We discuss recent literature data on the relaxation times (the primary tα), viscosity, and self-diffusion in water-glycerol and water-methanol mixtures across a wide temperature range from the stable water phase to the deep supercooled regime (373–147K). In particular, to clarify the role of hydrophilicity interactions (the hydrogen bonds) and hydrophobic interactions we study the mixture in terms of the water molar fraction (XW) with fixed temperatures at 5K steps across the entire composition range, and we find a marked deviation from the ideal thermodynamic behavior of the transport functions. This deviation is strongly T and XW dependent and spans values that range from two orders of magnitude at the highest temperature to more than five in the deeply supercooled regime (more precisely, at ≃200K). We analyze these deviations in terms of how the measured values differ from ideal values and find that the hydrogen-bonding water network dominates system properties up to XW = 0.3. We also examine an Arrhenius plot of the maximum excess value (Δtα(T) vs. 1/T) and find two significant changes due to water: one at the dynamical crossover temperature (TL ≃ 225K, i.e., the locus of the Widom line), and one at T ≃ 315K (the water isothermal compressibility χT minimum).

Nowadays there are clear evidences from both experiments and MD-simulations proving that the chain Rouse modes correlation functions are non-exponential in unentangled polymer blends and also in pure polymers at low temperature (with respect to that of the glass transition Tg) even for the long wavelengths modes where local potentials and chain stiffness should not play any role. In a recent paper [S. Arrese-Igor et al, Phys. Rev. Lett.113, 078302 (2014)] it has been proposed that this non-exponential behavior depends on the ratio between the so-called Rouse time - i.e., the characteristic time of the slowest chain mode relaxation - and the time scale of the α-relaxation. This parameter is in some way ‘universal’ in the meaning that it can encode many different experimental situations. In this paper, we show that this behavior can be quantitatively explained in the framework of a theoretical approach based on: (i) a generalized Langevin equation (GLE) formalism and (ii) a memory function which takes into account the effect of collective dynamics on the chain dynamics of a tagged chain and which was constructed taking inspirations from the original ideas of the reptation model proposed by de Gennes.

Recent developments in a number of fields call for high-throughput, high-resolution imaging of large areas. Examples are reconstruction of macroscopic volumes of mouse brain tissue, or wafer defect inspection. To address these needs, we have developed a multi-beam, single column SEM which utilizes an array of 61 or 91 electron beams and detectors in parallel. The total possible detection speed of the multiple beam SEM is the single detection speed times the number of beams. In the same time a single beam SEM creates an image of several million pixels size, the multi-beam SEM produces between several hundred million and one billion pixels. Herein we demonstrate the capabilities of generating massive data sets using the multi-beam SEM on a variety of samples including brain tissue serial sections and semiconductor test wafers.

X-ray diffraction pattern collected with two-dimensional detector contains the scattering intensity distribution as a function of two orthogonal angles. One is the Bragg angle 2θ and the other is the azimuthal angle about the incident x-ray beam, denoted by γ. A 2D diffraction pattern can be integrated to a conventional diffraction pattern and evaluated by most exiting software and algorithms for conventional applications, such as, phase identification, structure refinement and 2θ-profile analysis. However, the materials structure information associated to the intensity distribution along γ direction is lost through the integration. The diffraction vector approach has been approved to be the genuine theory in 2D data analysis. The unit diffraction vector used for 2D analysis is a function of both 2θ and γ. The unit diffraction vector for all the pixels in the 2D pattern can be expressed either in the laboratory coordinates or in the sample coordinates. The vector components can then be used to derive fundamental equations for many applications, including stress, texture, crystal orientation and crystal size evaluation.

An innovative model based on spinodal-like decomposition of supercooled multi-component liquids was developed for prediction of the glass forming ability (GFA) of ternary alloys. The up-hill diffusion in the initially homogeneous freezing ternary liquid was considered as a necessary condition for solidification of crystalline phases. New generalized criteria of spinodal decomposition of ternary alloys for time/space correlated fluctuations were formulated. These criteria take into account both thermodynamic and kinetic properties of the system. The introduced criteria were found to provide adequate GFA evaluation of different compositions in the Mg-based ternary alloys.

We performed dynamical cluster analysis in a Cu-Zr-Al based glass-forming metallic liquid using an unsupervised machine learning algorithm. The size of the dynamical clusters is used to quantify the onset of cooperative dynamics as the underlying mechanism leading to the Arrhenius dynamic crossover in transport coefficients of the metallic liquid. This technique is useful to directly visualize dynamical clusters and quantify their sizes upon cooling. We demonstrate the robustness of this algorithm by performing sensitivity analysis against two key parameters: number of mobility groups and inconsistency coefficient of the hierarchical cluster tree. The results elucidate the optimized range of values for both of these parameters that capture the underlying physical picture of increasing cooperative dynamics appropriately.

We established a Ca1-xBixMn1-yNiyO3 (0 ≤ x, y ≤ 0.1) powder library using a combinatorial system based on the electrostatic spray deposition method. Single phase perovskite-type structures were identified in all of the powders. To measure electrical conductivity, the powder library was subjected to high-pressure (200 MPa) and heat-treated at 950°C for 1 hour in an oxygen atmosphere. As a representative example, the electrical conductivity of 5%-Bi-substituted CaMnO3-δ showed a higher value (63 S·cm-1) than an unsubstituted powder (13 S·cm-1). The improved electrical conductivity, on the other hand, was still very far from the ideal result (167 S·cm-1).

An integrated experimental – simulation – control theory approach designed to enable adaptive control of microstructural evolution in polycrystalline metals is described. A micro-heater array, containing ten addressable channels, is used to create desired temperature profiles across thin polycrystalline films in situ to a scanning electron microscope (SEM). The goal is that on heating with controlled temperature profiles, the evolution of grain growth within the film can be continuously monitored and compared to Monte Carlo simulations of trajectories towards a desired microstructure. Feed-forward and feedback control strategies are then used to guide the microstructure along the desired trajectory.