Chiral hydroperoxides as oxygen source in the catalytic stereoselective epoxidation of allylic alcohols by sandwich-type polyoxometalates: control of enantioselectivity through a metal-coordinated template

The epoxidation of allylic alcohols is shown to be efficiently and selectively catalyzed by the oxidatively resistant sandwich-type polyoxometalates, POMs, namely [WZnM(2)(ZnW(9)O(34))(2)](q)(-) [M = OV(IV), Mn(II), Ru(III), Fe(III), Pd(II), Pt(II), Zn(II); q = 10-12], with organic hydroperoxides as oxygen source. Conspicuous is the fact that the nature of the transition metal M in the central ring of polyoxometalate affects significantly the reactivity, chemoselectivity, regioselectivity, and stereoselectivity of the allylic alcohol epoxidation. For the first time, it is demonstrated that the oxovanadium(IV)-substituted POM, namely [ZnW(VO)(2)(ZnW(9)O(34))(2)](12-), is a highly chemoselective, regioselective, and also stereoselective catalyst for the clean epoxidation of allylic alcohols. A high enantioselectivity (er values up to 95:5) has been achieved with [ZnW(VO)(2)(ZnW(9)O(34))(2)](12)(-) and the sterically demanding TADOOL-derived hydroperoxide TADOOH as regenerative chiral oxygen source. Thus, a POM-catalyzed asymmetric epoxidation of excellent catalytic efficiency (up to 42 000 TON) has been made available for the development of sustainable oxidation processes. The high reactivity and selectivity of this unprecedented oxygen-transfer process are mechanistically rationalized in terms of a peroxy-type vanadium(V) template.     

The thermal conductivity of alumina nanoparticles dispersed in ethylene glycol

The thermal conductivities of several nanofluids (dispersions of alumina nanoparticles in ethylene glycol) were measured at temperatures ranging from 298 to 411 K using a liquid metal transient hot wire apparatus. Our measurements span the widest range of temperatures that have been investigated to date for any nanofluid. A maximum in the thermal conductivity versus temperature behavior was observed at all mass fractions of nanoparticles, closely following the behavior of the base fluid (ethylene glycol). Our results confirm that additional temperature contributions inherent in Brownian motion models are not necessary to describe the temperature dependence of the thermal conductivity of nanofluids. Our results also show that the effect of mass or volume fraction of nanoparticles on the thermal conductivity of nanofluids can be correlated using the Hamilton and Crosser or Yu and Choi models with one adjustable parameter (the shape factor in the Hamilton and Crosser model, or the ordered liquid layer thickness in the Yu and Choi model).     

Systematic Comparison of Peptidic Proteasome Inhibitors Highlights the α‐Ketoamide Electrophile as an Auspicious Reversible Lead Motif

The ubiquitin–proteasome system (UPS) has been successfully targeted by both academia and the pharmaceutical industry for oncological and immunological applications. Typical proteasome inhibitors are based on a peptidic backbone endowed with an electrophilic C‐terminus by which they react with the active proteolytic sites. Although the peptide moiety has attracted much attention in terms of subunit selectivity, the target specificity and biological stability of the compounds are largely determined by the reactive warheads. In this study, we have carried out a systematic investigation of described electrophiles by a combination of in vitro, in vivo, and structural methods in order to disclose the implications of altered functionality and chemical reactivity. Thereby, we were able to introduce and characterize the class of α‐ketoamides as the most potent reversible inhibitors with possible applications for the therapy of solid tumors as well as autoimmune disorders.     

Analysis of monoclonal antibody by a novel CE‐UV/MALDI‐MS interface

mAbs are highly complex proteins that present a wide range of microheterogeneity that requires multiple analytical methods for full structure assessment and quality control. As a consequence, the characterization of mAbs on different levels is particularly product‐ and time‐consuming. CE‐MS couplings, especially to MALDI, appear really attractive methods for the characterization of biological samples. In this work, we report the last instrumental development and performance of the first totally automated off‐line CE‐UV/MALDI‐MS/MS. This interface is based on the removal of the original UV cell of the CE apparatus, modification of the spotting device geometry, and creation of an integrated delivery matrix system. The performance of the method was evaluated with separation of five intact proteins and a tryptic digest mixture of nine proteins. Intact protein application shows the acquisition of electropherograms with high resolution and high repeatability. In the peptide mapping approach, a total number of 154 unique identified peptides were characterized using MS/MS spectra corresponding to average sequence coverage of 64.1%. Comparison with NanoLC/MALDI‐MS/MS showed complementarity at the peptide level with an increase of 42% when using CE/MALDI‐MS coupling. Finally, this work represents the first analysis of intact mAb charge variants by CZE using an MS detection. Moreover, using a peptide mapping approach CE‐UV/MALDI‐MS/MS fragmentation allowed 100% sequence coverage of the light chain and 92% of the heavy chain, and the separation of four major glycosylated peptides and their structural characterization.     

Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

High-dimensional partial differential equations (PDEs) appear in a number of models from the financial industry, such as in derivative pricing models, credit valuation adjustment models, or portfolio optimization models. The PDEs in such applications are high-dimensional as the dimension corresponds to the number of financial assets in a portfolio. Moreover, such PDEs are often fully nonlinear due to the need to incorporate certain nonlinear phenomena in the model such as default risks, transaction costs, volatility uncertainty (Knightian uncertainty), or trading constraints in the model. Such high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the computational effort for standard approximation methods grows exponentially with the dimension. In this work, we propose a new method for solving high-dimensional fully nonlinear second-order PDEs. Our method can in particular be used to sample from high-dimensional nonlinear expectations. The method is based on (1) a connection between fully nonlinear second-order PDEs and second-order backward stochastic differential equations (2BSDEs), (2) a merged formulation of the PDE and the 2BSDE problem, (3) a temporal forward discretization of the 2BSDE and a spatial approximation via deep neural nets, and (4) a stochastic gradient descent-type optimization procedure. Numerical results obtained using TensorFlow in Python illustrate the efficiency and the accuracy of the method in the cases of a 100-dimensional Black–Scholes–Barenblatt equation, a 100-dimensional Hamilton–Jacobi–Bellman equation, and a nonlinear expectation of a 100-dimensional G-Brownian motion.

     

Measurability of vacuum fluctuations and dark energy

Vacuum fluctuations of the electromagnetic field induce current fluctuations in resistively shunted Josephson junctions that are measurable in terms of a physically relevant power spectrum. In this paper we investigate under which conditions vacuum fluctuations can be gravitationally active, thus contributing to the dark energy density of the universe. Our central hypothesis is that vacuum fluctuations are gravitationally active if and only if they are measurable   in terms of a physical power spectrum in a suitable macroscopic or mesoscopic detector. This hypothesis is consistent with the observed dark energy density in the universe and offers a resolution of the cosmological constant problem. Using this hypothesis we show that the observable vacuum energy density _ρvac${\rho }_{\mathit{vac}}$ in the universe is related to the largest possible critical temperature _Tc$T_c$ of superconductors through _ρvac=σ·(_kTc)⁴/?³c³${\rho }_{\mathit{vac}}=\sigma ·({\mathit{kT}}_c{)}^4/{?}^3{c}^3$, where σ$\sigma$ is a small constant of the order 10^-3${10}^{-3}$. This relation can be regarded as an analog of the Stefan–Boltzmann law for dark energy. Our hypothesis is testable in Josephson junctions where we predict there should be a cutoff in the measured spectrum at 1.7 THz if the hypothesis is true.     

Tetracyanoquinodimethane Reduction by Complexed Guanidinyl‐Functionalized Aromatic Compounds

In this work, we report on the reduction of tetracyanoquinodimethane (TCNQ) with dicationic complexes of guanidinyl‐functionalized aromatic (GFA) electron donors. In contrast to reduction with free GFAs, milder reduction conditions were achieved, and this led to semiconducting materials with extended TCNQ π stacking. The charge on the TCNQ units was estimated from the structural data obtained by single‐crystal X‐ray diffraction analysis and from IR spectroscopic data. The electrical conductivity was studied and the activation energy of the semiconducting materials was estimated from the temperature dependence of the conductivity.     

Demonstrating the power of extended Masing models for hysteresis through model equivalencies and numerical investigation

Nonlinear Dynamics - The extended Masing model (EMM) is a powerful model for hysteresis that is theoretically sound, physically meaningful and computationally efficient. Any such model is defined...