Manganese-Substituted Rare-Earth Zinc Arsenides <i>RE</i>_1–<i>y</i>Mn_<i>x</i>Zn_2–<i>x</i>As₂ (<i>RE</i> = Eu–Lu) and <i>RE</i>_2–<i>y</i>Mn_<i>x</i>Zn_4–<i>x</i>As₄ (<i>RE</i> = La–Nd, Sm, Gd)

Two series of Mn-substituted rare-earth zinc arsenides <i>RE</i>_1–<i>y</i>Mn_<i>x</i>Zn_2–<i>x</i>As₂ (<i>RE</i> = Eu–Lu) and <i>RE</i>_2–<i>y</i>Mn_<i>x</i>Zn_4–<i>x</i>As₄ (<i>RE</i> = La–Nd, Sm, Gd) were prepared by reaction of the elements at 750 °C. Both series are derived from ideal empirical formula <i>RE</i>M₂As₂ (M = Mn, Zn) and adopt crystal structures related to the trigonal CaAl₂Si₂-type (space group <i>P</i>3̅<i>m</i>1) in which hexagonal nets of <i>RE</i> atoms and [M₂As₂] slabs built up of edge-sharing M-centered tetrahedra are alternately stacked along the <i>c</i>-direction. For compounds with divalent <i>RE</i> components (Eu, Yb), the fully stoichiometric and charge-balanced formula <i>RE</i>M₂As₂ is obtained, with Mn and Zn atoms statistically disordered within the same tetrahedral site. For compounds with trivalent <i>RE</i> components, the <i>RE</i> sites become deficient, and the Mn atoms are segregated from the Zn atoms in separate tetrahedral sites. Within the series <i>RE</i>_1–<i>y</i>Mn_<i>x</i>Zn_2–<i>x</i>As₂ (Gd–Tm, Lu), the parent CaAl₂Si₂-type structure is retained, and the Mn atoms are disordered within partially occupied interstitial sites above and below [Zn_2–<i>x</i>As₂] slabs. Within the series <i>RE</i>_2–<i>y</i>Mn_<i>x</i>Zn_4–<i>x</i>As₄ (<i>RE</i> = La–Nd, Sm, Gd), the <i>c</i>-axis becomes doubled as a result of partial ordering of Mn atoms between every other pair of [Zn_2–<i>x</i>As₂] slabs. Attempts to synthesize Gd-containing solid solutions with the charge-balanced formula Gd_0.67Mn_<i>x</i>Zn_2–<i>x</i>As₂ suggested that these phases could be formed with up to 50% Mn substitution. Band structure calculations reveal that a hypothetical superstructure model with the formula La_1.33MnZn₃As₄ would have no gap at the Fermi level and that slightly lowering the electron count alleviates antibonding Mn–As interactions; a spin-polarized calculation predicts nearly ferromagnetic half-metallic behavior. X-ray photoelectron spectroscopy confirms the presence of divalent Mn in these compounds

Powder X-ray diffraction and X-ray photoelectron spectroscopy of cutin from a 300 Ma tree fern (Alethopteris pseudograndinioides, Canada)

Experimental results of X-ray diffraction and X-ray photoelectron spectroscopy of fossil cutin from the compressed foliage of the Carboniferous tree fern Alethopteris pseudograndinioides, Cantabrian age, Sydney Coalfield, Canada, are presented in this paper. The light-colored cutin was obtained by oxidizing the compression in Schulze's solution in two stages for a total of 19 days. The broad peak in the powder diffractogram at 20° is characteristic of an average separation of ~4.4 Å between the methylenic hydrocarbon chains (CH2)n, whereas the sharper peaks at 26°–28° suggest that within a small fraction of the sample, the chains are more regularly separated. Most of the chains are likely randomly aligned to form a nematic structure. Elemental composition by mass amounts to 58.3% C, 1.1% N, 19.4% O, 19.7% Cl, and 1.5% Si, and the local chemical environment of C 1s, O 1s, and Cl 2p is probed. Cl content is a surprising result, and further research is needed for identifying chlorine-containing species.Fil: Stoyko, Stanislav S.. University of Alberta. Department of Chemistry; CanadáFil: Rudyk, Brent W.. University of Alberta. Department of Chemistry; CanadáFil: Mar, Arthur. University of Alberta. Department of Chemistry; CanadáFil: Zodrow, Erwin L.. Cape Breton University. Palaeobiological Laboratory; CanadáFil: D` Angelo, José Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Científico Tecnológico Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentin

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases <i>ABC</i>

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (>0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a “confused” region (0.3–0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases <i>ABC</i>

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (>0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a “confused” region (0.3–0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning