Electrocatalytic Oxygen and Nitrate Reduction Reactions Using Cu-Based Electrodes

There has been a resurgence of interest in electrocatalysis because interesting chemistry frequently happens at the electrochemical interface between electrodes and electrolytes that are relevant to energy conversion processes. The focus of this dissertation is on the electrocatalytic reduction of oxygen (O 2 ) and nitrate (NO 3 -) to water (H 2 O) and ammonia (NH 3 ), respectively, using molecular- and surface-based Cu electrocatalysts. The oxygen reduction reaction (ORR) is important due to its application in the cathode of fuel cells. The nitrate reduction reaction (NRR) can be used to generate NH 3 as an alternative to the traditional Haber-Bosch process. The first part (chapter 1) of this thesis discusses the importance of electrocatalysis in the field of energy conversion and storage technologies. The second part of this thesis (chapters 2 and 3) focusses on the ORR. Due to the high reduction potential and slow kinetics of the ORR, fuel cells are not being fully commercialized. This part discusses efforts to understand the ORR reaction mechanism and develop new ORR electrocatalysts using Cu tripeptide complexes. Laccase is a well-known Cu-containing ORR enzyme with a low overpotential, but it is only stable in a narrow pH range. Here, we synthesized Cu-tripeptide complexes and investigated their ORR activities in a wide pH range from 2.5 to 10 and determined the effect of peptide aggregation and Cu-peptide binding constant on ORR performance. In the last parts of chapter 2 and 3, we discuss some future prospects of the ORR using non-precious Cu-based electrocatalysts. The third part (chapters 4 and 5) of this thesis discusses the NRR. Here, we fabricated Nafion-modified metals electrodes and tested the activity of theses electrodes for the NRR to produce NH 3 electrochemically. We interrogate the mechanism of NH 3 production from NO 3 - reduction using electrochemical experiments, surface-enhanced Raman spectroscopy, and density functional theory calculations. We also explore this NO 3 - reduction reaction for Nafion-modified electrodeposited Cu electrodes to increase the current density and NH 3 Faradaic efficiency. Lastly, we discuss the future prospect of this electrocatalytic NRR using different fluoropolymers instead of Nafion and propose methods to increase the durability of the catalysts

Humayun Ahmed’s Gouripur Junction: A Saga of Unforgiving Realities and Perpetual Uncertainties of the Marginalized People

Humayun Ahmed in his novel Gouripur Junction attempts to portray the picture of a train station along with the lives of the people depending on it. His sincere efforts are directed mainly towards the marginal people who are fated to be there, somehow eke out a living there and are bound to face the uncertainties and complexities in the struggle for living an unstable life. While the writer has made a comprehensive delineation of their lives along with relationships of different types, love and hatred, strengths and shortcomings, the basic aspect of their life is uncertainty, sufferings and struggle. Humayun Ahmed though commonly considered as a writer having good understanding of and compassion for the middle class, he has another strong but less attended area and that is his unflinching endeavor to portray the marginal people in a comprehensive way. In this novel, his manifest intention is to portray life in totality by a balance attention on the realities as well as the psychosocial aspects of those people and make the readers empathetic towards them. This paper aims at studying this strong but less attended dimension of Humayun Ahmed's fictional works with special attention on his short novel Gouripur Junction. To make the study, the researcher will avail the works of the writer as primary source and major prevailing works on him as the secondary source and hopefully open up a new dimension. Keywords: station, struggle, uncertainty, marginal, dimension

Interaction of an Antituberculosis Drug with a Nanoscopic Macromolecular Assembly: Temperature-Dependent Förster Resonance Energy Transfer Studies on Rifampicin in an Anionic Sodium Dodecyl Sulfate Micelle

In this contribution, we report studies on the nature of binding of a potent antituberculosis drug, Rifampicin (RF) with a model drug delivery system, sodium dodecyl sulfate (SDS) micelle. Temperature dependent dynamic light scattering (DLS), conductometry, and circular dichroism (CD) spectroscopy have been employed to study the binding interaction of the drug with the micelle. The absorption spectrum of the drug RF in the visible region has been employed to study Förster resonance energy transfer (FRET) from another fluorescent drug Hoechst 33258 (H33258), bound to the micelle. Picosecond-resolved FRET studies at room temperature confirm the simultaneous binding of the two drugs to the micelle and the distance between the donor−acceptor pair is found to be 34 Å. The temperature dependent FRET study also confirms that the location and efficiency of drug binding to the micelle changes significantly at the elevated temperature. The energy transfer efficiency of the donor H33258, as measured from time-resolved studies, decreases significantly from 76% at 20 °C to 60% at 55 °C. This reveals detachment of some amount of the drug molecules from the micelles and increased donor−acceptor distance at elevated temperatures. The estimated donor−acceptor distance increases from a value of 33 Å at 20 °C to 37 Å at 55 °C. The picosecond resolved FRET studies on a synthesized DNA bound H33258 in RF solution have been performed to explore the interaction between the two. Our studies are expected to find relevance in the exploration of a potential vehicle for the vital drug rifampicin

Structure and bonding in reduced boron and aluminium complexes with formazanate ligands

Group 13 complexes of the type [(PhNNC(p-tol)NNPh)ZPh2]2- (Z = B, Al) containing a highly reduced, trianionic formazanate-derived ligand were studied and the differences in the structure, bonding and reactivity between the B and Al compounds were investigated. The increased ionic character in the bonding of the Al complex is evident from the enhanced charge delocalization onto the peripheral ligand substituents (N-Ph) via the π-framework, as shown by the rotation barrier around the N-C(Ph) bond. The electron-rich nature of these compounds allows facile benzylation at the ligand, and the structures of the products were analysed by X-ray crystallography. The products are inorganic analogues of 1-alkylated 1,2,3,4-tetrahydro-1,2,4,5-tetrazines ('leucoverdazyls'). The six-membered heterocyclic cores of the B and Al compounds are shown to be different, having envelope- and boat-type conformations, respectively. Homolysis of the N-C(benzyl) bond in these compounds was studied by NMR spectroscopy under conditions that trap the organic radical as TEMPO-Bn. Analysis of the reaction kinetics affords activation parameters that approximate the N-C(benzyl) bond strength. The ionic Al compound has one of the weakest N-C bonds reported so far in this type of inorganic leucoverdazyl analogues

Commercial Scale Solar Power Generation (5MW to 50 MW) and its Connection to Distribution Power Network in the United Kingdom

Over the years, the contribution of solar photovoltaic systems to the power generation is expected to grow through household small scale, and commercial scale solar installation. Researcher pronouncing that delivering such a determination require greater motivation and innovation and much more dynamic power grid network to manage solar generation connection. This research work identifies and recommends the possibilities of applying proven technical know how to get the maximum from the existing power network economically. The simulated case study examples of various capacity connection requests was carried out to provide key insights on the problems faced by the PV farm connections in their line of business. This research is also an effort to give many answers to solar PV developers and enthusiasts who are not very technical and confused about different money saving connection options and the electrical constraints of the power grid. This study data can be used to provide recommendations to further enhance the growth of commercial scale solar power generation in the UK

Nox4 redox regulation of PTP1B contributes to the proliferation and migration of glioblastoma cells by modulating tyrosine phosphorylation of coronin-1C（Nox4-PTP1Bレドックスシグナルは、coronin-1Cのタイロシンリン酸化を介して膠芽腫細胞の増殖と運動に寄与する）

信州大学(Shinshu university)博士（医学）雑誌に発表。FREE RADICAL BIOLOGY AND MEDICINE. 67(0):285-291 (2014); doi:10.1016/j.freeradbiomed.2013.11.005.ThesisMD. ABDUS SALAM MONDOL. Nox4 redox regulation of PTP1B contributes to the proliferation and migration of glioblastoma cells by modulating tyrosine phosphorylation of coronin-1C（Nox4-PTP1Bレドックスシグナルは、coronin-1Cのタイロシンリン酸化を介して膠芽腫細胞の増殖と運動に寄与する）. 信州大学, 2014, 博士論文.doctoral thesi

Cation effects on dynamics of ligand-benzylated formazanate boron and aluminium complexes

The dynamic processes present in ligand-benzylated formazanate boron and aluminium complexes are investigated using variable temperature NMR experiments and lineshape analyses. The observed difference in activation parameters for complexes containing either organic countercations (NBu4+) or alkali cations is rationalized on the basis of a different degree of ion-pairing in the ground state, and the data are in all cases consistent with a mechanism that involves pyramidal inversion at the nitrogens in the heterocyclic ring rather than homolytic N-C(benzyl) bond cleavage. This journal i

Monitoring geological storage of CO2: a new approach

publishedVersio

Neural networks approach towards determining Flax-Biocomposites composition and processing parameters

This research introduces neural networks (NN) as a novel approach towards aiding biocomposite materials processing. At its core, the aim of the research was to investigate NN usage as a tool for advancing the field of biocomposites. Empirical data was generated for compression-molded flax fiber and High Density Polyethylene (HDPE) matrix based biocomposite materials. In an attempt to create the NN model, tensile strength, impact strength, hardness, bending strength, and density were provided to the NN as inputs. These inputs were processed through multiple layers of the NN, and contributed to the prediction of the composition (fiber loading percentage) and operating parameter (pressure in MPa) as output. In précis, NN’s use was investigated to predict composition and operational parameter for biocomposites production when the desired mechanical properties of the biocomposites were available. Flax (Linum usitatissimum) fiber biocomposite boards were manufactured using chemically pretreated flax fiber and high density polyethylene (HDPE). After extensive preprocessing (combing and size reduction to 2 mm particles) and pretreatment regimen - flax fiber was mixed with HDPE and extruded using a laboratory scale single screw extruder. Extrudates generated from the extruder were again ground to 2 mm particles. Ground extrudates from different sample sets were exposed to a compression molding unit. The mold was put under two sets of pressures, (variable operating parameters) for all individual fiber loading. These boards were used to determine the mechanical properties tensile force, impact force, hardness, bending, and density. For verification and analysis of the mechanical properties, Microsoft Office Excel and a statistical software package SAS were used. After verification five different multilayer neural networks, i.e., cascade forward neural network, feedforward backpropagation neural network, neural unit (single layer, single neuron), feedforward time delay neural network and NARX, were trained and evaluated for performance. Ultimately, the feedforward backpropagation NN (FFBPNN) was selected as the most efficient. After rigorous testing, the FFBPNN trained by the TRAINSCG algorithm (Matlab ®) was selected to generate prediction results that were the most suitable, fast and accurate. Once the selection and training of the NN architecture was complete, biocomposite materials prediction was performed. From 9 separate input sets, NNs provided overall prediction error between 2 - and 4%. This was the same amount of error that was observed in the training of the neural network. It was concluded that the neural network approach for the experimental design and operational conditions were satisfied