Spectroscopic and Computational Study of Structural Changes in γ-LiV2O5 Cathodic Material Induced by Lithium Intercalation

2015 ◽  
Vol 119 (36) ◽  
pp. 20801-20809 ◽  
Author(s):  
M. B. Smirnov ◽  
E. M. Roginskii ◽  
V. Yu. Kazimirov ◽  
K. S. Smirnov ◽  
R. Baddour-Hadjean ◽  
...  
Keyword(s):  
Structural Changes ◽  
Computational Study ◽  
Lithium Intercalation ◽  
Cathodic Material

scholarly journals Computational Study of Lithium Intercalation in Silicene Channels on a Carbon Substrate after Nuclear Transmutation Doping

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 60 ◽  
Author(s):  
Alexander Galashev ◽  
Ksenia Ivanichkina ◽  
Konstantin Katin ◽  
Mikhail Maslov
Keyword(s):  
Anode Material ◽  
Computational Study ◽  
Lithium Ion ◽  
Lithium Intercalation ◽  
Graphite Substrate ◽  
Carbon Substrate ◽  
Self Diffusion ◽  
Battery Capacity ◽  
Transmutation Doping ◽  
Phosphorus Doped

Silicene is considered to be the most promising anode material for lithium-ion batteries. In this work, we show that transmutation doping makes silicene substantially more suitable for use as an anode material. Pristine and modified bilayer silicene was simulated on a graphite substrate using the classical molecular dynamics method. The parameters of Morse potentials for alloying elements were determined using quantum mechanical calculations. The main advantage of modified silicene is its low deformability during lithium intercalation and its possibility of obtaining a significantly higher battery charge capacity. Horizontal and vertical profiles of the density of lithium as well as distributions of the most significant stresses in the walls of the channels were calculated both in undoped and doped systems with different gaps in silicene channels. The energies of lithium adsorption on silicene, including phosphorus-doped silicene, were determined. High values of the self-diffusion coefficient of lithium atoms in the silicene channels were obtained, which ensured a high cycling rate. The calculations showed that such doping increased the normal stress on the walls of the channel filled with lithium to 67% but did not provoke a loss of mechanical strength. In addition, doping achieved a greater battery capacity and higher charging/discharging rates.

scholarly journals Reductive activation and structural rearrangement in superoxide reductase: a combined infrared spectroscopic and computational study

2014 ◽  
Vol 16 (27) ◽  
pp. 14220-14230 ◽  
Author(s):  
M. Horch ◽  
A. F. Pinto ◽  
T. Utesch ◽  
M. A. Mroginski ◽  
C. V. Romão ◽  
...  
Keyword(s):  
Computational Methods ◽  
Structural Changes ◽  
Computational Study ◽  
Structural Rearrangement ◽  
Superoxide Reductase ◽  
Difference Spectroscopy ◽  
Combined Approach ◽  
Reductive Activation ◽  
Infrared Spectroscopic

Local and global structural changes that enable reductive activation of superoxide reductase are revealed by a combined approach of infrared difference spectroscopy and computational methods.

Silver-Doped Vanadium Oxides as Host Materials for Lithium Intercalation

1997 ◽  
Vol 496 ◽  
Author(s):  
F. Coustier ◽  
S. Passerini ◽  
J. Hill ◽  
W. H. Smyrl
Keyword(s):  
Electronic Conductivity ◽  
Molar Fraction ◽  
Vanadium Oxides ◽  
Lithium Intercalation ◽  
Cathodic Material ◽  
Host Materials ◽  
Lithium Diffusion Coefficient ◽  
Lithium Diffusion ◽  
Silver Silver ◽  
Very High

ABSTRACTAn improved cathodic material has been obtained by doping vanadium oxide hydrogel with silver. Silver-doped vanadium pentoxides with a silver molar fraction ranging from 0.01 to 1 were synthesized. With the successful doping, the electronic conductivity of V2O5 was increased by 2 to 3 orders of magnitude. The electrochemical performance of the silver doped materials is very high, up to 4 moles of lithium per mole of silver-doped V2O5 were found to be reversibly intercalated. In addition, the lithium diffusion coefficient is found to be high in the silver-doped material and with a smaller dependence on the lithium intercalation level. These enhancements resulted in high rates of insertion and delivered capacities.

scholarly journals 2140

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12
Author(s):  
Pinaki Sarder ◽  
Rabi Yacoub ◽  
John E. Tomaszewski
Keyword(s):  
Structural Changes ◽  
Computational Study ◽  
Kidney Diseases ◽  
Quantitative Information ◽  
Renal Tissue ◽  
Therapeutic Interventions ◽  
Theoretical Physics ◽  
Computational Pipeline ◽  
Disease Trajectory ◽  
Digital Biomarkers

OBJECTIVES/SPECIFIC AIMS: (i) Digitally quantify pathologically relevant glomerular microcompartmental structures in murine renal tissue histopathology images. (ii) Digitally model disease trajectory in a mouse model of diabetic nephropathy (DN). METHODS/STUDY POPULATION: We have developed a computational pipeline for glomerular structural compartmentalization based on Gabor filtering and multiresolution community detection (MCD). The MCD method employs improved, efficient optimization of a Potts model Hamiltonian, adopted from theoretical physics, modeling interacting electron spins. The method is parameter-free and capable of simultaneously selecting relevant structure at all biologically relevant scales. It can segment glomerular compartments from a large image containing hundreds of glomeruli in seconds for quantification—which is not possible manually. We will analyze the performance of our computational pipeline in healthy and streptozotocin induced DN mice using renal tissue images, and model the structural distributions of automatically quantified glomerular features as a function of DN progression. The performance of this structural-disease model will be compared with existing visual quantification methods used by pathologists in the clinic. RESULTS/ANTICIPATED RESULTS: Computational modeling will reveal digital biomarkers for early proteinuria in DN, able to predict disease trajectory with greater precision and accuracy than manual inspection alone. DISCUSSION/SIGNIFICANCE OF IMPACT: Automated detection of microscopic structural changes in renal tissue will eventually lead to objective, standardized diagnosis, reflecting cost savings for DN through discovery of digital biomarkers hidden within numerical structural distributions. This computational study will pave the path for the creation of new digital tools which provide clinicians invaluable quantitative information about expected patient disease trajectory, enabling earlier clinical predictions and development of early therapeutic interventions for kidney diseases.

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