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Register a new userRapid online solid-state battery diagnostics with optically pumped magnetometers
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Solid state battery technology is motivated by the desire to deliver flexible power storage in a safe and efficient manner. The increasingly widespread use of batteries from mass-production facilities highlights the need for a rapid and sensitive diagnostic for identifying battery defects. We demonstrate the use of atomic magnetometry to measure the magnetic fields around miniature solid-state battery cells. These fields encode information about battery manufacturing defects, state of charge, impurities, or can provide important insights into ageing processes. Compared with SQUID-based magnetometry, the availability of atomic magnetometers, however, highlights the possibility for a low-cost, portable, and flexible implementation of battery quality-control and characterization technology.
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The ever-increasing demand for high-capacity rechargeable batteries highlights the need for sensitive and accurate diagnostic technology for determining the state of a cell, for identifying and localizing defects, or for sensing capacity loss mechanisms. Here, we demonstrate the use of atomic magnetometry to map the weak induced magnetic fields around a Li-ion battery cell as a function of state of charge and upon introducing mechanical defects. These measurements provide maps of the magnetic susceptibility of the cell, which follow trends characteristic for the battery materials under study upon discharge. In addition, the measurements reveal hitherto unknown long time-scale transient internal current effects, which were particularly pronounced in the overdischarged regime. The diagnostic power of this technique is promising for the assessment of cells in research, quality control, or during operation, and could help uncover details of charge storage and failure processes in cells.
Solid state battery technology has recently garnered considerable interest from companies including Toyota, BMW, Dyson, and others. The primary driver behind the commercialization of solid state batteries (SSBs) is to enable the use of lithium metal as the anode, as opposed to the currently used carbon anode, which would result in ~20% energy density improvement. However, no reported solid state battery to date meets all of the performance metrics of state of the art liquid electrolyte lithium ion batteries (LIBs) and indeed several solid state electrolyte (SSE) technologies may never reach parity with current LIBs. We begin with a review of state of the art LIBs, including their current performance characteristics, commercial trends in cost, and future possibilities. We then discuss current SSB research by focusing on three classes of solid state electrolytes: Sulfides, Polymers, and Oxides. We discuss recent and ongoing commercialization attempts in the SSB field. Finally, we conclude with our perspective and timeline for the future of commercial batteries.
When optically pumped magnetometers are aimed for the use in Earths magnetic field, the orientation of the sensor to the field direction is of special importance to achieve accurate measurement result. Measurement errors and inaccuracies related to the heading of the sensor can be an even more severe problem in the case of special operational configurations, such as for example the use of strong off-resonant pumping. We systematically study the main contributions to the heading error in systems that promise high magnetic field resolutions at Earths magnetic field strengths, namely the non-linear Zeeman splitting and the orientation dependent light shift. The good correspondence of our theoretical analysis to experimental data demonstrates that both of these effects are related to a heading dependent modification of the interaction between the laser light and the dipole moment of the atoms. Also, our results promise a compensation of both effects using a combination of clockwise and counter clockwise circular polarization.
We demonstrate identification of position, material, orientation and shape of objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97$%$ are obtained. This circumvents the need of solving the inverse problem, and demonstrates the extension of machine learning to diffusive systems such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security.
We develop the XCALIB toolkit to calibrate the beam profile of an X-ray free-electron laser (XFEL) at the focal spot based on the experimental charge state distributions (CSDs) of light atoms. Accurate characterization of the fluence distribution at the focal spot is essential to perform the volume integrations of physical quantities for a quantitative comparison between theoretical and experimental results, especially for fluence dependent quantities. The use of the CSDs of light atoms is advantageous because CSDs directly reflect experimental conditions at the focal spot, and the properties of light atoms have been well established in both theory and experiment. To obtain theoretical CSDs, we use XATOM, a toolkit to calculate atomic electronic structure and to simulate ionization dynamics of atoms exposed to intense XFEL pulses, which involves highly excited multiple core hole states. Employing a simple function with a few parameters, the spatial profile of an XFEL beam is determined by minimizing the difference between theoretical and experimental results. We have implemented an optimization procedure employing the reinforcement learning technique. The technique can automatize and organize calibration procedures which, before, had been performed manually. XCALIB has high flexibility, simultaneously combining different optimization methods, sets of charge states, and a wide range of parameter space. Hence, in combination with XATOM, XCALIB serves as a comprehensive tool to calibrate the fluence profile of a tightly focused XFEL beam in the interaction region.
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