Synthesis and Characterization of Two Metallic Spin-Glass Phases of FeMo₄Ge₃

Polycrystalline samples of FeMo4Ge3 have been synthesized by the reduction of an oxide mixture at 1248 K and characterized by a combination of diffraction, muon spin relaxation (µ+SR), Mössbauer spectroscopy, magnetometry, transport, and heat-capacity measurements. The compound adopts a tetragonal W5Si3 structure (space group I4/mcm); the iron and molybdenum atoms are disordered over two crystallographic sites, 16k and either 4a or 4b. The synthesis conditions determine which fourfold site is selected; occupation of either leads to the presence of one-dimensional chains of transition metals in the structure. In both cases, the electrical resistivity below 200 K is ~175 µΩ cm. The dc magnetization rapidly rises below 35 K (Fe/Mo on 16k and 4b sites) or 16 K (16k and 4a sites), and a magnetization of 1µB or 0.8µB per Fe atom is observed in 4 T at 2 K. The ac susceptibility and the heat capacity both suggest that these are glasslike magnetic transitions, although the transition shows a more complex temperature dependence (with two maxima in χ ) when the 4b sites are partially occupied by iron. No long-range magnetic order is thought to be present at 5 K in either structural form; this has been proven by neutron diffraction and µ+SR for the case when Fe and Mo occupy the 16k and 4b sites

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Synthesis and characterisation of intermetallic nitrides and germanides

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The Influence of Chemical Composition on the Magnetic Properties of Fe₁.₅₋ₓCoₓRh₀.₅Mo₃N (0 ≤ x ≤ 1.5)

The system Fe1.5-xCoxRh0.5Mo3N has been studied by neutron diffraction, magnetometry, Mössbauer spectroscopy and transport measurements in order to follow the variations in the electronic properties as cobalt is added to ferromagnetic Fe 1.5Rh0.5Mo3N. The Curie temperature is maximised (132 K) when x = 0.5, although the saturation magnetisation decreases with increasing cobalt content. Co1.5Rh0.5Mo3N does not show long-range magnetic order above 5 K. All compositions show metallic conductivity and the temperature dependence of the internal hyperfine fields in iron-rich compositions can be fitted using the Stoner model. The change in behaviour observed for x \u3e 0.9 is attributed to the dominance of antiferromagnetic interactions in cobalt-rich regions

Synthesis and characterization of two metallic spin-glass phases of Fe Mo4 Ge3

Polycrystalline samples of Fe Mo4 Ge3 have been synthesized by the reduction of an oxide mixture at 1248 K and characterized by a combination of diffraction, muon spin relaxation (μ+ SR), Mössbauer spectroscopy, magnetometry, transport, and heat-capacity measurements. The compound adopts a tetragonal W5 Si3 structure (space group I4 mcm); the iron and molybdenum atoms are disordered over two crystallographic sites, 16k and either 4a or 4b. The synthesis conditions determine which fourfold site is selected; occupation of either leads to the presence of one-dimensional chains of transition metals in the structure. In both cases, the electrical resistivity below 200 K is ∼175 μΩ cm. The dc magnetization rapidly rises below 35 K (Fe Mo on 16k and 4b sites) or 16 K (16k and 4a sites), and a magnetization of 1 μB or 0.8 μB per Fe atom is observed in 4 T at 2 K. The ac susceptibility and the heat capacity both suggest that these are glasslike magnetic transitions, although the transition shows a more complex temperature dependence (with two maxima in χ″) when the 4b sites are partially occupied by iron. No long-range magnetic order is thought to be present at 5 K in either structural form; this has been proven by neutron diffraction and μ+ SR for the case when Fe and Mo occupy the 16k and 4b sites. © 2008 The American Physical Society

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Targeting natural killer cells and natural killer T cells in cancer.

International audienceNatural killer (NK) cells and natural killer T (NKT) cells are subsets of lymphocytes that share some phenotypical and functional similarities. Both cell types can rapidly respond to the presence of tumour cells and participate in antitumour immune responses. This has prompted interest in the development of innovative cancer therapies that are based on the manipulation of NK and NKT cells. Recent studies have highlighted how the immune reactivity of NK and NKT cells is shaped by the environment in which they develop. The rational use of these cells in cancer immunotherapies awaits a better understanding of their effector functions, migratory patterns and survival properties in humans