Simultaneous detection of ultraviolet and infrared radiation in a single GaN/GaAlN heterojunction

Results are presented for a dual-band detector that simultaneously detects UV radiation in the 250-360 nm and IR radiation in the 5-14 microm regions with near zero spectral cross talk. In this detector having separate UV- and IR-active regions with three contacts (one common contact for both regions) allows the separation of the UV and IR generated photocurrent components, identifying the relative strength of each component. This will be an important development in UV-IR dual-band applications such as fire-flame detection, solar astronomy, and military sensing, eliminating the difficulties of employing several individual detectors with separate electronics-cooling mechanisms.     

Taking compact NMR to monitoring real reactions in large-scale chemical industries—General considerations and learnings from a lab-scale test case

The considerations for use of compact nuclear magnetic resonance in a large-scale industrial environment clearly differ from those in academic and educational settings and even from those in smaller companies. In the first part of this article, these differences will be discussed along with the additional requirements that need to be fulfilled for successful applicability in different use cases. In the second part of the article, outcomes from different research activities aiming to fulfill these requirements will be presented with a focus on an online reaction-monitoring study on a lab-scale nucleophilic chlorination reaction.     

Guayulin B

The sesquiterpene guayulin B, C₂₃H₃₀O₃, 1, crystallizes with two independent molecules in monoclinic space group P2₁ with a = 18.099 (1), b = 6.0649(6), c = 19.126(1) Å, = 100.82(1)°, V = 2062.1(5) ų, and Z = 4. The double bonds of the cyclodecadiene ring are E, both methyl groups on the 10-membered ring are -oriented. The cyclopropane ring at C6 and C7 also has the orientation, while the anisoyloxy group is oriented . The 10-membered rings of the two independent molecules have the same conformation as that of guayulin A.     

The pseudoguaianolide isoconfertiflorin

The sesquiterpene lactone isoconfertiflorin, C₁₇H₂₂O₅, 1, crystallizes in trigonal space group P3₁ with a = 10.4498(5), c = 12.7516(8) Å, V = 1205.9(2) ų, and Z = 3. It differs from confertiflorin in having an endocyclic C=C bond in the lactone ring, which carries an exocyclic methyl group. The seven-membered ring adopts an approximate twist-chair conformation with C10 lying in the local two-fold axis, and asymmetry parameter C₂ = 5.8°. The cyclopentanone ring has the half-chair conformation with C4 on the local two-fold axis, and C₂ = 0.9°. The lactone ring is almost planar with maximum deviation 0.014(2) Å. Both methyl groups on the 7-membered ring are –oriented. The acetyl group at C8 has an orientation.     

A probabilistic approach to classifying metabolic stability

Metabolic stability is an important property of drug molecules that should-optimally-be taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays being implemented in early drug discovery, a prediction tool for this property could be of high value. However, metabolic stability is inherently difficult to predict, and no commercial tools are available for this purpose. In this work, we present a machine learning approach to predicting metabolic stability that is tailored to compounds from the drug development process at Bayer Schering Pharma. For four different in vitro assays, we develop Bayesian classification models to predict the probability of a compound being metabolically stable. The chosen approach implicitly takes the "domain of applicability" into account. The developed models were validated on recent project data at Bayer Schering Pharma, showing that the predictions are highly accurate and the domain of applicability is estimated correctly. Furthermore, we evaluate the modeling method on a set of publicly available data.     

Accurate solubility prediction with error bars for electrolytes: a machine learning approach

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.     

Metastable liquid clusters in super- and undersaturated protein solutions

Dense liquid phases, metastable with respect to a solid phase, but stable with respect to the solution, have been known to form in solutions of proteins and small-molecule substances. Here, with the protein lumazine synthase as a test system, using dynamic and static light scattering and atomic force microscopy, we demonstrate submicron size clusters of dense liquid. In contrast to the macroscopic dense liquid, these clusters are metastable not only with respect to the crystals, but also with respect to the low-concentration solution: the characteristic cluster lifetime is limited to approximately 10 s, after which they decay. The cluster population is detectable only if they occupy >10(-6) of the solution volume and have a number density >105 cm-3 for 3 to 11% of the monitored time. The cluster volume fraction varies within wide limits and reaches up to 10(-3). Increasing protein concentration increases the frequency of cluster detection but does not affect the ranges of the cluster sizes, suggesting that a preferred cluster size exists. A simple Monte Carlo model with protein-like potentials reproduces the metastable clusters of dense liquid with limited lifetimes and variable sizes and suggests that the mean cluster size is determined by the kinetics of growth and decay and not by thermodynamics.