Recent Advances in Nanostructured Thermoelectric Half-Heusler Compounds

Half-Heusler (HH) alloys have attracted considerable interest as promising thermoelectric (TE) materials in the temperature range around 700 K and above, which is close to the temperature range of most industrial waste heat sources. The past few years have seen nanostructuing play an important role in significantly enhancing the TE performance of several HH alloys. In this article, we briefly review the recent progress and advances in these HH nanocomposites. We begin by presenting the structure of HH alloys and the different strategies that have been utilized for improving the TE properties of HH alloys. Next, we review the details of HH nanocomposites as obtained by different techniques. Finally, the review closes by highlighting several promising strategies for further research directions in these very promising TE materials.Comment: 34 pages, 22 figure

Data on the characterization and anticancer action of iron(II) polypyridyl complexes

AbstractThis data article contains complementary figures and results related to the research article entitled, “Cellular localization of iron(II) polypyridyl complexes determines their anticancer action mechanisms” [1] (Chen et al., 2015).The characterization of Fe(II) complexes by ESI-MS, 1H NMR, 13C NMR spectroscopy, FT-IR spectra, UV–vis spectra was provided. Also,the data for the stability of Fe(II) complexes 1–5 in DMSO/Milli-Q water/ culture medium (without serum or phenol red) at 37°C at different periods of time by UV–vis spectra and 1H NMR was showed. At the same time, the anticancer efficacy, cellular distribution and ROS generation in MCF-7 cells of complexes are reported. In addition, we also show the cellular localization of complex 4, the relative fluorescence intensity of complex 1 and complex 3 pretreated with anti-TfR (2μg/mL) in MCF-7 cells using flow cytometry. The compilation of this data provides an invaluable resource for the wider research community and the interpretation of these data could be found in the research article noted above

Modeling Quantum Entanglements in Quantum Language Models

Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM's dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model

Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader's trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory

Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus

Citation: Gong, W. J., Jia, J. J., Zhang, B. K., Mi, S. J., Zhang, L., Xie, X. M., . . . Tu, C. C. (2017). Serum Metabolomic Profiling of Piglets Infected with Virulent Classical Swine Fever Virus. Frontiers in Microbiology, 8, 14. doi:10.3389/fmicb.2017.00731Classical swine fever (CSF) is a highly contagious swine infectious disease and causes significant economic losses for the pig industry worldwide. The objective of this study was to determine whether small molecule metabolites contribute to the pathogenesis of CSF. Birefly, serum metabolomics of CSFV Shimen strain-infected piglets were analyzed by ultraperformance liquid chromatography/electrospray ionization time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) in combination with multivariate statistical analysis. In CSFV-infected piglets at days 3 and 7 post-infection changes were found in metabolites associated with several key metabolic pathways, including tryptophan catabolism and the kynurenine pathway, phenylalanine metabolism, fatty acid and lipid metabolism, the tricarboxylic acid and urea cycles, branched-chain amino acid metabolism, and nucleotide metabolism. Several pathways involved in energy metabolism including fatty acid biosynthesis and beta-oxidation, branched-chain amino acid metabolism, and the tricarboxylic acid cycle were significantly inhibited. Changes were also observed in several metabolites exclusively associated with gut microbiota. The metabolomic profiles indicate that CSFV-host gut microbiome interactions play a role in the development of CSF

High performance Bi2Te3 nanocomposites prepared by single-element-melt-spinning spark-plasma sintering

The last decade has witnessed nanocomposites becoming a new paradigm in the field of thermoelectric (TE) research. At its core is to prepare high performance TE nanocomposites, both p- and n-type, in a time and energy efficient way. To this end, we in this article summarize our recent effort and results on both p- and n-type Bi2Te3-based nanocomposites prepared by a unique single-element-melt-spinning spark-plasma sintering procedure. The results of transport measurements, scanning and transmission electronic microscopy, and small angle neutron scattering have proved essential in order to establish the correlation between the nanostructures and the TE performance of the materials. Interestingly, we find that in situ formed nanocrystals with coherent boundaries are the key nanostructures responsible for the significantly improved TE performance of p-type Bi2Te3 nanocomposites whereas similar nanostructures turn out to be less effective for n-type Bi2Te3 nanocomposites. We also discuss the alternative strategies to further improve the TE performance of n-type Bi2Te3 materials via nanostructuring processe