 (Commercial) Electromentioning

confidence: 99%

“…Previous work using both synchrotron and in-house NAP-XPS setups probing an electrolyte drop on a Li-metal substrate showed that the salt concentration varied within that drop. At the surface of the liquid, an almost twofold increase in salt concentration was observed [16].…”

Section: Introductionmentioning

confidence: 99%

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Probing Lithium-Ion Battery Electrolytes with Laboratory Near-Ambient Pressure XPS

Dietrich¹,

Gehrlein

Maibach

et al. 2020

Crystals

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In this article, we present Near Ambient Pressure (NAP)-X-ray Photoelectron Spectroscopy (XPS) results from model and commercial liquid electrolytes for lithium-ion battery production using an automated laboratory NAP-XPS system. The electrolyte solutions were (i) LiPF6 in EC/DMC (LP30) as a typical commercial battery electrolyte and (ii) LiTFSI in PC as a model electrolyte. We analyzed the LP30 electrolyte solution, first in its vapor and liquid phase to compare individual core-level spectra. In a second step, we immersed a V2O5 crystal as a model cathode material in this LiPF6 solution. Additionally, the LiTFSI electrolyte model system was studied to compare and verify our findings with previous NAP-XPS data. Photoelectron spectra recorded at pressures of 2–10 mbar show significant chemical differences for the different lithium-based electrolytes. We show the enormous potential of laboratory NAP-XPS instruments for investigations of solid-liquid interfaces in electrochemical energy storage systems at elevated pressures and illustrate the simplicity and ease of the used experimental setup (EnviroESCA).

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Section: Lipf 6 (Commercial) Electrolyte-v 2 O 5 Single Crystal In Sosupporting

confidence: 71%

Section: Peak Componentmentioning

confidence: 82%

Section: Lipf 6 (Commercial) Electrolyte-vapor Only and Vapor-liquid MIXmentioning

confidence: 99%

Section: Comparison Of Litfsi (Model) and Lipf 6 (Commercial) Electromentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Probing Lithium-Ion Battery Electrolytes with Laboratory Near-Ambient Pressure XPS

Dietrich¹,

Gehrlein

Maibach

et al. 2020

Crystals

Self Cite

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Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Vegge¹,

Tarascon

Edström

2021

Advanced Energy Materials

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With an exponentially growing demand for rechargeable batteries, the development of new ultra‐performant, fully scalable, and sustainable battery technologies and materials must be accelerated. Creating a holistic, closed‐loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. By embedding multisensory and self‐healing capabilities in future battery technologies and integrating these with AI and physics‐aware machine learning models capable of predicting the spatio‐temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, for example, by preemptively changing the battery charging conditions or releasing self‐healing additives from the separator membrane, akin to preemptive medicine, and form the basis for inverse design of new battery materials, interfaces, and additives. The large‐scale and long‐term European research initiative BATTERY 2030+ seeks to make this longer‐than ten‐year vision a reality through the development of a versatile and chemistry neutral “Battery Interface Genome—Materials Acceleration Platform” infrastructure (BIG‐MAP).

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Understanding Battery Interfaces by Combined Characterization and Simulation Approaches: Challenges and Perspectives

Atkins

Ayerbe

Benayad

et al. 2021

Advanced Energy Materials

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Driven by the continuous search for improving performances, understanding the phenomena at the electrode/electrolyte interfaces has become an overriding factor for the success of sustainable and efficient battery technologies for mobile and stationary applications. Toward this goal, rapid advances have been made regarding simulations/modeling techniques and characterization approaches, including high‐throughput electrochemical measurements coupled with spectroscopies. Focusing on Li‐ion batteries, current developments are analyzed in the field as well as future challenges in order to gain a full description of interfacial processes across multiple length/timescales; from charge transfer to migration/diffusion properties and interphases formation, up to and including their stability over the entire battery lifetime. For such complex and interrelated phenomena, developing a unified workflow intimately combining the ensemble of these techniques will be critical to unlocking their full investigative potential. For this paradigm shift in battery design to become reality, it necessitates the implementation of research standards and protocols, underlining the importance of a concerted approach across the community. With this in mind, major collaborative initiatives gathering complementary strengths and skills will be fundamental if societal and environmental imperatives in this domain are to be met.

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Probing a battery electrolyte drop with ambient pressure photoelectron spectroscopy

Cited by 44 publications

References 45 publications

Probing Lithium-Ion Battery Electrolytes with Laboratory Near-Ambient Pressure XPS

Probing Lithium-Ion Battery Electrolytes with Laboratory Near-Ambient Pressure XPS

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Understanding Battery Interfaces by Combined Characterization and Simulation Approaches: Challenges and Perspectives

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