<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>7562</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>777</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>11</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2020-02-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique</title>
    <abstract language="eng">The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the ‘unrolling’ provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.</abstract>
    <parentTitle language="eng">Nature Communications</parentTitle>
    <identifier type="doi">10.1038/s41467-019-13943-3</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2019-12-04</enrichment>
    <author>Ralf F. Ziesche</author>
    <submitter>Daniel Baum</submitter>
    <author>Tobias Arlt</author>
    <author>Donal P. Finegan</author>
    <author>Thomas M.M. Heenan</author>
    <author>Alessandro Tengattini</author>
    <author>Daniel Baum</author>
    <author>Nikolay Kardjilov</author>
    <author>Henning Markötter</author>
    <author>Ingo Manke</author>
    <author>Winfried Kockelmann</author>
    <author>Dan J.L. Brett</author>
    <author>Paul R. Shearing</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="institutes" number="vissys">Image Analysis in Biology and Materials Science</collection>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="projects" number="PAPYRUS">PAPYRUS</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
