Partially reduced Ni0.8Co0.15Al0.05LiO2-δ for low-temperature SOFC cathode

Nowadays, Ni0.8Co0.15Al0.05LiO2-δ (NCAL) has been increasingly applied into the solid oxide fuel cell (SOFC) field as a promising electrode material. Here, the performances of NCAL cathode were investigated for low-temperature SOFCs (LT-SOFCs) on Ce0.8Sm0.2O2-δ (SDC) electrolyte. After on-line reduction of NCAL for 30 min, the partially reduced NCAL, i.e., NCAL(r), was employed as the new cathode and its performances were also investigated. The area specific resistances of NCAL and NCAL(r) cathodes on SDC electrolyte are 7.076 and 1.214 Ω cm2 at 550 °C, respectively. Moreover, NCAL(r) exhibits the activation energy of 0.46 eV for oxygen reduction reaction (ORR), which is much lower than that of NCAL (0.88 eV). The fuel cell consisted of NCAL electrodes and SDC electrolyte shows an open circuit voltage (OCV) of 0.95 V and power output of 436 mW cm−2 at 550 °C. After cathode on-line optimization, the cell\u27s OCV and power output are significantly increased to 1.01 V and 648 mW cm−2, which mainly attributed to the accelerated ORR and decreased electrode polarization resistance. These results demonstrate that NCAL(r) is a promising cathode material for LT-SOFCs

From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis