Electronic redistribution around oxygen atoms in silicate melts by ab initio molecular dynamics simulation

The structure around oxygen atoms of four silicate liquids (silica, rhyolite, a model basalt and enstatite) is evaluated by ab initio molecular dynamics simulation. Thanks to the use of maximally localized Wannier orbitals to represent the electronic ground state of the simulated system, one is able to quantify the redistribution of electronic density around oxygen atoms as a function of the cationic environment and melt composition. It is shown that the structure of the melt in the immediate vicinity of the oxygen atoms modulates the distribution of the Wannier orbitals associated with oxygen atoms. In particular the evaluation of the distances between the oxygen-core and the orbital Wannier centers and their evolution with the nature of the cation indicates that the Al-O bond in silicate melts is certainly less covalent than the Si-O bond while for the series Mg-O, Ca-O, Na-O and K-O the covalent character of the M-O bond diminishes rapidly to the benefit of the ionic character. Furthermore it is found that the distribution of the oxygen dipole moment coming from the electronic polarization is only weakly dependent on the melt composition, a finding which could explain why some empirical force fields can exhibit a high degree of transferability with melt composition.Comment: 27 pages, 7 figures. To be published in Journal of Non-Crystalline Solid

Solid-state nuclear magnetic resonance spectroscopy of cements

Cement is the ubiquitous material upon which modern civilisation is built, providing long-term strength, impermeability and durability for housing and infrastructure. The fundamental chemical interactions which control the structure and performance of cements have been the subject of intense research for decades, but the complex, crystallographically disordered nature of the key phases which form in hardened cements has raised difficulty in obtaining detailed information about local structure, reaction mechanisms and kinetics. Solid-state nuclear magnetic resonance (SS NMR)spectroscopy can resolve key atomic structural details within these materials and has emerged as a crucial tool in characterising cement structure and properties. This review provides a comprehensive overview of the application of multinuclear SS NMR spectroscopy to understand composition–structure–property relationships in cements. This includes anhydrous and hydrated phases in Portland cement, calcium aluminate cements, calcium sulfoaluminate cements, magnesia-based cements, alkali-activated and geopolymer cements and synthetic model systems. Advanced and multidimensional experiments probe 1 H, 13 C, 17 O, 19 F, 23 Na, 25 Mg, 27 Al, 29 Si, 31 P, 33 S, 35 Cl, 39 K and 43 Ca nuclei, to study atomic structure, phase evolution, nanostructural development, reaction mechanisms and kinetics. Thus, the mechanisms controlling the physical properties of cements can now be resolved and understood at an unprecedented and essential level of detail

<i>LIN28A</i> erythroid-specific over-expression does not affect cell proliferation or prevent terminal maturation of cultured erythroblasts.

<p>Cell proliferation was assessed by cell counts performed on <b>(A)</b> culture day 14 and <b>(B)</b> culture day 21. Mean fold change ± SD from three separate donors for each condition: KLF1-Empty vector control (C, open bar), KLF1-LIN28A-OE (KLF1, red bar), SPTA1-Empty vector control (C, open bar), and SPTA1-LIN28A-OE (SPTA1, blue bar). Representative flow cytometry dot plots of cells stained with antibodies against transferrin receptor (CD71) and glycophorin A (GPA) cultured on <b>(C-F)</b> day 14 and <b>(G-J)</b> day 21 with percentages shown. KLF1-Empty vector control (control, panels C and G), KLF1-LIN28A-OE (KLF1, panels D and H), SPTA1-Empty vector control (control, panels E and I), and SPTA1-LIN28A-OE (SPTA1, panels F and J).</p

<i>LIN28A</i> over-expression mediated by KLF1 or SPTA1 promoter regulates the <i>let-7</i> family of miRNAs.

<p>RNA samples from erythroblasts cultured on day 14 were examined for <b>(A)</b><i>LIN28A</i> over-expression and <b>(B)</b> the total levels of <i>let-7</i> miRNAs using Q-RT-PCR. Mean value ± SD from three separate donors for each condition: KLF1-Empty vector control (control, open bar), KLF1-LIN28A-OE (KLF1, red bar), SPTA1-Empty vector control (control, open bar), and SPTA1-LIN28A-OE (SPTA1, blue bar). Asterisks indicate p<0.05.</p

Erythroid-specific <i>LIN28A</i> over-expression effects upon globin gene and protein levels in cultured adult erythroblasts.

<p><i>LIN28A</i> over-expression driven by KLF1 or SPTA1 promoter compared to control samples in the mRNA expression levels of <b>(A)</b><i>alpha</i>-, <i>mu</i>-, <i>theta</i>- and <i>zeta</i>-<i>globins</i> and <b>(B)</b><i>beta</i>-, <i>delta</i>-, <i>gamma</i>- and <i>epsilon</i>-<i>globins</i>. Analyses were performed at culture day 14. <b>(C)</b> HPLC analysis of hemoglobin from each respective control, KLF1-LIN28A-OE, and SPTA1-LIN28A-OE erythroblasts at culture day 21. KLF1-Empty vector control (C, open bar), KLF1-LIN28A-OE (KLF1, red bar), SPTA1-Empty vector control (C, open bar), and SPTA1-LIN28A-OE (SPTA1, blue bar). Representative HPLC tracing from <b>(D)</b> KLF1-Empty vector control (gray tracing), <b>(E)</b> KLF1-LIN28A-OE (red tracing), <b>(F)</b> SPTA1-Empty vector control (gray tracing), and <b>(G)</b> SPTA1-LIN28A-OE (blue tracing). Mean value ± SD three separate donors for each condition. Asterisks indicate p<0.05.</p

Melts Under Extreme Conditions From Shock Experiments

Shock compression methods form an important complement to static compression and computational approaches for probing the equation of state and thermodynamic properties of melts. This chapter summarizes shock compression, the theory by which laboratory shocks constrain liquid properties, and the standard experimental methods. It discusses the equations of state of several silicate liquid compositions and implications for microscopic structural mechanisms of liquid compression. From the available data, it examines the range of applicability and limitations of the linear mixing model for multicomponent liquid volumes and applies it to evaluate the relative buoyancy of melts and solids during lower-mantle magma ocean crystallization. Finally, the thermodynamic Grüneisen parameter is defined, its importance in shock wave research and in convecting systems explained, and its behavior according to shock experiments and molecular dynamics simulations discussed. Shock wave data on silicate liquids from several papers and recommended equation of state parameters for those liquids are compiled for convenient reference

Melts Under Extreme Conditions From Shock Experiments

Shock compression methods form an important complement to static compression and computational approaches for probing the equation of state and thermodynamic properties of melts. This chapter summarizes shock compression, the theory by which laboratory shocks constrain liquid properties, and the standard experimental methods. It discusses the equations of state of several silicate liquid compositions and implications for microscopic structural mechanisms of liquid compression. From the available data, it examines the range of applicability and limitations of the linear mixing model for multicomponent liquid volumes and applies it to evaluate the relative buoyancy of melts and solids during lower-mantle magma ocean crystallization. Finally, the thermodynamic Grüneisen parameter is defined, its importance in shock wave research and in convecting systems explained, and its behavior according to shock experiments and molecular dynamics simulations discussed. Shock wave data on silicate liquids from several papers and recommended equation of state parameters for those liquids are compiled for convenient reference

Organs-on-Chips as Bridges for Predictive Toxicology

The next generation of chemical toxicity testing will use organs-on-chips (OoCs)—3D cultures of heterotypic cells with appropriate extracellular matrices to better approximate the in vivo cellular microenvironment. Researchers are already working to validate whether OoCs are predictive of toxicity in humans. Here, we review two other key aspects of how OoCs may advance predictive toxicology—each taking advantage of OoCs as systems of intermediate complexity that remain experimentally accessible. First, the intermediate complexity of OoCs will help elucidate the scale(s) of organismal complexity that currently confound computational predictions of in vivo toxicity from in vitro data sets. Identifying the strongest confounding factors will help researchers improve the computational models underlying such predictions. Second, the experimental accessibility of OoCs will allow researchers to analyze chemical-exposure responses in OoCs using an array of high-content readouts—from fluorescent biosensors that report dynamic changes in specific cell signaling pathways to unbiased searches over broader biochemical space using technologies like ion mobility-mass spectrometry. Such high-content information on OoC responses will help determine the details of adverse outcome pathways. We note these possible uses of OoCs so that researchers and engineers can consider them in the design of next-generation OoC control, perfusion, and analysis platforms