RESUMEN
The rapid population growth coupled with rising global energy demand underscores the crucial importance of advancing intermittent renewable energy technologies and low-emission vehicles, which will be pivotal toward carbon neutralization. Reversible solid oxide cells (RSOCs) hold significant promise as a technology for high-efficiency power generation, long-term chemical energy storage, and CO2 conversion. Herein, RSOCs were, for the first time, studied to power electric vehicles. Based on our experimental results, an ideal RSOC stack was established with reasonable assumptions. Subsequently, through analysis and comparison of important merits, such as power densities, energy densities, charging/refueling time, and fuel economy of RSOC-based electric vehicles (RSOCEVs), conventional internal combustor vehicles (ICEVs), and battery-based electric vehicles (BEVs), the advantages and prospects of RSOCEVs were highlighted. Our H2-H2O RSOCs exhibit high electrochemical performances in both fuel cell (peak power density = 1.6 W cm-2 at 750 °C) and electrolysis modes (current density = 2.0 A cm-2 at 1.3 V and 750 °C), along with durable reversible operation under a wide range of conditions. In CO-CO2, our RSOCs achieved excellent performance in fuel cell mode (peak power density = 0.68 cm-2 at 700 °C). Furthermore, a world record current density of 3.4 A cm-2 at 1.5 V and 750 °C was achieved in the CO2 electrolysis mode. Moreover, an assessment of the CO2 electrolysis efficiency was conducted, offering insights for establishing energy storage strategies and mitigating CO2 emissions. Therefore, the RSOC technology has the potential to assume a central role in a future energy system with abundant renewable power generation while mitigating the CO2 released from fossil fuels.
RESUMEN
Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer from the degeneration of image quality due to speckle noise and bulk-motion noise, respectively. Because the cross-sectional retina has distinct features in OCT and OCTA B-scans, existing digital filters that can denoise OCT efficiently are unable to handle the bulk-motion noise in OCTA. In this Letter, we propose a universal digital filtering approach that is capable of minimizing both types of noise. Considering that the retinal capillaries in OCTA are hard to differentiate in B-scans while having distinct curvilinear structures in 3D volumes, we decompose the volumetric OCT and OCTA data with 3D shearlets, thus efficiently separating the retinal tissue and vessels from the noise in this transform domain. Compared with wavelets and curvelets, the shearlets provide better representation of the layer edges in OCT and the vasculature in OCTA. Qualitative and quantitative results show the proposed method outperforms the state-of-the-art OCT and OCTA denoising methods. Also, the superiority of 3D denoising is demonstrated by comparing the 3D shearlet filtering with its 2D counterpart.
