A single director switching mode for monodomain liquid crystal elastomer

We report rotation of a single director in a nematic monodomain, acrylate based side-chain elastomer which was subjected to mechanical fields applied at angles in the range to the director, , present at the time of network formation. Time and spatially resolving wide angle X-ray scattering, together with polarised light microscopy measurements revealed a pronounced, almost discontinuous switching mode at a critical extension as the strain was applied at angles approaching to , whereas a more continuous rotation was seen when the strain was applied at more acute angles. This director reorientation was more or less uniform across the complete sample and was accompanied by a modest decrease in orientation parameter . At strains sufficient to induce switching there was some continuous distribution of director orientations with fluctuations of 10 although there was no evidence for any localised director inhomogenities such as domain formation. The observed deformation behaviour of these acrylate-based nematic monodomains was in accord with the predictions of a theory developed by Bladon et al., in that the complete set of data could be accounted for through a single parameter describing the chain anisotropy. The experimentally deduced chain anisotropy parameter was in broad agreement with that obtained from small-angle neutron scattering procedures, but was somewhat greater than that obtained by spontaneous shape changes at the nematic-isotropic transition.

Evaluation of empirical mode decomposition for event-related potential analysis

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.