4 research outputs found

    Supercapacitor Degradation: Understanding Mechanisms of Cycling-Induced Deterioration and Failure of a Pseudocapacitor

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    Owing to a reputation for long lifetimes and excellent cycle stability, degradation in supercapacitors has largely been overlooked. In this work, we demonstrate that significant degradation in some commercial supercapacitors can in fact occur early in their life, leading to a rapid loss in capacitance, especially when utilized in full voltage range, high charge-discharge frequency applications. By using a commercial 300 F lithium-ion pseudocapacitor rated for 100,000 charge/discharge cycles as an example system, it is shown that a ∼96 % loss in capacitance over the first ∼2000 cycles is caused by significant structural and chemical change in the cathode active material (LiMn2O4, LMO). Multi-scale in-situ and ex-situ characterization, using a combination of X-ray computed tomography, Raman spectroscopy and X-ray photoelectron spectroscopy, shows that while minimal material loss (∼5.5 %), attributed to the dissolution of Mn2+, is observed, the primary mode of degradation is due to manganese charge disproportionation (Mn3+→Mn4++Mn2+) and its physical consequences (i. e. microstrain formation, particle fragmentation, loss of conductivity etc.). In contrast to prior understanding of LMO material degradation in battery systems, negligible contributions from cubic-to-tetragonal phase transitions are observed. Hence, as supercapacitors are becoming more widely utilized in real-world applications, this work demonstrates that it is vital to understand the mechanisms by which this family of devices change during their lifetimes, not just for lithium-ion pseudocapacitors, but for a wide range of commercial chemistries

    Ingress of Li into Solid Electrolytes: Cracking and Sparsely Filled Cracks

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    Multi-Dimensional Characterization of Battery Materials

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    Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X-ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi-modal microscopy in a correlative fashion, providing contrast modes spanning length- and time-scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments
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