PVC Supported Liquid Membrane and Carbon Paste Potentiometric Sensors Incorporating a Mn(III)-Porphyrin for the Direct Determination of Undissociated Paracetamol

PVC supported liquid membrane and carbon paste potentiometric sensors incorporating an Mn(III)-porphyrin complex as a neutral host molecule were developed for the determination of paracetamol. The measurements were carried out in solution at pH 5.5. Under such conditions paracetamol exists as a neutral molecule. The mechanism of molecular recognition between the Mn(III)-porphyrin and paracetamol, leading to potentiometric signal generation, is discussed.The sensitivity and selectivity toward paracetamol of carbon paste and polymeric liquid membrane electrodes incorporating an Mn(III)-porphyrin host were compared. The applicability of these sensors to the direct determination of paracetamol was checked by performing a recovery test in human plasma.

Investigations on The Finite Size Effects of Some Inverse Spinels and Studies on Their Composites Based on Nitrile Rubber

Magnetism and magnetic materials have been an ever-attractive subject area for engineers and scientists alike because of its versatility in finding applications in useful devices. They find applications in a host of devices ranging from rudimentary devices like loud speakers to sophisticated gadgets like waveguides and Magnetic Random Access Memories (MRAM).The one and only material in the realm of magnetism that has been at the centre stage of applications is ferrites and in that spinel ferrites received the lions share as far as practical applications are concerned.It has been the endeavour of scientists and engineers to remove obsolescence and improve upon the existing so as to save energy and integrate in to various other systems. This has been the hallmark of material scientists and this has led to new materials and new technologies.In the field of ferrites too there has been considerable interest to devise new materials based on iron oxides and other compounds. This means synthesising ultra fine particles and tuning its properties to device new materials. There are various preparation techniques ranging from top- down to bottom-up approaches. This includes synthesising at molecular level, self assembling,gas based condensation. Iow temperature eo-precipitation, solgel process and high energy ball milling. Among these methods sol-gel process allows good control of the properties of ceramic materials. The advantage of this method includes processing at low temperature. mixing at the molecular level and fabrication of novel materials for various devices.Composites are materials. which combine the good qualities of one or more components. They can be prepared in situ or by mechanical means by the incorporation of fine particles in appropriate matrixes. The size of the magnetic powders as well as the nature of matrix affect the processability and other physical properties of the final product. These plastic/rubber magnets can in turn be useful for various applications in different devices. In applications involving ferrites at high frequencies, it is essential that the material possesses an appropriate dielectric permittivity and suitable magnetic permeability. This can be achieved by synthesizing rubber ferrite composites (RFC's). RFCs are very useful materials for microwave absorptions. Hence the synthesis of ferrites in the nanoregirne.investigations on their size effects on the structural, magnetic, and electrical properties and the incorporation of these ferrites into polymer matrixes assume significance.In the present study, nano particles of NiFe204, Li(!5Fe2S04 and Col-e-O, are prepared by sol gel method. By appropriate heat treatments, particles of different grain sizes are obtained. The structural, magnetic and electrical measurements are evaluated as a function of grain size and temperature. NiFel04 prepared in the ultrafine regime are then incorporated in nitrile rubber matrix. The incorporation was carried out according to a specific recipe and for various loadings of magnetic fillers. The cure characteristics, magnetic properties, electrical properties and mechanical properties of these elastomer blends are carried out. The electrical permittivity of all the rubber samples in the X - band are also conducted.

Design of Cyclophanes: Synthesis and Study of Photophysical and Biomolecular Recognition Properties

Development of organic molecules that exhibit selective interactions with different biomolecules has immense significance in biochemical and medicinal applications. In this context, our main objective has been to design a few novel functionaIized molecules that can selectively bind and recognize nucleotides and DNA in the aqueous medium through non-covalent interactions. Our strategy was to design novel cycIophane receptor systems based on the anthracene chromophore linked through different bridging moieties and spacer groups. It was proposed that such systems would have a rigid structure with well defined cavity, wherein the aromatic chromophore can undergo pi-stacking interactions with the guest molecules. The viologen and imidazolium moieties have been chosen as bridging units, since such groups, can in principle, could enhance the solubility of these derivatives in the aqueous medium as well as stabilize the inclusion complexes through electrostatic interactions.We synthesized a series of water soluble novel functionalized cyclophanes and have investigated their interactions with nucleotides, DNA and oligonucIeotides through photophysical. chiroptical, electrochemical and NMR techniques. Results indicate that these systems have favorable photophysical properties and exhibit selective interactions with ATP, GTP and DNA involving electrostatic. hydrophobic and pi-stacking interactions inside the cavity and hence can have potential use as probes in biology.

Nonlinear Optical Properties of Organic and Polymer Systems

Present thesis has discussed the design and synthesis of polymers suitable for nonlinear optics. Most of the molecules that were studied have shown good nonlinear optical activity. The second order nonlinear optical activity of the polymers was measured experimentally by Kurtz and Perry powder technique. The thesis comprises of eight chapters.The theory of NLO phenomenon and a review about the various nonlinear optical polymers has been discussed in chapter 1. The review has provided a survey of NLO active polymeric materials with a general introduction, which included the principles and the origin of nonlinear optics, and has given emphasis to polymeric materials for nonlinear optics, including guest-host systems, side chain polymers, main chain polymers, crosslinked polymers, chiral polymers etc.Chapter 2 has discussed the stability of the metal incorporated tetrapyrrole molecules, porphyrin, chlorin and bacteriochlorin.Chapter 3 has provided the NLO properties of certain organic molecules by computational tools. The chapter is divided into four parts. The first part has described the nonlinear optical properties of chromophore (D-n-A) and bichromophore (D-n-A-A-n-D) systems, which were separated by methylene spacer, by making use of DPT and semiempirical calculations.Chapter 4: A series of polyurethanes was prepared from cardanol, a renewable resource and a waste of the cashew industry by previously designed bifunctional and multifunctional polymers using quantum theoretical approach.Chapter 5: A series of chiral polyurethanes with main chain bis azo diol groups in the polymer backbone was designed and NLO activity was predicted by ZlNDO/ CV methods.In Chapter 7, polyurethanes were first designed by computational methods and the NLO properties were predicted by correction vector method. The designed bifunctional and multifunctional polyurethanes were synthesized by varying the chiral-achiral diol compositions

Crystal Structures and Spectral Investigation of Aroylhydrazones and their Mixed Ligand Metal Chelates

Supra molecular architectures of coordination complexes of liydrazones through non covalent interactions have been explored. Molecular self—assernbly driven by weak interactions such as hydrogen— bonding, K '”T[, C-1-I‘ "TE, van der Waals interactions, and so forth are currently of tremendous research interest in the fields of molecule based materials. The directional properties of the hydrogembonding interaction associate discrete molecules into aggregate structures that are sufficiently stable to be considered as independent chemical species. Chemistry can borrow nature’s strategy to utilize hydrogen-bonding as Well as other noncovalent interactions as found in secondary and tertiary structures of proteins such as the double helix folding of DNA, hydrophobic selflorganization of phospholipids in cell membrane etc. In supramolecular chemistry hydrogen bonding plays an important role in forming a variety of architectures. Thus, the wise modulation and tuning of the complementary sites responsible for hydrogen—bond formation have led to its application in supramolecular electronics, host-guest chemistry, self-assembly of molecular capsules, nanotubes etc. The work presented in this thesis describes the synthesis and characterization of metal complexes derived from some substituted aroylhydrazones. The thesis is divided into seven chapters.

Design Of Metal Architectures: Synthesis And Study Of Their Photophysical And Biomolecular Recognition Properties

Design and study of molecular receptors capable of mimicking natural processes has found applications in basic research as well as in the development of potentially useful technologies. Of the various receptors reported, the cyclophanes are known to encapsulate guest molecules in their cavity utilizing various non–covalent interactions resulting in significant changes in their optical properties. This unique property of the cyclophanes has been widely exploited for the development of selective and sensitive probes for a variety of guest molecules including complex biomolecules. Further, the incorporation of metal centres into these systems added new possibilities for designing receptors such as the metallocyclophanes and transition metal complexes, which can target a large variety of Lewis basic functional groups that act as selective synthetic receptors. The ligands that form complexes with the metal ions, and are capable of further binding to Lewis-basic substrates through open coordination sites present in various biomolecules are particularly important as biomolecular receptors. In this context, we synthesized a few anthracene and acridine based metal complexes and novel metallocyclophanes and have investigated their photophysical and biomolecular recognition properties.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Syntactic Based Machine Translation from English to Malayalam

Due to the emergence of multiple language support on the Internet, machine translation (MT) technologies are indispensable to the communication between speakers using different languages. Recent research works have started to explore tree-based machine translation systems with syntactical and morphological information. This work aims the development of Syntactic Based Machine Translation from English to Malayalam by adding different case information during translation. The system identifies general rules for various sentence patterns in English. These rules are generated using the Parts Of Speech (POS) tag information of the texts. Word Reordering based on the Syntax Tree is used to improve the translation quality of the system. The system used Bilingual English –Malayalam dictionary for translation.

Automated Transcription System for Malayalam Language

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.

Development & evaluation of different acoustic models for Malayalam continuous speech recognition

Performance of any continuous speech recognition system is dependent on the accuracy of its acoustic model. Hence, preparation of a robust and accurate acoustic model lead to satisfactory recognition performance for a speech recognizer. In acoustic modeling of phonetic unit, context information is of prime importance as the phonemes are found to vary according to the place of occurrence in a word. In this paper we compare and evaluate the effect of context dependent tied (CD tied) models, context dependent (CD) and context independent (CI) models in the perspective of continuous speech recognition of Malayalam language. The database for the speech recognition system has utterance from 21 speakers including 11 female and 10 males. Our evaluation results show that CD tied models outperforms CI models over 21%.

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data

Connected digit speech recognition system for Malayalam language

A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.