Predicting thermal displacements in modular tool systems

In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated first from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and again get promising results. The thermally induced errors can be estimated with 1-2 microm accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.     

Probing molecular free energy landscapes by periodic loading

Single molecule pulling experiments provide information about interactions in biomolecules that cannot be obtained by any other method. However, the reconstruction of the molecule's free energy profile from the experimental data is still a challenge, in particular, for the unstable barrier regions. We propose a new method for obtaining the full profile by introducing a periodic ramp and using Jarzynski's relation for obtaining equilibrium quantities from nonequilibrium data. Our simulated experiments show that this method delivers significant more accurate data than previous methods, under the constraint of equal experimental effort.     

Use of an ionic liquid in a two-phase system to improve an alcohol dehydrogenase catalysed reduction

Due to favourable partition coefficients the highly enantioselective reduction of 2-octanone, catalysed by an alcohol dehydrogenase from Lactobacillus brevis, is faster in a biphasic system containing buffer and the ionic liquid [BMIM][(CF(3)SO(2))(2)N] compared to the reduction in a biphasic system containing buffer and methyl tert-butyl ether.     

Reversal of diastereoselectivities in intra- and intermolecular reactions of 2-adamantanylidenes primarily caused by electron-donating and electron- withdrawing substituents on C5

[reaction: see text] A reversal of diastereoselectivity was observed for novel 5-(trimethylsilyl)adamantan-2-ylidene (1c) with regard to 5-hydroxyadamantan-2-ylidene (1a). Ostensibly in intermolecular reactions, 5-substituted 2-adamantanylidenes (1) are sterically unbiased. However, inductive effects originating from the pendant group bend the divalent carbon bridge of 1 either toward (ERG's, e.g., -Si(CH(3))(3)) or away from (EWG's, e.g., -OH) the gamma-position. Hence, the more exposed side is more susceptible to intermolecular reaction and the other side concomitantly undergoes intramolecular 1,3-CH insertions more readily.