Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Using new structurally related additive schemes in the precalculation of gas chromatographic retention indices of polychlorinated hydroxybiphenyls on HP-5 stationary phase

A new additive scheme is proposed for the precalculation of gas chromatographic retention indices of complex organic compounds. The principal feature of this approach is the absence of previously calculated I increments for any structural fragments or functional groups in the molecule. Instead, arithmetical operations involving I values of simpler structural analogues of target compounds are used directly. I precalculation for polychlorinated hydroxybiphenyls (839 congeners) on the HP-5 stationary phase was chosen as one of the most important applications of the method under discussion. Such a large number of congeners cannot be obtained as reference samples and their gas chromatographic (GC)-mass spectrometric (MS) identification should therefore be based currently on precalculated I values.     

Correlation of antioxidant depletion and mechanical performance during thermal degradation of an HTPB elastomer

Thermal degradation studies of a stabilized HTPB based elastomer were conducted at temperatures from 50 °C to 110 °C. The concentration of extractable antioxidant (AO2246) in the polymer was quantified via AO extraction and a gas chromatography-based method using internal standards. The decrease in extractable AO levels as a function of time and temperature was evaluated and correlated with mechanical property changes. Most importantly, AO depletion features were found to be temperature dependent. At elevated temperatures (>80 °C) extractable AO levels decreased rapidly and faster than the concurrent loss in mechanical properties. While extractable AO concentrations decrease quickly, the material is able to maintain some useful mechanical properties, perhaps via non-extractable or grafted AO species formed during degradation providing additional protection. At lower aging temperatures extractable or free AO levels decreased more slowly than the mechanical properties. Therefore, for condition monitoring purposes a universal correlation between AO levels and aging state or material condition could not be established. Most importantly, however, loss of mechanical properties and oxidative degradation is observed at lower temperatures despite significant levels of free antioxidant in the material. The antioxidant appears to be limited in its effectiveness to completely prevent degradation reactions, or only fractions of the total AO available are actually involved in the inhibition process.     

Computer-assisted choice of discrete spacers for anionic isotachophoresis separations

In isotachophoresis (ITP), the sample constituents migrate, depending on their concentrations in the loaded sample, either in fully developed zones or in the boundary layers between the zones of constituents of the corresponding effective mobilities. The latter (spike) migration mode is analytically beneficial in selective detections of trace analytes, especially, when appropriately chosen discrete spacers minimize detection interferences due to matrix constituents. To facilitate a search for suitable mixtures of discrete spacers, a two-step calculation procedure was developed in this work. Using a pool of discrete spacers consisting of 42 anionic and zwitterionic constituents, this procedure was shown effective in the anionic ITP separations performed at pH = 6.5-10.0. Besides the predictions of the migration orders, it was helpful in identifying the spacing constituents that could cause resolution problems due to an uncertainty with which pH of the leading electrolyte solution is known. The ionic mobility and pKa data, taken for the spacing constituents from the literature and the ones obtained from the ITP experiments carried out in this work, were used in the calculations performed in a context with the choice of spacers. Although the data obtained from the ITP experiments provided better results, small uncertainties with which they were acquired (attributable to fluctuations in the experimental conditions) set practical limits in the calculation based choice of multi-component mixtures of the spacing constituents.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

The effect of the sintering atmosphere on the densification of B4C ceramics

Densification of boron carbide during sintering may be improved by a two-stage process, namely heating to 2000°C under vacuum and sintering at 2190°C under argon. This sintering regime allows achieving a relative density of the ceramic bodies fabricated from a fine powder higher than 95%. The nitrogen treatment of the boron carbide phase at 1900°C leads to the formation of the BN phase and precipitation of graphite. Vacuum treatment of these samples at 2000°C leads to decomposition of the boron nitride phase. The liberated free boron may again react with graphite to form in situ boron carbide particles. The experimental investigations of the sintering behavior of the boron carbide phase under various atmospheres supported the thermodynamic predictions regarding the phase transformation. No evidence, however, was found for enhanced sintering under a nitrogen atmosphere.