Protein engineering of Heparinase I : elucidation of structure-activity relationships

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1996.Includes bibliographical references (leaves 218-229).by Ranganathan S. Godavarti.Sc.D

Structural, morphological, magnetic and electrical properties of Ni doped ZnO nanoparticles synthesized by co-precipitation method

Weak ferromagnetic behaviour was obtained in a systematic way at room temperature by doping of ZnO with nickel (Zn1-xNixO, where x = 0.00, 0.05, 0.15 and 0.20). The obtained results were correlated with conductivity and impedance studies. Diamagnetic to ferromagnetic change was observed with increased concentration of nickel. X-ray diffraction analysis confirmed wurtzite ZnO structure of prepared nanopowders while microstrain was increased with nickel concentration. Incorporation of nickel in ZnO structure was confirmed using EDAX analysis, while FTIR spectroscopy provided further information on functional groups. Transmission electron microscopy images showed that the particle sizes are in the range of 12–20 nm, and scanning electron microscopy analyses that grain size decreases with increase in nickel concentration. Photo luminescence studies confirmed the presence of VO and Zni defects in the prepared samples. It was concluded that the defect induced strain, grain boundaries and lower particle sizes are the reasons for weak ferromagnetic behaviour of the investigated samples

Quantitative high throughput analytics to support polysaccharide production process development.

The rapid development of purification processes for polysaccharide vaccines is constrained by a lack of analytical tools current technologies for the measurement of polysaccharide recovery and process-related impurity clearance are complex, time-consuming, and generally not amenable to high throughput process development (HTPD). HTPD is envisioned to be central to the improvement of existing polysaccharide manufacturing processes through the identification of critical process parameters that potentially impact the quality attributes of the vaccine and to the development of de novo processes for clinical candidates, across the spectrum of downstream processing. The availability of a fast and automated analytics platform will expand the scope, robustness, and evolution of Design of Experiment (DOE) studies. This paper details recent advances in improving the speed, throughput, and success of in-process analytics at the micro-scale. Two methods, based on modifications of existing procedures, are described for the rapid measurement of polysaccharide titre in microplates without the need for heating steps. A simplification of a commercial endotoxin assay is also described that features a single measurement at room temperature. These assays, along with existing assays for protein and nucleic acids are qualified for deployment in the high throughput screening of polysaccharide feedstreams. Assay accuracy, precision, robustness, interference, and ease of use are assessed and described. In combination, these assays are capable of measuring the product concentration and impurity profile of a microplate of 96 samples in less than one day. This body of work relies on the evaluation of a combination of commercially available and clinically relevant polysaccharides to ensure maximum versatility and reactivity of the final assay suite. Together, these advancements reduce overall process time by up to 30-fold and significantly reduce sample volume over current practices. The assays help build an analytical foundation to support the advent of HTPD technology for polysaccharide vaccines. It is envisaged that this will lead to an expanded use of Quality by Design (QbD) studies in vaccine process development

Active noise control over adaptive distributed networks

© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper presents the implementation of Active Noise Control (ANC) systems over a network of distributed acoustic nodes. For this purpose we define a general acoustic node consisting of one or several microphones and one or several loudspeakers together with a unique processor with communication capabilities. ANC systems can use a wide range of adaptive algorithms, but we have considered specifically the Multiple Error Filtered-x Least Mean Square (MEFxLMS), which has been proved to perform very well for ANC systems with multiple microphones and loudspeakers, and centralized processing. We present a new formulation to introduce the distributed version of the MEFxLMS together with an incremental collaborative strategy in the network. We demonstrate that the distributed MEFxLMS exhibits the same performance as the centralized one when there are no communication constraints in the network. Then, we re-formulate the distributed MEFxLMS to include parameters related to its implementation on an acoustic sensor network: latency of the network, computational capacity of the nodes, and trustworthiness of the signals measured at each node. Simulation results in realistic scenarios show the ability of the proposed distributed algorithms to achieve good performance when proper values of these parameters are chosen. (C) 2014 Elsevier B.V. All rights reserved.This work has been supported by European Union ERDF and Spanish Government through TEC2012-38142-C04 Project, and Generalitat Valenciana through PROMETEOII/2014/003 Project.Ferrer Contreras, M.; Diego Antón, MD.; Piñero Sipán, MG.; González Salvador, A. (2015). Active noise control over adaptive distributed networks. Signal Processing. 107:82-95. https://doi.org/10.1016/j.sigpro.2014.07.026S829510

Thermal and electrical conductivity of melt mixed polycarbonate hybrid composites co-filled with multi-walled carbon nanotubes and graphene nanoplatelets

"This is the peer reviewed version of the following article: Wegrzyn, M., Ortega, A., Benedito, A., & Gimenez, E. (2015). Thermal and electrical conductivity of melt mixed polycarbonate hybrid composites co‐filled with multi‐walled carbon nanotubes and graphene nanoplatelets. Journal of Applied Polymer Science, 132(37), which has been published in final form at https://doi.org/10.1002/app.42536. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] In this work, we present thermoplastic nanocomposites of polycarbonate (PC) matrix with hybrid nanofillers system formed by a melt-mixing approach. Various concentrations of multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelets (GnP) were mixed in to PC and the melt was homogenized. The nanocomposites were compression molded and characterized by different techniques. Torque dependence on the nanofiller composition increased with the presence of carbon nanotubes. The synergy of carbon nanotubes and GnP showed exponential increase of thermal conductivity, which was compared to logarithmic increase for nanocomposite with no MWCNT. Decrease of Shore A hardness at elevated loads present for all investigated nanocomposites was correlated with the expected low homogeneity caused by a low shear during melt-mixing. Mathematical model was used to calculate elastic modulus from Shore A tests results. Vicat softening temperature (VST) showed opposite pattern for hybrid nanocomposites and for PC-MWCNT increasing in the latter case. Electrical conductivity boost was explained by the collective effect of high nanofiller loads and synergy of MWCNT and GnP. (c) 2015 Wiley Periodicals, Inc. J. Appl. Polym. 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Multiple Antennas in Wireless Communications: Array Signal Processing and Channel Capacity

We investigate two aspects of multiple-antenna wireless communication systems in this thesis: 1) deployment of an adaptive beamformer array at the receiver; and 2) space-time coding for arrays at the transmitter and the receiver. In the first part of the thesis, we establish sufficient conditions for the convergence of a popular least mean squares (LMS) algorithm known as the sequential Partial Update LMS Algorithm for adaptive beamforming. Partial update LMS (PU-LMS) algorithms are reduced complexity versions of the full update LMS that update a subset of filter coefficients at each iteration. We introduce a new improved algorithm, called Stochastic PU-LMS, which selects the subsets at random at each iteration. We show that the new algorithm converges for a wider class of signals than the existing PU-LMS algorithms