Real-time observation of interlayer vibrations in bilayer and few-layer graphene.

We report real-time observation of the interlayer shearing mode, corresponding to the lateral oscillation of graphene planes, for bi- and few-layer graphene. Using a femtosecond pump-probe technique, we have followed coherent oscillations of this vibrational mode directly in the time domain. The shearing-mode frequency, as expected for an interlayer mode, exhibits a strong and systematic dependence on the number of layers, varying from 1.32 THz for the bulk limit to 0.85 THz for bilayer graphene. We explored the role of interactions with the external environment on this vibrational mode by comparing the response observed for graphene layers supported by different substrates and suspended in free space. No significant frequency shifts were observed.

The Electrochemical and Structural Changes of Phosphorus-Doped TiO2 through In Situ Raman and In Situ X-Ray Diffraction Analysis.

Doping is a widely employed technique to enhance the functionality of lithium-ion battery materials, tailoring their performance for specific applications. In our study, we employed in situ Raman and in situ X-ray diffraction (XRD) spectroscopic techniques to examine the structural alterations and electrochemical behavior of phosphorus-doped titanium dioxide (TiO2) nanoparticles. This investigation revealed several notable changes: an increase in structural defects, enhanced ionic and electronic conductivity, and a reduction in crystallite size. These alterations facilitated higher lithiation rates and led to the first observed appearance of LiTiO2 in the Raman spectra due to anatase lithiation, resulting in a reversible double-phase transition during the charging and discharging processes. Furthermore, doping with 2, 5, and 10 wt % phosphorus resulted in an initial increase in specific capacity compared to undoped TiO2. However, higher doping levels were associated with diminished capacity retention, pinpointing an optimal doping level for phosphorus. These results underscore the critical role of in situ characterization techniques in understanding doping effects, thereby advancing the performance of anode materials, particularly TiO2, in lithium-ion batteries.

Photon number statistics uncover the fluctuations in non-equilibrium lattice dynamics.

Fluctuations of the atomic positions are at the core of a large class of unusual material properties ranging from quantum para-electricity to high temperature superconductivity. Their measurement in solids is the subject of an intense scientific debate focused on seeking a methodology capable of establishing a direct link between the variance of the atomic displacements and experimentally measurable observables. Here we address this issue by means of non-equilibrium optical experiments performed in shot-noise-limited regime. The variance of the time-dependent atomic positions and momenta is directly mapped into the quantum fluctuations of the photon number of the scattered probing light. A fully quantum description of the non-linear interaction between photonic and phononic fields is benchmarked by unveiling the squeezing of thermal phonons in α-quartz.

Synthetic generation of myocardial blood-oxygen-level-dependent MRI time series via structural sparse decomposition modeling.

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI. CP-BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by 1) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and 2) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.