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High-Resolution Mapping of Discharge Product in Li─O2 Batteries.

Accepted version
Peer-reviewed

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Abstract

Lithium-oxygen (Li─O2) batteries have the highest theoretical specific energy of any chemical battery (∼3500 Wh kg-1). However, their practical capacity is usually lower than theoretically expected, particularly at higher rates of discharge. Air electrode surface passivation and slow O2 mass transport through the porous electrode structure are the primary limitations on Li─O2 discharge capacity. To determine which of these factors is most significant, it is necessary to spatially determine the relative utilisation of different portions of the electrode. Here, we demonstrate how cross-sectional scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) can be used to semi-quantitatively map the discharge product distribution within Li─O2 air electrodes. The distribution shows increased discharge product accumulation at the O2-side of the electrode, confirming that pore blockage and oxygen starvation are key factors leading to under-utilisation of the air electrode in Li─O2 cells. Simulated Li─O2 battery results align with the characterised discharge product distribution, lending validation to the model. Lab-based X-ray nano-computed tomography corroborates the SEM-EDS data to ensure that sample preparation had minimal effect on the measured distribution. Collectively, these techniques can be used to determine the cause of performance changes due to different discharge conditions, alternative electrolyte compositions, or electrode structure.

Description

Journal Title

Small Methods

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Journal ISSN

2366-9608
2366-9608

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Publisher

Wiley

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Except where otherwised noted, this item's license is described as Attribution 4.0 International
Sponsorship
Engineering and Physical Sciences Research Council (EP/S022953/1)
European Commission Horizon 2020 (H2020) ERC (866005)
This research was funded by the EPSRC via the NanoDTC, grant no. EP/S022953/1, and by Cambridge Display Technologies Ltd. M.M. and E.M. acknowledge funding from the French Agency for national Research (Agence Nationale pour la Recherche). Project name: Dynamobat, reference : ANR-22-CE42-0025. M.D.V. acknowledges funding from ERC (Consolidator Grant, MIGHTY, 866005)

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