Single-crystalline nickel-rich layered oxides are promising cathode materials for building high-energy lithium-ion batteries because of alleviated particle cracking and irreversible phase transitions upon cycling, compared with their polycrystalline counterparts. Under a high state of charge, parasitic reactions tend to occur at the cathode− electrolyte interface, which could result in sluggish Li-ion diffusion kinetics and quickly faded electrochemical performance of cathodes. In this work, a concentration-gradient niobium-doping strategy was applied to modify the single-crystal LiNi 0.83 Co 0.12 Mn 0.05 O 2 cathode, with Nb concentration decreasing linearly from the surface to the core of the particle. As a result, the Nb-rich surface functions as an electrochemically active protective layer against electrolyte corrosion and transition metal dissolution, while the Nb-deficient core contributes to a higher capacity. The linear concentration gradient also minimizes structural transition from the surface to the core and helps to maintain structural integrity during repeated Li (de)intercalation. In addition, Nb-doping also assists to alleviate Li + /Ni 2+ mixing and increases the interlayer distance to enable faster Li-ion diffusion kinetics. By taking these advantages, the Nb-doped cathode materials (containing 1.0 atom% Nb) demonstrate a high reversible capacity, a high capacity retention, and improved rate capabilities. This work provides a general and facile approach to improve the storage performance of layered-oxide cathode materials by rationally tuning the bulk structure and interface with the electrolyte.

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Background: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.Methods: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.Results: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity, 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P<0.001), had longer incubation period (P<0.001), were more likely to have abnormal laboratory findings (P<0.05) and be in severity status (P<0.001). More lesions (including larger volume of lesion, consolidation and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P<0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.Conclusions: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.