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Manganese-cobalt hexacyanoferrate cathodes for sodium-ion batteries
Pasta, Mauro,Wang, Richard Y.,Ruffo, Riccardo,Qiao, Ruimin,Lee, Hyun-Wook,Shyam, Badri,Guo, Minghua,Wang, Yayu,Wray, L. Andrew,Yang, Wanli,Toney, Michael F.,Cui, Yi The Royal Society of Chemistry 2016 Journal of materials chemistry. A, Materials for e Vol.4 No.11
<P>Prussian Blue analogues (PBAs) have shown promise as electrode materials for grid-scale batteries because of their high cycle life and rapid kinetics in aqueous-based electrolytes. However, these materials suffer from relatively low specific capacity, which may limit their practical applications. Here, we investigate strategies to improve the specific capacity of these materials while maintaining their cycling stability and elucidate mechanisms that enhance their electrochemical properties. In particular, we have studied the electrochemical and structural properties of manganese hexacyanoferrate (MnHCFe) and cobalt hexacyanoferrate (CoHCFe) in an aqueous, sodium-ion electrolyte. We also studied manganese-cobalt hexacyanoferrate (Mn-CoHCFe) solid solutions with different Mn/Co ratios that combine properties of both MnHCFe and CoHCFe. The materials have the characteristic open-framework crystal structure of PBAs, and their specific capacities can be significantly improved by electrochemically cycling (oxidizing and reducing) both the carbon-coordinated Fe and the nitrogen-coordinated Co or Mn ions.<I>In situ</I>synchrotron X-ray diffraction studies and<I>ex situ</I>soft X-ray absorption spectroscopy combined with an in-depth electrochemical characterization provide insight into the different electrochemical properties associated with the Fe, Co, and Mn redox couples. We show that cycling the C-coordinated Fe preserves the crystal structure and enables the outstanding kinetics and cycle life previously displayed by PBAs in aqueous electrolytes. On the other hand, the N-coordinated Co and Mn ions exhibit a slower kinetic regime due to structural distortions resulting from the weak N-coordinated crystal field, but they still contribute significantly towards increasing the specific capacity of the materials. These results provide the understanding needed to drive future development of PBAs for grid-scale applications that require extremely high cycle life and kinetics.</P>
Effect of the indentation process on fatigue life of drilled specimens
Giuseppe Marannano,Antonino Pasta,Francesco Parrinello,Antonio Giallanza 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.7
Design and manufacture of mechanical elements are strongly influenced by the evaluation of the residual stresses due to their effectson the material strength. This paper presents numerical and experimental results performed on AW 6082-T6 aluminum alloy drilledspecimens when the hole is created after a bilateral indentation process. The plastic deformation induced by the indenters creates a compressiveresidual stress field around the hole, which persists after the drilling operation. Several numerical analysis have been carried outin ANSYS APDL explicit solver for different indentation depths and hole diameters in order to evaluate the compressive circumferentialstresses, optimal process parameters and relevant geometric features. Fatigue tests are performed in order to determine the cycles to failureand the corresponding Wöhler diagram.
Elena Formisano,Andrea Pasta,Anna L. Cremonini,Elda Favari,Annalisa Ronca,Federico Carbone,Tommaso Semino,Francesco Di Pierro,Samir G. Sukkar,Livia Pisciotta 한국식품영양과학회 2020 Journal of medicinal food Vol.23 No.6
The guidelines for the treatment of dyslipidemias include the use of nutraceuticals (NUTs) in association with lifestyle modifications to achieve therapeutic goals. In NUT pill, different substances may be associated; in this study we investigated a combined NUT containing monacolin K (MonK)+KA (1:1), berberine (BBR), and silymarin. The aim of the study was to evaluate low-density lipoprotein cholesterol (LDL-C) reduction in 53 patients suffering from polygenic hypercholesterolemia, characterized by a low/intermediate cardiovascular risk calculated with SCORE algorithm. The effects on lipid profile of 2-month treatment with NUT containing MonK+KA (1:1), BBR, and sylimarin, were compared with Atorvastatin (ATO) 10 mg administrated in a matched control group. Serum proprotein convertase subtilisin/kexin type 9 (PCSK9) levels and the cholesterol loading capacity (CLC) were determined at baseline and at the end of the study in NUT-treated group; variations were assessed. NUT was effective as lipid-lowering agent with a wide interindividual response variability (mean LDL-C from 170.8 ± 19.9 to 123.8 ± 20.0 with a change of −47.0 ± 21.5 mg/dL; P < .001) and the effect was similar to that induced by ATO. The use of NUT significantly modified PCSK9 levels (P < .01) and CLC (P < .001), ultimately suppressing the serum-mediated foam cell generation directly measured on human macrophages. NUT reduces LDL-C levels with an effect similar to what is induced by 10 mg of ATO and ex vivo improves the functional profile of lipoproteins with antiatherogenic action.
A phosphorene–graphene hybrid material as a high-capacity anode for sodium-ion batteries
Sun, Jie,Lee, Hyun-Wook,Pasta, Mauro,Yuan, Hongtao,Zheng, Guangyuan,Sun, Yongming,Li, Yuzhang,Cui, Yi Nature Publishing Group 2015 Nature nanotechnology Vol.10 No.11
Sodium-ion batteries have recently attracted significant attention as an alternative to lithium-ion batteries because sodium sources do not present the geopolitical issues that lithium sources might. Although recent reports on cathode materials for sodium-ion batteries have demonstrated performances comparable to their lithium-ion counterparts, the major scientific challenge for a competitive sodium-ion battery technology is to develop viable anode materials. Here we show that a hybrid material made out of a few phosphorene layers sandwiched between graphene layers shows a specific capacity of 2,440 mA h g<SUP>−1</SUP> (calculated using the mass of phosphorus only) at a current density of 0.05 A g<SUP>−1</SUP> and an 83% capacity retention after 100 cycles while operating between 0 and 1.5 V. Using in situ transmission electron microscopy and ex situ X-ray diffraction techniques, we explain the large capacity of our anode through a dual mechanism of intercalation of sodium ions along the x axis of the phosphorene layers followed by the formation of a Na<SUB>3</SUB>P alloy. The presence of graphene layers in the hybrid material works as a mechanical backbone and an electrical highway, ensuring that a suitable elastic buffer space accommodates the anisotropic expansion of phosphorene layers along the y and z axial directions for stable cycling operation.
Deep learning approach for the segmentation of aneurysmal ascending aorta
Albert Comelli,Navdeep Dahiya,Alessandro Stefano,Viviana Benfante,Giovanni Gentile,Valentina Agnese,Giuseppe M. Raffa,Michele Pilato,Anthony Yezzi,Giovanni Petrucci,Salvatore Pasta 대한의용생체공학회 2021 Biomedical Engineering Letters (BMEL) Vol.11 No.1
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter,but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel imagederivedrisk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibilityand efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNettechniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspidaortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (MaterializeNV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance interms of accuracy and time inference were compared using several parameters. All deep learning models reported a dicescore higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that theENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deeplearning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating theexpansion into clinical workflow of personalized approach to the management of patients with ATAAs.