Grand-potential based phase-field model for systems with interstitial sites

Existing grand-potential based multicomponent phase-field model is extended to handle systems with interstitial sublattice. This is achieved by treating the concentration of alloying elements in site-fraction. Correspondingly, the chemical species are distinguished based on their lattice positions, and their mode of diffusion, interstitial or substitutional, is appropriately realised. An approach to incorporate quantitative driving-force, through parabolic approximation of CALPHAD data, is introduced. By modelling austenite decomposition in ternary Fe–C–Mn, albeit in a representative microstructure, the ability of the current formalism to handle phases with interstitial components, and to distinguish interstitial diffusion from substitutional in grand-potential framework is elucidated. Furthermore, phase transformation under paraequilibrium is modelled to demonstrate the limitation of adopting mole-fraction based formulation to treat multicomponent systems

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments

Pioglitazone administration alters ovarian gene expression in aging obese lethal yellow mice

<p>Abstract</p> <p>Background</p> <p>Women with polycystic ovary syndrome (PCOS) are often treated with insulin-sensitizing agents, e.g. thiazolidinediones (TZD), which have been shown to reduce androgen levels and improved ovulatory function. Acting via peroxisome proliferator-activated receptor (PPAR) gamma, TZD alter the expression of a large variety of genes. Lethal yellow (LY; C57BL/6J Ay/a) mice, possessing a mutation (Ay) in the agouti gene locus, exhibit progressive obesity, reproductive dysfunction, and altered metabolic regulation similar to women with PCOS. The current study was designed to test the hypothesis that prolonged treatment of aging LY mice with the TZD, pioglitazone, alters the ovarian expression of genes that may impact reproduction.</p> <p>Methods</p> <p>Female LY mice received daily oral doses of either 0.01 mg pioglitazone (n = 4) or an equal volume of vehicle (DMSO; n = 4) for 8 weeks. At the end of treatment, ovaries were removed and DNA microarrays were used to analyze differential gene expression.</p> <p>Results</p> <p>Twenty-seven genes showed at least a two-fold difference in ovarian expression with pioglitazone treatment. These included leptin, angiopoietin, angiopoietin-like 4, Foxa3, PGE1 receptor, resistin-like molecule-alpha (RELM), and actin-related protein 6 homolog (ARP6). For most altered genes, pioglitazone changed levels of expression to those seen in untreated C57BL/6J(a/a) non-mutant lean mice.</p> <p>Conclusion</p> <p>TZD administration may influence ovarian function via numerous diverse mechanisms that may or may not be directly related to insulin/IGF signaling.</p

Quasi-fission reactions as a probe of nuclear viscosity

Fission fragment mass and angular distributions were measured from the ^{64}Ni+^{197}Au reaction at 418 MeV and 383 MeV incident energy. A detailed data analysis was performed, using the one-body dissipation theory implemented in the code HICOL. The effect of the window and the wall friction on the experimental observables was investigated. Friction stronger than one-body was also considered. The mass and angular distributions were consistent with one-body dissipation. An evaporation code DIFHEAT coupled to HICOL was developed in order to predict reaction time scales required to describe available data on pre-scission neutron multiplicities. The multiplicity data were again consistent with one-body dissipation. The cross-sections for touch, capture and quasi-fission were also obtained.Comment: 25 pages REVTeX, 3 tables, 13 figures, submitted to Phys. Rev