Dataset Information

Electron paramagnetic resonance imaging for real-time monitoring of Li-ion batteries.

ABSTRACT: Batteries for electrical storage are central to any future alternative energy paradigm. The ability to probe the redox mechanisms occurring at electrodes during their operation is essential to improve battery performances. Here we present the first report on Electron Paramagnetic Resonance operando spectroscopy and in situ imaging of a Li-ion battery using Li2Ru0.75Sn0.25O3, a high-capacity (>270?mAh?g(-1)) Li-rich layered oxide, as positive electrode. By monitoring operando the electron paramagnetic resonance signals of Ru(5+) and paramagnetic oxygen species, we unambiguously prove the formation of reversible (O2)(n-) species that contribute to their high capacity. In addition, we visualize by imaging with micrometric resolution the plating/stripping of Li at the negative electrode and highlight the zones of nucleation and growth of Ru(5+)/oxygen species at the positive electrode. This efficient way to locate 'electron'-related phenomena opens a new area in the field of battery characterization that should enable future breakthroughs in battery research.

SUBMITTER: Sathiya M

PROVIDER: S-EPMC4347297 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Electron paramagnetic resonance imaging for real-time monitoring of Li-ion batteries.

Sathiya M M Leriche J-B JB Salager E E Gourier D D Tarascon J-M JM Vezin H H

Nature communications 20150209

Batteries for electrical storage are central to any future alternative energy paradigm. The ability to probe the redox mechanisms occurring at electrodes during their operation is essential to improve battery performances. Here we present the first report on Electron Paramagnetic Resonance operando spectroscopy and in situ imaging of a Li-ion battery using Li2Ru0.75Sn0.25O3, a high-capacity (>270 mAh g(-1)) Li-rich layered oxide, as positive electrode. By monitoring operando the electron paramag ...[more]

PMID: 25662295

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Project description:Electron paramagnetic resonance (EPR) imaging has evolved as a promising tool to provide non-invasive assessment of tissue oxygenation levels. Due to the extremely short T2 relaxation time of electrons, single point imaging (SPI) is used in EPRI, limiting achievable spatial and temporal resolution. This presents a problem when attempting to measure changes in hypoxic state. In order to capture oxygen variation in hypoxic tissues and localize cycling hypoxia regions, an accelerated EPRI imaging method with minimal loss of information is needed.We present an image acceleration technique, partial Fourier compressed sensing (PFCS), that combines compressed sensing (CS) and partial Fourier reconstruction. PFCS augments the original CS equation using conjugate symmetry information for missing measurements. To further improve image quality in order to reconstruct low-resolution EPRI images, a projection onto convex sets (POCS)-based phase map and a spherical-sampling mask are used in the reconstruction process. The PFCS technique was used in phantoms and in vivo SCC7 tumor mice to evaluate image quality and accuracy in estimating O2 concentration.In both phantom and in vivo experiments, PFCS demonstrated the ability to reconstruct images more accurately with at least a 4-fold acceleration compared to traditional CS. Meanwhile, PFCS is able to better preserve the distinct spatial pattern in a phantom with a spatial resolution of 0.6mm. On phantoms containing Oxo63 solution with different oxygen concentrations, PFCS reconstructed linewidth maps that were discriminative of different O2 concentrations. Moreover, PFCS reconstruction of partially sampled data provided a better discrimination of hypoxic and oxygenated regions in a leg tumor compared to traditional CS reconstructed images.EPR images with an acceleration factor of four are feasible using PFCS with reasonable assessment of tissue oxygenation. The technique can greatly enhance EPR applications and improve our understanding cycling hypoxia. Moreover this technique can be easily extended to various MRI applications.

Dataset Information

Electron paramagnetic resonance imaging for real-time monitoring of Li-ion batteries.

Publications

Electron paramagnetic resonance imaging for real-time monitoring of Li-ion batteries.

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