Sensitive CE-MS method for monitoring of riociguat and desmethylriociguat levels in human serum

Riociguat is novel antihypertensive drug for treatment of pulmonary hypertension. As such, it is still being tested in many clinical and pharmacokinetic trials. Existing methods that determine serum riociguat and desmethylriociguat (DMR) are based solely on liquid chromatography with mass spectrometry. Therefore, we present a novel capillary electrophoresis with mass spectrometry method (CE-MS) for their determination in human serum as alternative method for ongoing trials. Complete resolution of both analytes was achieved by means of pH optimization of ammonium formate background electrolytes that are fully compatible with ESI/MS detection. Simple liquid-liquid extraction was used as sample pretreatment. The calibration dependence of the method was linear (in the range of 10–1000 ng/mL), with adequate accuracy (90.1–114.9%) and precision (13.4%). LOD and LOQ were arbitrarily set at 10 ng/mL for both analytes. Clinical applicability was validated using serum samples from patients treated with riociguat in pharmacokinetic study and the results corresponded with reference HPLC-MS/MS values. Capillary electrophoresis proved to be sensitive and selective tool for the analysis of riociguat and DMR.     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

3D‐morphology reconstruction of nanoscale phase‐separation in polymer memory blends

In many organic electronic devices functionality is achieved by blending two or more materials, typically polymers or molecules, with distinctly different optical or electrical properties in a single film. The local scale morphology of such blends is vital for the device performance. Here, a simple approach to study the full 3D morphology of phase‐separated blends, taking advantage of the possibility to selectively dissolve the different components is introduced. This method is applied in combination with AFM to investigate a blend of a semiconducting and ferroelectric polymer typically used as active layer in organic ferroelectric resistive switches. It is found that the blend consists of a ferroelectric matrix with three types of embedded semiconductor domains and a thin wetting layer at the bottom electrode. Statistical analysis of the obtained images excludes the presence of a fourth type of domains. The criteria for the applicability of the presented technique are discussed. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2015 , 53, 1231–1237     

Determination of 5-nitroindazole using silver solid amalgam electrode

Monatshefte für Chemie - Chemical Monthly - The voltammetric behavior of 5-nitroindazole was investigated at polished (p-AgSAE) and at mercury meniscus-modified (m-AgSAE) silver solid amalgam...