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Separating Initial Loss from Storage Capacity: Core-Shell Materials as Advanced Anode Materials for Sodium Ion Batteries

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The current imperative to shift towards an energy grid equipped with sustainable energy storage solutions has caused a renewed interest in sodium-ion batteries (SIBs). Hard carbons (HCs) are a promising option high-capacity anode materials in SIBs. Nevertheless, their elevated capacities frequently come at the cost of experiencing substantial non-reversible initial capacity losses.[1, 2]
Commonly, significant losses are associated with irreversible reactions, such as the creation of the solid electrolyte interphase (SEI), that occur during the initial sodium insertion in HC-materials. Intriguingly, high values of irreversible capacity are often found for samples with experimentally determined low specific surface area.[1] A more comprehensive understanding of the structure-property relations is essential for quantifying and grasping the potential of hard carbon materials in sodium-ion batteries (SIBs). Thus, the objective is to employ analytical methods to establish a link between the structure and the electrochemical attributes of HC materials. This has been a challenge, partly due to the non-stoichiometric nature of the sodium storage mechanism and the disordered structure of HCs.
To address the challenges mentioned above, our approach is to explore whether a core-shell structure can separate sodium storage and SEI-formation. This way, we can investigate and fine-tune storage capacity and irreversible losses, independently. The strategy involves the synthesis of various porous carbon structures to serve as the core material and their combination with sodium-conductive structures to core-shell materials. Herein, we will present different synthesis routes towards tailor-made carbon core materials. Moreover, different coatings concepts will be introduced, and the electrochemical performance of the core and core-shell materials compared.
Generally, these core-shell anodes promise to enable high capacities accompanied with low irreversible losses due to optimized SEI-formation.

Literature:
[1] Y. Matsukawa, F. Linsenmann, M. A. Plass, G. Hasegawa, K. Hayashi, and T.-P. Fellinger, Beilstein J. Nanotechnol. 2020, 11, 1217.
[2] A. Mehmood , G. Ali, B. Koyutürk, J. Pampel, K. Y. Chung, and T.-P. Fellinger, Energy Storage Materials 2020, 28, 101.

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Poster



TESCAN GmbH
Zum Lonnenhohl 46
44319 Dortmund

https://info.tescan.com/batteries

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Datum_UhrzeitPosterTitleAutorCompanyCategory
10/4/2024/TESCAN GmbH
Zum Lonnenhohl 46
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https://info.tescan.com/batteries
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Co-Autoren:
Sebastian Schuhmann, Jens Tübke, Hermann Nirschl
Karlsruher Institut für TechnologiePack Level 2: Pack Safety
11.04.2024/13:553CComparison of Internal Short-Circuit diagnosis methods with synthetic and experimental dataMSc Jon Perez
Co-Autoren:
Mikel Arrinda, Mikel Oyarbide, Haritz Macicior, Erik Garayalde, Unai Iraola
Fundación CIDETECPack Level 2: Pack Safety
11.04.2024/14:153CSafety Assessment of Lithium Plating in Lithium-Ion Batteries for Use in Second-Life ApplicationsMax Feinauer
Co-Autoren:
Dr. Gabriela Gerosa, Michael Wörz, Margret Wohlfahrt-Mehrens, Olaf Böse, Thomas Waldmann
Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (ZSW)Pack Level 2: Pack Safety
11.04.2024/15:354AOperando and In Situ Study on the Lithium Redistribution within Silicon-Graphite Composite Electrodes in Li-Ion Full CellsChristin Hogrefe
Co-Autoren:
Neelima Paul (MLZ), Lioba Boveleth (DLR, HIU), Marius Bolsinger (HSAA), Marius Flügel, Timo Danner (DLR, HIU), Arnulf Latz (DLR, HIU), Ralph Gilles (MLZ), Volker Knoblauch (HSAA), Margret Wohlfahrt-Mehrens, Markus Hölzle, Thomas Waldmann
Zentrum für Sonnenenergie- und Wasserstoff-Forschung (ZSW)Material Ledvel 3: Li-Ion Batteries
11.04.2024/15:554AAn investigation of the cathode electrolyte interphase (CEI) formation of Ni-rich layered materials by Ni ion catalyzation: monolayer CEI formation from an oligomerPhD Fu-Ming WangNational Taiwan University of Science and TechnologyMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/16:154AUnderstanding and Developing High Voltage Li-Ion Batteries via Commercial ElectrolytesDr. Johannes KasnatscheewUniversität MünsterMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/16:354AMachine Learning-guided Optimization of the Ionic Conductivity with the Liquid Electrolyte Composition Analysis (LECA) packageMSc Mirko Fischer
Co-Autoren:
Harrison Martin, Moumita Maiti, Anand Narayanan Krishnamoorthy, Diddo Diddens, Andreas Heuer, Peng Yan, Christian Wölke, Isidora Cekic-Laskovic
Universität MünsterMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/15:354BInfluence of Process Parameters in Direct Aqueous Recycling of NMC811 CathodesFelix Nagler
Co-Autoren:
Nino Christian, Leonhard Kolb, Philip Daubinger, Andreas Flegler, Michael Hofmann, Guinevere A. Giffin
Fraunhofer ISCLife Cycle 2: Recycling
11.04.2024/15:554BInfluences on the properties of black mass from mechanical lithium-ion battery recyclingChristian Wilke
Co-Autoren:
Christian Wilke, Alexandra Kaas, Denis Werner, Jannik Born, Harald Zetzener, Arno Kwade, Urs A. Peuker
TU Bergakademie FreibergLife Cycle 2: Recycling
11.04.2024/16:154BDirect Recycling of Ni-rich Layered Oxide-based Cathodes from Spent Lithium Ion BatteriesMaike Michelle Gnutzmann
Co-Autoren:
Ardavan Makvandi, Julius Buchmann, Bianca Helm, Aurora Gomez-Martin, Martin Winter, Johannes Kasnatscheew
Universität MünsterLife Cycle 2: Recycling
11.04.2024/16:354BEffects of early lithium removal on the hydro- and pyrometallurgical treatment of battery black mass – A general process description from a technical point of viewAlexander Nickol
Co-Autoren:
Hans-Jürgen Friedrich, Prof. Alexander Michaelis
Fraunhofer IKTSLife Cycle 2: Recycling
11.04.2024/15:354CMachine learning-based fast charging of lithium-ion batteries by monitoring and controlling internal physical statesDr.-Ing. Weihan Li
Co-Autoren:
Dirk Uwe Sauer
RWTH Aachen University | ISEAPack Level 3 - Modelling & Machine learning
11.04.2024/15:554CModeling the change of the open-circuit voltage curve of commercial lithium-ion batteries during agingM.Sc. Alexander Karger
Co-Autoren:
Julius Schmitt, Cedric Kirst, Jan P. Singer, Leo Wildfeuer, Andreas Jossen
Technische Universität MünchenPack Level 3 - Modelling & Machine learning
11.04.2024/16:154CSegregating the Degradation Mechanisms in Lithium-Ion Cells during Strongly Accelerated Cyclic Ageing with Critically Extended Voltage RangesM.Sc. Philippa Scharpmann
Co-Autoren:
Robert Leonhardt, Tim Tichter, Anita Schmidt, Julia Kowal, Jonas Krug von Nidda
Bundesanstalt für Materialforschung und -prüfungPack Level 3 - Modelling & Machine learning
11.04.2024/16:354CEnhancing Battery State-of-Health Estimation with Transfer Learning: Leveraging Voltage Window Features and Combining Synthetic and Real Cell Data in FCNN ModelsMarkel Azkue
Co-Autoren:
I. Gandiaga, S. Bockrath, E. Miguel, L. Oca, U. Iraola
IkerlanPack Level 3 - Modelling & Machine learning