Giant magneto-elastic coupling in multiferroic hexagonal manganites.

The motion of atoms in a solid always responds to cooling or heating in a way that is consistent with the symmetry of the given space group of the solid to which they belong. When the atoms move, the electronic structure of the solid changes, leading to different physical properties. Therefore, the determination of where atoms are and what atoms do is a cornerstone of modern solid-state physics. However, experimental observations of atomic displacements measured as a function of temperature are very rare, because those displacements are, in almost all cases, exceedingly small. Here we show, using a combination of diffraction techniques, that the hexagonal manganites RMnO3 (where R is a rare-earth element) undergo an isostructural transition with exceptionally large atomic displacements: two orders of magnitude larger than those seen in any other magnetic material, resulting in an unusually strong magneto-elastic coupling. We follow the exact atomic displacements of all the atoms in the unit cell as a function of temperature and find consistency with theoretical predictions based on group theories. We argue that this gigantic magneto-elastic coupling in RMnO3 holds the key to the recently observed magneto-electric phenomenon in this intriguing class of materials.

Promoting computational thinking through project-based learning.

This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

Data on the effect of particle size, porosity and discharging rate on the performance of lithium-ion battery with NMC 622 cathode through numerical analysis.

The data presented in this article are related to the computed results reported in the article entitled "A modeling approach to study the performance of Ni-rich layered oxide cathode for lithium-ion battery" [1]. The lithium-ion battery (LIB) employed in the simulation is made up of a LiNi0.6Mn0.2Co0.2O2 (NMC 622) cathode and lithium metal foil anode. The numerical simulations were carried out using COMSOL Multiphysics 5.4 software which is based on the finite element (FE) method. The data presented in this manuscript shows how varying particle size and porosity affect the performance of the battery as the discharging rate is varied. Four different particle sizes and six different porosities were varied for the purpose of understanding the above behavior. The data presented can be used to further the analysis reported in the accompanying manuscript and aid in design of other cathode materials for LIB and other battery systems. It can also be used to compare some measured results for validation purposes. A comprehensive analysis of the data is found in [1].

Lithium barium silicate, Li(2)BaSiO(4), from synchrotron powder data.

The structure of lithium barium silicate, Li(2)BaSiO(4), has been determined from synchrotron radiation powder data. The title compound was synthesized by high-temperature solid-state reaction and crystallizes in the hexagonal space group P6(3)cm. It contains two Li atoms, one Ba atom (both site symmetry ..m on special position 6c), two Si atoms [on special positions 4b (site symmetry 3..) and 2a (site symmetry 3.m)] and four O atoms (one on general position 12d, and three on special positions 6c, 4b and 2a). The basic units of the structure are (Li(6)SiO(13))(5-) units, each comprising seven tetrahedra sharing edges and vertices. These basic units are connected by sharing corners parallel to [001] and through sharing (SiO(4))(4-) tetrahedra in (001). The relationship between the structures and luminescence properties of Li(2)SrSiO(4), Li(2)CaSiO(4) and the title compound is discussed.

Swift synthesis of hierarchically ordered mesocellular mesoporous silica by microwave-assisted hydrothermal method.

Hierarchically ordered mesocellular mesoporous silicas (HMMS) were swiftly synthesized using P123 and sodium silicate as a silica source within an hour by applying microwave irradiation without pore expander. The XRD, TEM and BET studies demonstrated that materials as-synthesized and calcined have the hierarchically ordered mesocellular structure with two different sizes about 10 nm and 30 nm pores having a minimized micropore volume compared with conventional hydrothermal method. Moreover, the HMMS sample prepared by microwave-assisted hydrothermal method had higher surface area than that of conventional hydrothermal method. The variation of the pore size and morphology of mesocellular structure varied with the aging time.

An extremely simple macroscale electronic skin realized by deep machine learning.

Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.

Classification of crystal structure using a convolutional neural network.

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

Vaporization-condensation-recrystallization process-mediated synthesis of helical m-aminobenzoic acid nanobelts.

One-dimensional (1D) helical organic nanostructures were synthesized by a modified vapor-solid (VS) process, called the vaporization-condensation-recrystallization (VCR) process. The conventional solution-phase synthetic methods generally mediate self-assemblies of repeating unit molecules. To provide enough intermolecular interaction forces among the unit molecules, such strategy requires specific designs and syntheses of complex unit molecules as they possess numerous functional groups including phenyl rings, hydroxyl groups, long aliphatic chains, etc. On the contrary, we found that small and simple organic molecules, for example, m-ABA, could be self-assembled by the VCR process, resulting in 1D helical organic nanostructures. When m-aminobenzoic acid (m-ABA) powders were vaporized and transported to be condensed on a cooler region, the condensates were recrystallized into 1D helical nanobelts. Each step of the VCR process was confirmed from control experiments performed by varying reaction times, substrate types, and reaction temperatures. Powder XRD data, SAED analysis, and theoretical calculations revealed that dimers of m-ABA molecules have repeating units, and the growth axis of m-ABA nanohelices is [100].

A new strontium borophosphate, Sr6BP5O20, from synchrotron powder data.

Strontium borophosphate, Sr6BP5O20, was prepared by a solution synthesis method. The crystal structure was solved ab initio from synchrotron powder data without preliminary knowledge of the chemical formula. The compound crystallizes in space group -4c2. Sr atoms occupy sites coordinated by eight or nine O atoms, and the anionic layer consists of BO4 and PO4 tetrahedra. The eightfold-coordinated Sr atom lies at a site with twofold symmetry, while one P atom and the B atom are located on special positions of site symmetry -4.