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Abstract Protonic ceramic electrochemical cells (PCECs) represent a promising class of solid‐state energy conversion devices capable of high‐efficiency hydrogen production and power generation. However, the practical deployment of planar PCECs is fundamentally constrained by severe structural deformation and mechanical failure during fabrication, stemming from asymmetric shrinkage between the thin electrolyte and the thick NiO‐based support layer. In this work, a functionally integrated, symmetry‐engineered double‐sided electrolyte (DE) design is unveiled, which not only suppresses thermally induced curvature but also unlocks significant gains in electrochemical performance and stability. This architecture intrinsically balances shrinkage dynamics across the cell bilaterally, enabling the fabrication of ultra‐flat 5 × 5 cm²cells with sub‐100 µm thickness variation. A numerical solid mechanics simulation is introduced to investigate and interpret this achievement. Beyond structural advantages, the DE configuration enhances the cell operational stability, delivering a low open‐circuit voltage degradation of 9.5 mV/100 h across 80 thermal cycles. This work establishes a compelling paradigm wherein architectural symmetry directly translates to both mechanical fidelity and functional enhancement, offering a promising route toward PCECs scale‐up. 

Abstract In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. 

In this work, point pattern estimators are used to analyze the distribution of measurements from a multi-beam Lidar on a pitching platform. Multi-beam Lidars have high resolution in the horizontal plane, but poor vertical resolution. Placing the Lidar on a pitching base improves this resolution, but causes the distribution of measurements to be highly irregular. In this work, these measurement distributions are treated as point patterns and three estimators are used to quantity how measurements are spaced, which has implications in robotic detection of objects using Lidar sensors. These estimators are used to demonstrate how a pitching trajectory for the platform can be chosen to improve multiple performance criteria, such as increasing the likelihood of detection of an object, or adjusting how closely measurements should be spaced. 

In this article, a compressive sensing (CS) reconstruction algorithm is applied to data acquired from a nodding multi-beam Lidar system following a Lissajous-like trajectory. Multi-beam Lidar systems provide 3D depth information of the environment for applications in robotics, but the vertical resolution of these devices may be insufficient to identify objects, especially when the object is small and/or far from the robot. In order to overcome this issue, the Lidar can be nodded in order to obtain higher vertical resolution with the side-effect of increased scan time, especially when raster scan patterns are used. Such systems, especially when combined with nodding, also yield large volumes of data which may be difficult to store and mange on resource constrained systems. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally sub-sample the imaged area and the data can be further reduced by simply not collecting each data point along the trajectory. The final depth image must then be reconstructed from the sub-sampled data. In this article, a CS reconstruction algorithm is applied to data collected during a fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare its results to images produced from simple nearest-neighbor interpolation.