Controllable Technology for Thermal Expansion Coefficient of Commercial Materials for Solid Oxide Electrolytic Cells.

Solid oxide electrolysis cell (SOEC) industrialization has been developing for many years. Commercial materials such as 8 mol% Y2O3-stabilized zirconia (YSZ), Gd0.1Ce0.9O1.95 (GDC), La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF), La0.6Sr0.4CoO3-δ (LSC), etc., have been used for many years, but the problem of mismatched thermal expansion coefficients of various materials between cells has not been fundamentally solved, which affects the lifetime of SOECs and restricts their industry development. Currently, various solutions have been reported, such as element doping, manufacturing defects, and introducing negative thermal expansion coefficient materials. To promote the development of the SOEC industry, a direct treatment method for commercial materials-quenching and doping-is reported to achieve the controllable preparation of the thermal expansion coefficient of commercial materials. The quenching process only involves the micro-treatment of raw materials and does not have any negative impact on preparation processes such as powder slurry and sintering. It is a simple, low-cost, and universal research strategy to achieve the controllable preparation of the thermal expansion coefficient of the commercial material La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) through a quenching process by doping elements and increasing oxygen vacancies in the material. Commercial LSCF materials are heated to 800 °C in a muffle furnace, quickly removed, and cooled and quenched in 3.4 mol/L of prepared Y(NO3)3. The thermal expansion coefficient of the treated material can be reduced to 13.6 × 10-6 K-1, and the blank sample is 14.1 × 10-6 K-1. In the future, it may be possible to use the quenching process to select appropriate doping elements in order to achieve similar thermal expansion coefficients in SOECs.

Mutual Conversion of CO-CO2 on a Perovskite Fuel Electrode with Endogenous Alloy Nanoparticles for Reversible Solid Oxide Cells.

Reversible solid oxide cells (RSOCs) can efficiently render the mutual conversion between electricity and chemicals, for example, electrolyzing CO2 to CO under a solid oxide electrolysis cell (SOEC) mode and oxidizing CO to CO2 under a solid oxide fuel cell (SOFC) mode. Nevertheless, the development of RSOCs is still hindered, owing to the lack of catalytically active and carbon-tolerant fuel electrodes. For improving mutual CO-CO2 conversion kinetics in RSOCs, here, we demonstrate a high-performing and durable fuel electrode consisting of redox-stable Sr2(Fe, Mo)2O6-δ perovskite oxide and epitaxially endogenous NiFe alloy nanoparticles. The electrochemical impedance spectrum (EIS) and distribution of relaxation time (DRT) analyses reveal that surface/interface oxygen exchange kinetics and the CO/CO2 activation process are both greatly accelerated. The assembled single cell produces a maximum power density (MPD) of 443 mW cm-2 at 800 °C under the SOFC mode, with the corresponding CO oxidation rate of 5.524 mL min-1 cm-2. On the other hand, a current density of -0.877 A cm-2 is achieved at 1.46 V under the SOEC mode, equivalent to a CO2 reduction rate of 6.108 mL min cm-2. Furthermore, reliable reversible conversion of CO-CO2 is proven with no performance degradation in 20 cycles under SOEC (1.3 V) and SOFC (0.6 V) modes. Therefore, our work provides an alternative way for designing highly active and durable fuel electrodes for RSOC applications.

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors.

The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.