Correlation of primary and secondary relaxations in a supercooled liquid

The widespread assumption that primary and secondary relaxations in glass-forming materials are independent processes is scrutinized using spin-lattice relaxation weighted stimulated-echo spectroscopy. This nuclear magnetic resonance (NMR) technique is simultaneously sensitive to the dynamics on well-separated time scales. For the deeply supercooled liquid sorbitol, which exhibits a strong secondary relaxation, the primary relaxation (that is observable using NMR) can be modified by suppressing the contributions of those subensembles which are characterized by relatively slow secondary relaxations. This is clear evidence for a correlation between primary and secondary relaxation times. In the disordered crystal orthocarborane high-frequency processes are absent and consequently no such modifications could be achieved.

Correlation of primary relaxations and high-frequency modes in supercooled liquids. II. Evidence from spin-lattice relaxation weighted stimulated-echo spectroscopy

Using spin-lattice relaxation weighted stimulated-echo spectroscopy, we report evidence for a correlation of the primary and secondary relaxation times. The experiments are performed using deuteron nuclear magnetic resonance somewhat above the calorimetric glass-transition of ortho-terphenyl, D-sorbitol, and cresolphthalein-dimethylether. The data analysis is based on the procedure outlined in the accompanying theoretical paper [B. Geil, G. Diezemann, and R. Böhmer, Phys. Rev. E 74, 041504 (2006)]. Direct experimental evidence for a modified spin-lattice relaxation is obtained from measurements on a methyl deuterated acetyl salicylic acid glass. The limitations of the present experimental method are discussed.

Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli

AIMS

This article presents models of artificial neural networks (ANN) employed to predict the biological activity of chemical compounds based of their structure. Regression and classification models were designed to determine antimicrobial properties of quaternary ammonium salts against Escherichia coli strain.

METHODS AND RESULTS

The minimum inhibitory concentration microbial growth E. coli was experimentally determined by the serial dilution method for a series of 140 imidazole derivatives. Then, three-dimensional models for imidazole chlorides were constructed with computational chemistry methods which allowed to calculate molecular descriptors. The transformation of chemical information into a useful number is a main result of this operation. The designed regression and classification ANN models were characterized by a high predictive ability (classification accuracy was 95%, regression model: learning set R = 0.87, testing set R = 0.91, validation set R = 0.89).

CONCLUSIONS

Artificial neural networks can be successfully used to find potential antimicrobial preparations.

SIGNIFICANCE AND IMPACT OF THE STUDY

The neural networks are a very elaborate modelling technique, which allows not only to optimize and minimize labour costs but also to increase food safety.