Photochemical Z-->E isomerization of a hemithioindigo/hemistilbene omega-amino acid.

The molecule HTI, which combines hemithioindigo and hemistilbene molecular parts, allows reversible switching between two isomeric states. Photochromic behaviour of the HTI molecule is observed by irradiation with UV/Vis light. The photochemical reaction, a Z/E isomerization around the central double bond connecting the two molecular parts, is investigated by transient absorption and emission spectroscopy. For a special HTI molecule, namely, an omega-amino acid, the Z-->E isomerization process occurs on a timescale of 30 ps. In the course of the reaction fast processes on the 1-10 ps timescale are observed which point to motions of the molecule on the potential-energy surface of the excited state. The combination of transient absorption experiments in the visible spectral range with time-resolved fluorescence and infrared measurements reveal a photochemical pathway with three intermediate states. Together with a theoretical modelling procedure the experiments point to a sequential reaction scheme and give indications of the nature of the involved intermediates.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

Demonstration of the two-chamber approach for high-voltage measurements using collinear laser spectroscopy

An electronic measurement of high-voltages of several ten kV with accuracy as required for precision experiments is currently only feasible using highly sophisticated voltage dividers. Collinear laser spectroscopy can provide a direct and precise measurement of high-voltages using the Doppler shift of accelerated ions. Although proposed already in 1982, a measurement with relative accuracy better than 10^?4 was not reported so far. To improve this accuracy, a dedicated new setup for high-voltage measurements will be installed at the Technische Universität Darmstadt. A two-chamber approach will be used to remove uncertainties due to the insufficiently known starting potential inside the ion source. Here we present a demonstration of the pump-and-probe technique performed in preparatory studies at the TRIGA-LASER experiment in Mainz.     

Contact bactericides and fungicides on the basis of amino‐functionalized poly(norbornene)s

This study is aimed at investigating the microbiocidal potential of amino‐functionalized poly(norbornenes) in the solid state. A series of norbornene‐type monomers that carry secondary or tertiary amine functions as well as hexyl and dodecyl groups were prepared. Ring‐opening metathesis polymerization was used to prepare homopolymers of the amine bearing monomers and random copolymers of amine‐ and alkyl‐substituted monomers of high average molar mass. The resulting polymers were characterized by nuclear magnetic resonance, thermogravimetry, differential scanning calorimetry, infrared spectroscopy, and contact angle measurements, and their contact biocidal potential was evaluated according to the Japanese Industry standard Z2801. Tested microorganisms comprised Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, Candida albicans, and Aspergillus niger. Microbiocidal activity of selected polymer films against E. coli, S. aureus, and A. niger was found, whereas against C. albicans and P. aeruginosa microbiostatic behavior was observed. Moreover, the most potent copolymer revealed no cytotoxicity rendering a biocidal polymer with potential applications in mammalian‐, and in particular, human‐related fields. © 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2010     

Bayesian Multivariate Distributional Regression With Skewed Responses and Skewed Random Effects

The normal and the t distribution are classical tools for building random effects regression models where both can be used for the specification of either the conditional response distribution or the random effects distribution. However, the underlying assumption of symmetry can be questionable in many applications. We, therefore, propose regression models where the skew-normal and skew-t distribution are considered for both the response and the random effects specification and embed these models in the framework of distributional regression such that regression predictors can be specified for all distributional parameters. The distributional regression framework also allows us to consider multivariate versions of the skew-normal and the skew-t distribution. For Bayesian inference, we adapt iteratively weighted least-square proposals within Markov chain Monte Carlo simulations such that they can also facilitate the inclusion of nonnormal random effects specifications. Model choice is based on the Watanabe–Akaike information criterion, in particular, to differentiate between skew and nonskew distributional specifications in a number of simulation studies. Finally, to illustrate their practical applicability, the developed models are applied to a study on cholesterol levels originating from the Framingham Heart Study and a dataset from the Demographic and Health Surveys on undernutrition among children in Nigeria. Supplementary material for this article is available online.     

Plasma-Assisted Pretreatment of Wheat Straw

O₃ generated in a plasma at atmospheric pressure and room temperature, fed with dried air (or oxygen-enriched dried air), has been used for the degradation of lignin in wheat straw to optimize the enzymatic hydrolysis and to get more fermentable sugars. A fixed bed reactor was used combined with a CO₂ detector and an online technique for O₃ measurement in the fed and exhaust gas allowing continuous measurement of the consumption of O₃. This rendered it possible for us to determine the progress of the pretreatment in real time (online analysis). The process time can be adjusted to produce wheat straw with desired lignin content because of the online analysis. The O₃ consumption of wheat straw and its polymeric components, i.e., cellulose, hemicellulose, and lignin, as well as a mixture of these, dry as well as with 50% water, were studied. Furthermore, the process parameters dry matter content and milled particle size (the extent to which the wheat straw was milled) were investigated and optimized. The developed methodology offered the advantage of a simple and relatively fast (0.5–2 h) pretreatment allowing a dry matter concentration of 45–60%. FTIR measurements did not suggest any structural effects on cellulose and hemicellulose by the O₃ treatment. The cost and the energy consumption for lignin degradation of 100 g of wheat straw were calculated.     

On a Smooth Dual Gap Function for a Class of Quasi-Variational Inequalities

A well-known technique for the solution of quasi-variational inequalities (QVIs) consists in the reformulation of this problem as a constrained or unconstrained optimization problem by means of so-called gap functions. In contrast to standard variational inequalities, however, these gap functions turn out to be nonsmooth in general. Here, it is shown that one can obtain an unconstrained optimization reformulation of a class of QVIs with affine operator by using a continuously differentiable dual gap function. This extends an idea from Dietrich (J. Math. Anal. Appl. 235:380–393 [24]). Some numerical results illustrate the practical behavior of this dual gap function approach.     

Modular Assembly of Host–Guest Metal–Phenolic Networks Using Macrocyclic Building Blocks

The manipulation of interfacial properties has broad implications for the development of high‐performance coatings. Metal–phenolic networks (MPNs) are an emerging class of responsive, adherent materials. Herein, host–guest chemistry is integrated with MPNs to modulate their surface chemistry and interfacial properties. Macrocyclic cyclodextrins (host) are conjugated to catechol or galloyl groups and subsequently used as components for the assembly of functional MPNs. The assembled cyclodextrin‐based MPNs are highly permeable (even to high molecular weight polymers: 250–500 kDa), yet they specifically and noncovalently interact with various functional guests (including small molecules, polymers, and carbon nanomaterials), allowing for modular and reversible control over interfacial properties. Specifically, by using either hydrophobic or hydrophilic guest molecules, the wettability of the MPNs can be readily tuned between superrepellency (>150°) and superwetting (ca. 0°).     

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.