Crystal field splitting is limiting the stability and strength of ultra-incompressible orthorhombic transition metal tetraborides

PubMed ID: 26976479The lattice stability and mechanical strengths of the supposedly superhard transition metal tetraborides (TmB4, Tm = Cr, Mn and Fe) evoked recently much attention from the scientific community due to the potential applications of these materials, as well as because of general scientific interests. In the present study, we show that the surprising stabilization of these compounds from a high symmetry to a low symmetry structure is accomplished by an in-plane rotation of the boron network, which maximizes the in-plane hybridization by crystal field splitting between d orbitals of Tm and p orbitals of B. Studies of mechanical and electronic properties of TmB4 suggest that these tetraborides cannot be intrinsically superhard. The mechanical instability is facilitated by a unique in-plane or out-of-plane weakening of the three-dimensional covalent bond network of boron along different shear deformation paths. These results shed a novel view on the origin of the stability and strength of orthorhombic TmB4, highlighting the importance of combinational analysis of a variety of parameters related to plastic deformation of the crystalline materials when attempting to design new ultra-incompressible, and potentially strong and hard solids.Web of Science6art. no. 2308

Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

The aim of this work is to efficiently and robustly solve the statistical inverse problem related to the identification of the elastic properties at both macroscopic and mesoscopic scales of heterogeneous anisotropic materials with a complex microstructure that usually cannot be properly described in terms of their mechanical constituents at microscale. Within the context of linear elasticity theory, the apparent elasticity tensor field at a given mesoscale is modeled by a prior non-Gaussian tensor-valued random field. A general methodology using multiscale displacement field measurements simultaneously made at both macroscale and mesoscale has been recently proposed for the identification the hyperparameters of such a prior stochastic model by solving a multiscale statistical inverse problem using a stochastic computational model and some information from displacement fields at both macroscale and mesoscale. This paper contributes to the improvement of the computational efficiency, accuracy and robustness of such a method by introducing (i) a mesoscopic numerical indicator related to the spatial correlation length(s) of kinematic fields, allowing the time-consuming global optimization algorithm (genetic algorithm) used in a previous work to be replaced with a more efficient algorithm and (ii) an ad hoc stochastic representation of the hyperparameters involved in the prior stochastic model in order to enhance both the robustness and the precision of the statistical inverse identification method. Finally, the proposed improved method is first validated on in silico materials within the framework of 2D plane stress and 3D linear elasticity (using multiscale simulated data obtained through numerical computations) and then exemplified on a real heterogeneous biological material (beef cortical bone) within the framework of 2D plane stress linear elasticity (using multiscale experimental data obtained through mechanical testing monitored by digital image correlation)

ATOMISTIC AND EXPERIMENTAL DETERMINATION OF THE STRUCTURAL AND THERMOPHYSICAL PROPERTIES OF THE ACCIDENT TOLERANT FUEL MATERIALS

The tragic nuclear accident at the Fukushima-Daiichi power station in Japan brought in to our attention the risk associated with the current design of reactors based on uranium dioxide (UO2) fuel and zirconium cladding. As an offshoot, the research towards accident tolerant nuclear fuel (ATF) that can withstand the loss of coolant for a long time while improving thermal efficiency has gained momentum. Most desirable thermophysical properties expected of an ATF is high thermal conductivity, the lack of which leads to the poor dissipation and rapid meltdown at the core of the fuel pellet during the loss of coolant. Several approaches are being considered by researchers across the world to improve the thermal conductivity of nuclear fuels. Apart from the state of art of uranium-based fuels, there is a renewed interest in thorium-based fuels (especially thorium dioxide (ThO2) and thorium nitride (ThN)) in the quest of ATF. This thesis focuses on evolutionary fuel concepts based on thoria fuels. Unlike UO2, the information regarding the thermophysical properties of ThO2 fuels, and the additive materials under the normal operating conditions and the extreme accident conditions are not well known. Therefore, in this thesis, the computational techniques such as density functional theory (DFT) and classical molecular dynamics (MD) are used to determine the thermophysical properties of the thoria fuel, surrogate of thoria CeO2 and additive materials such as SiC and BeO. One of the significant limitations in the front end of the thoria fuel cycle has the difficulty of fabricating dense pellets by conventional sintering techniques. Hence the processing of thoria fuels by the spark plasma sintering (SPS) was proposed, and the effect of the sintering parameters on the density, microstructure and the thermal conductivity of ThO2 fuel was established. Finally, using SPS, a novel composite fuel of ThO2-SiC has been fabricated with the enhanced thermal conductivity

Magnetism, FeS colloids, and Origins of Life

A number of features of living systems: reversible interactions and weak bonds underlying motor-dynamics; gel-sol transitions; cellular connected fractal organization; asymmetry in interactions and organization; quantum coherent phenomena; to name some, can have a natural accounting via $physical$ interactions, which we therefore seek to incorporate by expanding the horizons of `chemistry-only' approaches to the origins of life. It is suggested that the magnetic 'face' of the minerals from the inorganic world, recognized to have played a pivotal role in initiating Life, may throw light on some of these issues. A magnetic environment in the form of rocks in the Hadean Ocean could have enabled the accretion and therefore an ordered confinement of super-paramagnetic colloids within a structured phase. A moderate H-field can help magnetic nano-particles to not only overcome thermal fluctuations but also harness them. Such controlled dynamics brings in the possibility of accessing quantum effects, which together with frustrations in magnetic ordering and hysteresis (a natural mechanism for a primitive memory) could throw light on the birth of biological information which, as Abel argues, requires a combination of order and complexity. This scenario gains strength from observations of scale-free framboidal forms of the greigite mineral, with a magnetic basis of assembly. And greigite's metabolic potential plays a key role in the mound scenario of Russell and coworkers-an expansion of which is suggested for including magnetism.Comment: 42 pages, 5 figures, to be published in A.R. Memorial volume, Ed Krishnaswami Alladi, Springer 201

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Mechanics of Materials: Towards Predictive Methods for Kinetics in Plasticity, Fracture, and Damage

The workshop dealt with current advances of computational methods, mathematics and continuum mechanics directed at thermodynamically consistent forms of constitutive equations for complex evolutionary phenomena in modern materials such as plasticity, fracture and damage. The main aspects addressed in presentations and discussions were multiphysical description of new materials, (visco)plasticity, fracture, damage, structural mechanics, mechanics of materials and dislocation dynamics