ABSTRACT
The utilization of high-voltage LiCoO2 is an effective approach to break through the bottleneck of practical energy density in lithium ion batteries. However, the structural and interfacial degradations at the deeply delithiated state as well as the associated safety concerns impede the application of high-voltage LiCoO2. Herein, we present a synergetic strategy for promoting the surface stability of LiCoO2 at high voltage by Ti-Mg-Al co-doping and systematically study the effects of the dopants on the surface stability, electronic structure and Li+ diffusion properties of the LiCoO2 (104) surface using first-principles calculations. It is found that Ti, Mg and Al dopants can be facilely introduced into the Co sites of the LiCoO2 (104) surface. Furthermore, the co-doping could significantly stabilize the surface oxygen of LiCoO2 at a high delithiation state. Particularly, by aggregating Ti-Mg-Al co-dopant distribution in the surface layer, surface oxygen loss is dramatically suppressed. In addition, analysis of the electronic structure indicates that Ti-Mg-Al co-doping can enhance the electronic conductivity of the LiCoO2 (104) surface and greatly inhibit the charge deficiency of the superficial lattice O atoms at a highly delithiated state. In spite of a negligible improvement in the surface Li+ diffusion kinetics, the Ti-Mg-Al surface-modified LiCoO2 is expected to exhibit improved electrochemical performance at high voltage due to its superior surface stability. Our results suggest that aggregating Ti, Mg and Al co-dopant distribution in the surface layer is a promising modulation strategy to synergistically promote the surface oxygen stability of LiCoO2 at high voltages.
ABSTRACT
PURPOSE: Characterizing aspheric deformities of the femoral head-neck junction in cam-type femoroacetabular impingement (FAI) requires representing the location, size, or extent of the bony lesion. The objectives of this work are to (1) assess the feasibility of creating 3D models of cam deformities from MRI sets, (2) present a standardized 2D visualization of the lesion, and (3) present and evaluate the potential utility of summary metrics in distinguishing between FAI patients and control subjects. METHODS: Using MRIs from five subjects with diagnosed cam-type FAI and four healthy subjects, we developed a technique based on subtracting an estimated normal surface from each subject's actual bone surface in order to generate a subject-specific 2D "diagnosis graph" that characterized the femoral deformity. The models from three control subjects were combined to create the baseline model. RESULTS: The RMS fitting error between the surface models of individual control subjects and their corresponding baseline models was 1.05 mm across the head and the head-to-neck transition region. In the anterosuperior region of the 2D diagnosis graphs, the mean height of the detected cam deformities relative to the estimated baseline normal shape was 17.9 % of the head radius for the five FAI subjects (95 % CI 8.5-27.3 %) and 7.0 % (95 % CI 2.9-11.1 %) for the four control subjects. A binary logistic regression analysis indicated that an h/r ratio larger than a threshold of [Formula: see text] = 10.7 % (equivalent to approximately 2.3 mm in height) yielded the best discrimination between cam-type FAI subjects and normal subjects. CONCLUSIONS: Our 2D diagnosis graph qualitatively enabled the cam-type lesions in four of our five diagnosed patients to be clearly visualized on MRI-derived models. We believe this visualization tool may be helpful in better characterizing cam-type lesions for diagnosis and for developing more precise plans for surgical treatment.
