Design of crystal-like aperiodic solids with selective disorder--phonon coupling

Functional materials design normally focuses on structurally-ordered systems because disorder is considered detrimental to many important physical properties. Here we challenge this paradigm by showing that particular types of strongly-correlated disorder can give rise to useful characteristics that are inaccessible to ordered states. A judicious combination of low-symmetry building unit and high-symmetry topological template leads to aperiodic "procrystalline" solids that harbour this type of topological disorder. We identify key classes of procrystalline states together with their characteristic diffraction behaviour, and establish a variety of mappings onto known and target materials. Crucially, the strongly-correlated disorder we consider is associated with specific sets of modulation periodicities distributed throughout the Brillouin zone. Lattice dynamical calculations reveal selective disorder-phonon coupling to lattice vibrations characterised by these same periodicities. The principal effect on the phonon spectrum is to bring about dispersion in energy rather than wave-vector, as in the poorly-understood "waterfall" effect observed in relaxor ferroelectrics. This property of procrystalline solids suggests a mechanism by which strongly-correlated topological disorder might allow new and useful functionalities, including independently-optimised thermal and electronic transport behaviour as required for high-performance thermoelectrics.Comment: 4 figure

Hybrid Local-Order Mechanism for Inversion Symmetry Breaking

Using classical Monte Carlo simulations, we study a simple statistical mechanical model of relevance to the emergence of polarisation from local displacements on the square and cubic lattices. Our model contains two key ingredients: a Kitaev-like orientation-dependent interaction between nearest neighbours, and a steric term that acts between next-nearest neighbours. Taken by themselves, each of these two ingredients is incapable of driving long-range symmetry breaking, despite the presence of a broad feature in the corresponding heat capacity functions. Instead each component results in a "hidden" transition on cooling to a manifold of degenerate states, the two manifolds are different in the sense that they reflect distinct types of local order. Remarkably, their intersection---\emph{i.e.} the ground state when both interaction terms are included in the Hamiltonian---supports a spontaneous polarisation. In this way, our study demonstrates how local ordering mechanisms might be combined to break global inversion symmetry in a manner conceptually similar to that operating in the "hybrid" improper ferroelectrics. We discuss the relevance of our analysis to the emergence of spontaneous polarisation in well-studied ferroelectrics such as BaTiO$_3$ and KNbO$_3$.Comment: 8 pages, 8 figure

Using VR for Training how to Conduct A Tire Change on an Aircraft

Virtual Reality (VR) is becoming an affordable and increasingly common tool for training. Twenty-four Aviation Maintenance Science students were randomly assigned to a VR group or a Traditional Training (Demonstration) group for a Cessna tire change module. The VR group spent about 5 minutes interacting with the VR application where they completed several actions to replace a tire. The Traditional group watched the course instructor change a tire in the maintenance hangar. One week later, students from both groups changed a tire in a real-world environment. Time to complete tasks were measured for locating the procedure in the manual, jacking the airplane up, removing the cotter pin, removing the wheel, replacing the wheel, and jacking the airplane down. Participants self-reported on a scale of 1(Not helpful at all) to 10 (Extremely helpful) how beneficial they felt their training session was. The Traditional group was significantly faster than the VR group in locating the procedure in the manual (a task not conducted in the VR application). No other difference in time to complete tasks were found. The Traditional group rated their training as significantly more helpful for changing the tire than the VR group did. Except for downjacking the aircraft, a significant correlation was found between the more helpful students found their respective training and the faster they completed each task. Although the VR application was not better than the Traditional Training for the real-world task, finding VR training not worse than Traditional training for transfer of task warrants further study

Spin correlations in Ca3Co2O6: A polarised-neutron diffraction and Monte Carlo study

We present polarised-neutron diffraction measurements of the Ising-like spin-chain compound Ca3Co2O6 above and below the magnetic ordering temperature TN. Below TN, a clear evolution from a single-phase spin-density wave (SDW) structure to a mixture of SDW and commensurate antiferromagnet (CAFM) structures is observed on cooling. For a rapidly-cooled sample, the majority phase at low temperature is the SDW, while if the cooling is performed sufficiently slowly, then the SDW and the CAFM structure coexist between 1.5 and 10 K. Above TN, we use Monte Carlo methods to analyse the magnetic diffuse scattering data. We show that both intra- and inter-chain correlations persist above TN, but are essentially decoupled. Intra-chain correlations resemble the ferromagnetic Ising model, while inter-chain correlations resemble the frustrated triangular-lattice antiferromagnet. Using previously-published bulk property measurements and our neutron diffraction data, we obtain values of the ferromagnetic and antiferromagnetic exchange interactions and the single-ion anisotropy.Comment: 10 pages, 7 figure

DNA sequencing as a tool to monitor marine ecological status

© 2017 Goodwin, Thompson, Duarte, Kahlke, Thompson, Marques and Caçador. Many ocean policies mandate integrated, ecosystem-based approaches to marine monitoring, driving a global need for efficient, low-cost bioindicators of marine ecological quality. Most traditional methods to assess biological quality rely on specialized expertise to provide visual identification of a limited set of specific taxonomic groups, a time-consuming process that can provide a narrow view of ecological status. In addition, microbial assemblages drive food webs but are not amenable to visual inspection and thus are largely excluded from detailed inventory. Molecular-based assessments of biodiversity and ecosystem function offer advantages over traditional methods and are increasingly being generated for a suite of taxa using a "microbes to mammals" or "barcodes to biomes" approach. Progress in these efforts coupled with continued improvements in high-throughput sequencing and bioinformatics pave the way for sequence data to be employed in formal integrated ecosystem evaluation, including food web assessments, as called for in the European Union Marine Strategy Framework Directive. DNA sequencing of bioindicators, both traditional (e.g., benthic macroinvertebrates, ichthyoplankton) and emerging (e.g., microbial assemblages, fish via eDNA), promises to improve assessment of marine biological quality by increasing the breadth, depth, and throughput of information and by reducing costs and reliance on specialized taxonomic expertise

Understanding defects in amorphous silicon with million-atom simulations and machine learning

The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we show that atomistic machine-learning methods can reveal the complex structural and energetic landscape of defects in amorphous silicon. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and more than ten thousand defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned local atomic energies to develop a universal classification of the different types of defects in amorphous silicon. The results suggest a revision of the established floating-bond model by showing that fivefold-coordinated atoms in amorphous silicon exhibit a wide range of local environments, and it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for modelling and understanding defects in disordered materials beyond silicon alone