Architectural effect on the surface tension of an ABA triblock copolymer melt

The excess surface energy of lamellae formed by an ABA triblock copolymer melt oriented parallel to a neutral surface is evaluated using self-consistent field theory (SCFT). Consistent with experiments and previous SCFT calculations, we find a preference for the A-rich domains at the surface, which can only be attributed to the architectural asymmetry between the A and B blocks. The behavior was previously attributed to a loss of bridging configurations that occurs when the B-domain resides at the surface. Here we demonstrate that it is actually the presence of chain ends that reduces the excess surface energy of an A-rich domain relative that of a B-rich domain.

Solid-phase synthesis of an A-B loop mimetic of the C epsilon 3 domain of human IgE: macrocyclization by Sonogashira coupling

The solid-phase synthesis of a cyclic peptide containing the 21-residue epitope found in the A-B loop of the Cepsilon3 domain of human immunoglobulin E has been carried out. The key macrocyclization step to form the 65-membered ring is achieved in similar to15% yield via an "on-resin" Sonogashira coupling reaction which concomitantly installs a diphenylacetylene amino acid conformational constraint within the loop.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.