Whole-Cell Photoenzymatic Cascades to Synthesize Long-Chain Aliphatic Amines and Esters from Renewable Fatty Acids

Long-chain aliphatic amines such as (S,Z)-heptadec-9-en-7-amine and 9-aminoheptadecane were synthesized from ricinoleic acid and oleic acid, respectively, by whole-cell cascade reactions using the combination of an alcohol dehydrogenase (ADH) from Micrococcus luteus, an engineered amine transaminase from Vibrio fluvialis (Vf-ATA), and a photoactivated decarboxylase from Chlorella variabilis NC64A (Cv-FAP) in a one-pot process. In addition, long chain aliphatic esters such as 10-(heptanoyloxy)dec-8-ene and octylnonanoate were prepared from ricinoleic acid and oleic acid, respectively, by using the combination of the ADH, a Baeyer–Villiger monooxygenase variant from Pseudomonas putida KT2440, and the Cv-FAP. The target compounds were produced at rates of up to 37 U g⁻¹ dry cells with conversions up to 90 %. Therefore, this study contributes to the preparation of industrially relevant long-chain aliphatic chiral amines and esters from renewable fatty acid resources.     

A Predictive Model Towards Site‐Selective Metalations of Functionalized Heterocycles,Arenes, Olefins,and Alkanes using TMPZnCl⋅LiCl

The development of a predictive model towards site‐selective deprotometalation reactions using TMPZnCl?LiCl is reported (TMP=2,2,6,6‐tetramethylpiperidinyl). The pK_a values of functionalized N‐, S‐, and O‐heterocycles, arenes, alkenes, or alkanes were calculated and compared to the experimental deprotonation sites. Large overlap (>80 %) between the calculated and empirical deprotonation sites was observed, showing that thermodynamic factors strongly govern the metalation regioselectivity. In the case of olefins, calculated frozen state energies of the deprotonated substrates allowed a more accurate prediction. Additionally, various new N‐heterocycles were analyzed and the metalation regioselectivities rationalized using the predictive model.     

Extremely Low Vapor-Pressure Data as Access to PC-SAFT Parameter Estimation for Ionic Liquids and Modeling of Precursor Solubility in Ionic Liquids

Precursor solubility is a crucial factor in industrial applications, dominating the outcome of reactions and purification steps. The outcome and success of thermodynamic modelling of this industrially important property with equations of states, such as Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), vastly depends on the quality of the pure-component parameters. The pure-component parameters for low-volatile compounds such as ionic liquids (ILs) have been commonly estimated using mixture properties, e. g. the osmotic pressure of aqueous solutions. This leads to parameters that depend on the solvent, and transferability to other mixtures often causes poor modeling results. Mixture-independent experimental properties would be a more suitable basis for the parameter estimation offering a way to universal parameter sets. Model parameters for ILs are available in the literature [10.1016/j.fluid.2012.05.029], but they were estimated using pure-IL density data. The present work focuses on a step towards a more universal estimation strategy that includes new experimental vapor-pressure data of the pure IL. ILs exhibit an almost negligible vapor pressure in magnitude of usually 10⁻⁵ Pa even at elevated temperatures. In this work, such vapor-pressure data of a series of 1-ethyl-3-methyl-imidazolium-based [C₂mim]-ILs with various IL-anions (e. g. tetrafluoroborate [BF₄]⁻, hexafluorophosphate [PF₆]⁻, bis(trifluoromethylsulfonyl)imide [NTf₂]⁻) were experimentally determined and subsequently used for PC-SAFT parameter estimation. The so-determined parameters were used to predict experimental molecular precursor solubility in ILs and infinitely diluted activity coefficients of various solvents in ILs. The parameters were further compared to modeling results using classical parametrization methods (use of liquid-density data only for the molecular PC-SAFT and the ion-based electrolyte PC-SAFT). As a result, the modeled precursor solubilities using the new approach are much more precise than using the classical parametrization methods, and required binary parameters were found to be much smaller (if needed). In sum, including the pure-component vapor-pressure data of ILs opens the door towards parameter estimation that is not biased by mixture data. This procedure might be suitable also for polymers and for all kind of ionic species but needs extension to ion-specific parametrization in the long term.     

Mass Activated Droplet Sorting (MADS) Enables High‐Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Microfluidic droplet sorting enables the high‐throughput screening and selection of water‐in‐oil microreactors at speeds and volumes unparalleled by traditional well‐plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for high‐throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESI‐MS). Droplets are split, one portion is analyzed by ESI‐MS, and the second portion is sorted based on the MS result. Throughput of 0.7 samples s^?1 is achieved with 98 % accuracy using a self‐correcting and adaptive sorting algorithm. We use the system to screen ≈15 000 samples in 6 h and demonstrate its utility by sorting 25 nL droplets containing transaminase expressed in vitro. Label‐free ESI‐MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.     

Electrochemiluminescence Bioassays with a Water‐Soluble Luminol Derivative Can Outperform Fluorescence Assays

The most efficient and commonly used electrochemiluminescence (ECL) emitters are luminol, [Ru(bpy)₃]²⁺, and derivatives thereof. Luminol stands out due to its low excitation potential, but applications are limited by its insolubility under physiological conditions. The water‐soluble m‐carboxy luminol was synthesized in 15 % yield and exhibited high solubility under physiological conditions and afforded a four‐fold ECL signal increase (vs. luminol). Entrapment in DNA‐tagged liposomes enabled a DNA assay with a detection limit of 3.2 pmol L^?1, which is 150 times lower than the corresponding fluorescence approach. This remarkable sensitivity gain and the low excitation potential establish m‐carboxy luminol as a superior ECL probe with direct relevance to chemiluminescence and enzymatic bioanalytical approaches.     

Novel sb induced reconstruction of the (113) surface of ge

Sb induces on Ge(113) a c(2 x 2) reconstruction in which Sb breaks one Ge-Ge bond and occupies an interstitial site, in contrast to Sb adsorption on other Si or Ge surfaces. Sb saturates the three dangling bonds per unit cell of the (113) surface inducing a large strain which is released by occupation of the interstitial site. Two neighboring Sb at interstitial sites form a dimer. The structure has been determined by x-ray diffraction, applying direct methods, and ab initio density-functional-theory calculations. The adsorption geometry and the high binding energy lead one to expect that Sb cannot be used as a surfactant for the growth of Si/Ge layers on the (113) surface.     

Fermionic atoms in a three dimensional optical lattice: observing Fermi surfaces, dynamics, and interactions

We have studied interacting and noninteracting quantum degenerate Fermi gases in a three-dimensional optical lattice. We directly image the Fermi surface of the atoms in the lattice by turning off the optical lattice adiabatically. Because of the confining potential, gradual filling of the lattice transforms the system from a normal state into a band insulator. The dynamics of the transition from a band insulator to a normal state is studied, and the time scale is measured to be an order of magnitude larger than the tunneling time in the lattice. Using a Feshbach resonance, we increase the interaction between atoms in two different spin states and dynamically induce a coupling between the lowest energy bands. We observe a shift of this coupling with respect to the Feshbach resonance in free space which is anticipated for strongly confined atoms.     

Resonator-enhanced optical dipole trap for fermionic lithium atoms

We demonstrate a novel optical dipole trap that is based on enhancement of the optical power density of a Nd:YAG laser beam in a resonator. The trap is particularly suited for experiments with ultracold gases, as it combines a potential depth of the order of 1 mK with storage times of several tens of seconds. We study the interactions in a gas of fermionic lithium atoms in our trap and observe the influence of spin-changing collisions and off-resonant photon scattering. A key element in reaching long storage times is the use of an ultralow-noise laser. The dependence of storage time on laser noise is investigated.     

Nonlinear robust optimization via sequential convex bilevel programming

In this paper, we present a novel sequential convex bilevel programming algorithm for the numerical solution of structured nonlinear min–max problems which arise in the context of semi-infinite programming. Here, our main motivation are nonlinear inequality constrained robust optimization problems. In the first part of the paper, we propose a conservative approximation strategy for such nonlinear and non-convex robust optimization problems: under the assumption that an upper bound for the curvature of the inequality constraints with respect to the uncertainty is given, we show how to formulate a lower-level concave min–max problem which approximates the robust counterpart in a conservative way. This approximation turns out to be exact in some relevant special cases and can be proven to be less conservative than existing approximation techniques that are based on linearization with respect to the uncertainties. In the second part of the paper, we review existing theory on optimality conditions for nonlinear lower-level concave min–max problems which arise in the context of semi-infinite programming. Regarding the optimality conditions for the concave lower level maximization problems as a constraint of the upper level minimization problem, we end up with a structured mathematical program with complementarity constraints (MPCC). The special hierarchical structure of this MPCC can be exploited in a novel sequential convex bilevel programming algorithm. We discuss the surprisingly strong global and locally quadratic convergence properties of this method, which can in this form neither be obtained with existing SQP methods nor with interior point relaxation techniques for general MPCCs. Finally, we discuss the application fields and implementation details of the new method and demonstrate the performance with a numerical example.