The Inorganic Crystal Structure Database (<scp>ICSD</scp>): A Tool for Materials Sciences

Rühl, Stephan

doi:10.1002/9783527802265.ch2

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…The determination of unit cell parameters as well as the volume fraction of each phase was carried out through Rietveld refinement using the FullProf software (Version 19.04.24) [ 20 , 21 ]. The initial structural models used in the refinement were adopted from reference [ 22 ]. The profile adjustment led to rapid convergence with the FullProf software in pattern matching mode.…”

Section: Resultsmentioning

confidence: 99%

Structural Refinement and Optoelectrical Properties of Nd2Ru2O7 and Gd2Ru2O7 Pyrochlore Oxides for Photovoltaic Applications

Kraidy,

Yapi,

El Marssi

et al. 2024

Materials

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High-performance photovoltaic devices require active photoanodes with superior optoelectric properties. In this study, we synthesized neodymium ruthenate, Nd2Ru2O7 (NRO), and gadolinium ruthenate pyrochlore oxides, Gd2Ru2O7 (GRO), via the solid-state reaction technique, showcasing their potential as promising candidates for photoanode absorbers to enhance the efficiency of dye-sensitized solar cells. A structural analysis revealed predominantly cubic symmetry phases for both materials within the Fd-3m space group, along with residual orthorhombic symmetry phases (Nd3RuO7 and Gd3RuO7, respectively) refined in the Pnma space group. Raman spectroscopy further confirmed these phases, identifying distinct active modes of vibration in the predominant pyrochlore oxides. Additionally, a scanning electron microscopy (SEM) analysis coupled with energy-dispersive X-ray spectroscopy (EDX) elucidated the morphology and chemical composition of the compounds. The average grain size was determined to be approximately 0.5 µm for GRO and 1 µm for NRO. Electrical characterization via I-V measurements revealed that these pyrochlore oxides exhibit n-type semiconductor behavior, with conductivity estimated at 1.5 (Ohm·cm)−1 for GRO and 4.5 (Ohm·cm)−1 for NRO. Collectively, these findings position these metallic oxides as promising absorber materials for solar panels.

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Section: Resultsmentioning

confidence: 99%

Structural Refinement and Optoelectrical Properties of Nd2Ru2O7 and Gd2Ru2O7 Pyrochlore Oxides for Photovoltaic Applications

Kraidy,

Yapi,

El Marssi

et al. 2024

Materials

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“…With the launch of the materials genome initiative [155] in 2011 and the coming of the big data era, a large effort has been made in the materials science community to collect extensive datasets of materials properties and to provide materials engineers with ready access to the properties of known materials. Existing databases are the materials project [63, 98], the inorganic crystal structure database [39, 126], the Materials genome initiative [155, 171] ,the NOMAD archive [34, 113], the Topological Materials Database [156], Supercon [97], or the National Institute of Materials Science 2011 [111] with many databases of material properties of metal alloys, or the National Institute of Standards and Technology [112] with databases of properties for material classes such as structural ceramics, oxide glasses, superconductors [50, 73, 121, 127, 167, 175]. A more comprehensive list of material databases can be found in Correa‐Baena et al [27].…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

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Investigation into Cation-Ordered Magnetic Polar Double Perovskite Oxides

et al. 2021

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The technique of coupling cation ordering with other microscopic degrees of freedom was employed to induce magnetic polar phenomena within the materials belonging to the double perovskite oxide family represented by the chemical formula AA′BB′O 6 , i.e., by considering double cation occupancy at both the 12-and 6-fold oxygen coordinated A-and B-cation sites. Starting from a paraelectric material, La 2 MnNiO 6 , that exhibits ferromagnetic behavior near room temperature, we constructed a wide chemical composition space to search for prospective polar magnetic systems. Since we have employed the hybrid improper mechanism of ferroelectricity to induce polar phenomena in these perovskite oxides, ensuring the stability of the cation ordering within certain octahedral rotational pattern was our primary imperative. Hence, we studied the energetics of (1) various A-and B-site cation-ordered phases, (2) cation-disordered phases, (3) various spin configurations, (4) various octahedral rotated structures, and (5) formation of double perovskite compositions with respect to their decomposition into possible stable compositions. On the basis of our study of a series of compositions comprising of transition metal ions from various periods at the B-site, we identify prospective compounds, those are expected to show both polar phenomena with a considerable polar distortion and ferro/ferri-magnetic properties with a considerable high magnetization.

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The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences

Cited by 8 publications

References 36 publications

Structural Refinement and Optoelectrical Properties of Nd2Ru2O7 and Gd2Ru2O7 Pyrochlore Oxides for Photovoltaic Applications

Structural Refinement and Optoelectrical Properties of Nd2Ru2O7 and Gd2Ru2O7 Pyrochlore Oxides for Photovoltaic Applications

Machine learning for material characterization with an application for predicting mechanical properties

Investigation into Cation-Ordered Magnetic Polar Double Perovskite Oxides

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