Dislocation-tuned electrical conductivity in solid electrolytes (9YSZ): A micro-mechanical approach

Tailoring the electrical conductivity of functional ceramics by introducing dislocations is a comparatively recent research focus, and its merits were demonstrated through mechanical means. Especially bulk deformation at high temperatures is suggested to be a promising method to introduce a high dislocation density. So far, however, controlling dislocation generation and their annihilation remains difficult. Although deforming ceramics generate dislocations on multiple length scales, dislocation annihilation at the same time appears to be the bottleneck to use the full potential of dislocations-tailoring the electrical conductivity. Here, we demonstrate the control over these aspects using a micromechanical approach on yttria-stabilized zirconia - YSZ. Targeted indentation well below the dislocation annihilation temperature resulted in extremely dense dislocation networks, visualized by chemical etching and electron channeling contrast imaging. Microcontact-impedance measurements helped evaluate the electrical response of operating individual slip systems. A significant conductivity enhancement is revealed in dislocation-rich regions compared to pristine ones in fully stabilized YSZ. This enhancement is mainly attributed to oxygen ionic conductivity. Thus, the possibility of increasing the conductivity is illustrated and provides a prospect to transfer the merits of dislocation-tuned electrical conductivity to solid oxygen electrolytes.     

The Influence of Zinc and Citrate on T Cell Activation and Differentiation in Mixed Lymphocyte Cultures

Scope

Zinc is important for a balanced immune system, but the mechanisms are not yet fully elucidated. One possibility is an interaction of zinc with the tricarboxylic acid cycle (TCA), in which zinc inhibits the mitochondrial aconitase leading to an increase in intracellular citrate concentration as described for prostate cells. Therefore, the immune modulatory effects of zinc and citrate and their interaction in mixed lymphocyte cultures (MLC) are studied.

Methods and results

After allogeneic (MLC) or superantigen stimulation, the interferon-γ (IFNγ) production is quantified by ELISA and T cell subpopulations are determined by Western Blot. Intracellular concentrations of citrate and zinc are measured. Zinc and citrate reduce the IFNγ expression and the pro-inflammatory T helper cells (Th) 1 and Th17 in MLC. While zinc increases regulatory T cells, citrate reduces them. After superantigen stimulation IFNγ production is decreased only by citrate but increased by zinc. Zinc does not affect citrate concentration, while citrate impairs zinc uptake. Thus, zinc and citrate independently regulate IFNy expression.

Conclusion

These results may explain the immunosuppressive effect of blood products anticoagulated by citrate. In addition, high citrate consumption may lead to immunosuppressive effects, so upper limits for citrate should be established.     

Karl-Fischer-Reaktion in Dimethylsulfoxid

Karl-Fischer-Reaction in Dimethyl Sulfoxide The water equivalent (titer) of methanolic Karl-Fischer-reagents rises if dimethyl sulfoxide (DMSO) is used as an additional solvent. It is shown that the probable reason for this observation is the formation of iodine by the reaction between DMSO, iodide and sulfur trioxide.     

Synthesis of Aminoethyl-Substituted Piperidine Derivatives as σ1 Receptor Ligands with Antiproliferative Properties

A series of novel σ₁ receptor ligands with a 4-(2-aminoethyl)piperidine scaffold was prepared and biologically evaluated. The underlying concept of our project was the improvement of the lipophilic ligand efficiency of previously synthesized potent σ₁ ligands. The key steps of the synthesis comprise the conjugate addition of phenylboronic acid at dihydropyridin-4(1H)-ones 7 , homologation of the ketones 8 and introduction of diverse amino moieties and piperidine N-substituents. 1-Methylpiperidines showed particular high σ₁ receptor affinity and selectivity over the σ₂ subtype, whilst piperidines with a proton, a tosyl moiety or an ethyl moiety exhibited considerably lower σ₁ affinity. Molecular dynamics simulations with per-residue binding free energy deconvolution demonstrated that different interactions of the basic piperidine-N-atom and its substituents (or the cyclohexane ring) with the lipophilic binding pocket consisting of Leu105, Thr181, Leu182, Ala185, Leu186, Thr202 and Tyr206 are responsible for the different σ₁ receptor affinities. Recorded logD_7.4 and calculated clogP values of 4a and 18a indicate low lipophilicity and thus high lipophilic ligand efficiency. Piperidine 4a inhibited the growth of human non-small cell lung cancer cells A427 to a similar extent as the σ₁ antagonist haloperidol. 1-Methylpiperidines 20a , 21a and 22a showed stronger antiproliferative effects on androgen negative human prostate cancer cells DU145 than the σ₁ ligands NE100 and S1RA.     

Synthese von Dibenzodiazoninonen

Efficient total syntheses of dibenzodiazoninones 4 / 5 are reported which are of chemical and pharmacological interest as structural modifications of the bryophyte constituent anthocerodiazonin 1 . The nine-membered aza lactam structure was confirmed by X-ray structural analysis of compound 5 .     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.     

Untersuchungen zur Vergleichbarkeit der statischen und dynamischen Überdruckprüfung von druckfesten Kapselungen

Forschung im Ingenieurwesen - Der Einsatz von elektrischen Geräten in explosionsgefährdeten Bereichen erfordert besondere Schutzmaßnahmen, um eine Zündung der...