3-and 3,4-substituted pyrroles and thiophenes and their corresponding polymers: a review

In the field of conducting polymers, both poly(pyrrole) and poly(thiophene) have been investigated extensively and are used currently in a wide variety of applications including microelectronics, electrode materials, sensors and optoelectronics. Amongst these polymers, 3- and 3,4- substituted poly(pyrroles) and poly(thiophenes) have received significant attention in recent years as demonstrated by the increase in the number of patents and publications that describe their use. This review covers the development in the synthesis of 3- and 3,4- Substituted poly(pyrroles) and poly(thiophenes) over the last 30 years, their polymerisation in addition to describing the material properties and applications of the resulting polymers. In particular, this review focuses upon the variety of methodologies employed for the synthesis of 3- and 3,4-substituted pyrroles and thiophenes as well as upon the broad range of functional groups that can be attached to the heterocyclic ring system in order to tailor the properties of the resulting polymers.

Classification of lactose and mandelic acid THz spectra using subspace and wavelet-packet algorithms

This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their respective THz transients. The performance of three different pre-processing algorithms applied to the time-domain signatures obtained using a THz-transient spectrometer are contrasted by evaluating the classifier performance. A range of amplitudes of zero-mean white Gaussian noise are used to artificially degrade the signal-to-noise ratio of the time-domain signatures to generate the data sets that are presented to the classifier for both learning and validation purposes. This gradual degradation of interferograms by increasing the noise level is equivalent to performing measurements assuming a reduced integration time. Three signal processing algorithms were adopted for the evaluation of the complex insertion loss function of the samples under study; a) standard evaluation by ratioing the sample with the background spectra, b) a subspace identification algorithm and c) a novel wavelet-packet identification procedure. Within class and between class dispersion metrics are adopted for the three data sets. A discrimination metric evaluates how well the three classes can be distinguished within the frequency range 0. 1 - 1.0 THz using the above algorithms.