156 research outputs found
Synthesis of N-(6-Arylbenzo[d]thiazole-2-acetamide derivatives and their biological activities: An experimental and computational approach
No full textA new series of N-(6-arylbenzo[d]thiazol-2-yl)acetamides were synthesized by C-C coupling methodology in the presence of Pd(0) using various aryl boronic pinacol ester/acids. The newly synthesized compounds were evaluated for various biological activities like antioxidant, haemolytic, antibacterial and urease inhibition. In bioassays these compounds were found to have moderate to good activities. Among the tested biological activities screened these compounds displayed the most significant activity for urease inhibition. In urease inhibition, all compounds were found more active than the standard used. The compound N-(6-(p-tolyl)benzo[d]thiazol-2-yl)acetamide was found to be the most active. To understand this urease inhibition, molecular docking studies were performed. The in silico studies showed that these acetamide derivatives bind to the non-metallic active site of the urease enzyme. Structure-activity studies revealed that H-bonding of compounds with the enzyme is important for its inhibition
Ataf Ali - Excell Data
No full textIt contains all data files with description of figures in the file name
Advanced polymeric/inorganic nanohybrids: An integrated platform for gas sensing applications
No full textRapid industrial development, vehicles, domestic activities and mishandling of garbage are the main sources of pollutants, which are destroying the atmosphere. There is a need to continuously monitor these pollutants for the safety of the environment and human beings. Conventional instruments for monitoring of toxic gases are expensive, bigger in size and time-consuming. Hybrid materials containing organic and inorganic components are considered potential candidates for diverse applications, including gas sensing. Gas sensors convert the information regarding the analyte into signals. Various polymeric/inorganic nanohybrids have been used for the sensing of toxic gases. Composites of different polymeric materials like polyaniline (PANI), poly (4-styrene sulfonate) (PSS), poly (3,4-ethylene dioxythiophene) (PEDOT), etc. with various metal/metal oxide nanoparticles have been reported as sensing materials for gas sensors because of their unique redox features, conductivity and facile operation at room temperature. Polymeric nanohybrids showed better performance because of the larger surface area of nanohybrids and the synergistic effect between polymeric and inorganic materials. This review article focuses on the recent developments of emerging polymeric/inorganic nanohybrids for sensing various toxic gases including ammonia, hydrogen, nitrogen dioxide, carbon oxides and liquefied petroleum gas. Advantages, disadvantages, operating conditions and prospects of hybrid composites have also been discussed.Rivers, Ports, Waterways and Dredging EngineeringHydraulic EngineeringAerospace Manufacturing Technologie
A Study of Reduced Order 4D-VAR with a Finite Element Shallow Water Model
No full textForecast models often depend on unknown parameters, such as model initial and boundary conditions, or other tunable parameters not necessarily having any physical meaning. Calibration of these parameters to minimize errors between forecasted and observed states is called data assimilation. A common approach in this context are variational methods, of which four dimensional data variation (4D-VAR) is studied in this thesis. In 4D-VAR, a cost function is defined that penalizes misfits between observations and the corresponding numerical model results, obtained by running the model with the chosen configuration. Performing optimization with regard to this cost function yields an improved initial parameter set. Associated with this type of methods, however, are difficulties in connection with programming the adjoint model, which is needed to compute the exact gradient of the cost function. Additionally, having to integrate the adjoint model backwards in time adds significantly to the computational cost of the data assimilation process. To avoid manual implementation of adjoint code and to reduce computational complexity, approximation of the gradient calculation is considered through the use of proper orthogonal decomposition (POD), a flexible data-driven order reduction method. To facilitate this, a finite element model of the shallow water equations is tested with both the full adjoint 4D-VAR method and two different POD-reduced approaches. Twin experiments are performed and comparisons are made in terms of accuracy, computational complexity and sensitivity to perturbation and number of observation points.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Ataf Ali - Excell Data from Synthesis and characterization of azo-guanidine based alcoholic media naked eye DNA sensor
No full textDNA sensing always has an open meadow of curiosity for biotechnologists and other researchers. Recently, in this field, we have introduced an emerging class of molecules containing azo and guanidine functionalities. In this study, we have synthesized three new compounds (UA1, UA6 and UA7) for potential application in DNA sensing in alcoholic medium. The synthesized materials were characterized by elemental analysis, FTIR, UV-visible, 1H NMR and 13C NMR spectroscopies. Their DNA sensing potential were investigated by UV-visible spectroscopy. The insight of interaction with DNA was further investigated by electrochemical (cyclic voltammetry) and hydrodynamic (viscosity) studies. The results showed that compounds have moderate DNA binding properties, with the binding constants range being 7.2 x 103, 2.4 x 103 and 0.2 x 103M-1, for UA1, UA6 and UA7, respectively. Upon binding with DNA, there was a change in colour (a blue shift in the λmax value) which was observable with a naked eye. These results indicated the potential of synthesized compounds as DNA sensors with detection limit 1.8, 5.8 and 4.0ng µl-1 for UA1, UA6 and UA7, respectively
Model reduced variational data assimilation for shallow water flow models
No full textIdentifying uncertain parameters in large-scale numerical flow models can be done using the variational method. However, for implementing the variational method the adjoint model have to be available, which requires highly complex computer code and maintenance and thus hampers its applications. To ease this problem, this thesis has explored several methods for efficiently identifying uncertain parameters in a large-scale tidal model of the entire European continental shelf which does not require the implementation of these complex adjoint code. In this study, as a first step an estimation method based on model reduction is developed and investigated for the estimation of diffusion coefficient in a simple 2D-advection diffusion model. Two projection based model reduction methods were considered, namely proper orthogonal decomposition (POD) and Balanced proper orthogonal decomposition (BPOD). In the POD based estimation method an ensemble of forward model simulations is used to determine an approximation of the covariance matrix of the model variability and a small number of the leading eigenvectors of this matrix is used to define a model subspace. By projecting the original model onto this subspace an approximate linear reduced model is obtained. Once the reduced model is available its adjoint can be implemented easily and the minimization problem is solved completely in reduced space with very low computational cost. BPOD is also a model reduction method which considers both inputs and outputs of the system while determining the reduce subspace. The estimation method has been extended by including BPOD procedure into the estimation procedure. Numerical results from a simple pollution model demonstrate that the POD based estimation approach successfully estimate the diffusion coefficient for both advection dominated problems as for diffusion dominated problems. Another important message in this study, although lots of effort had been made in constructing a reduced order model by the BPOD method, the minimization results demonstrated that both the POD and the BPOD methods performed similarly. Preliminary results showed the validity of the POD based model reduction methods for parameter estimation. As a next step, the POD based estimation method is used to calibrate numerical tidal models. Results from (twin) numerical experiments showed that the POD based calibration method performed very efficiently to estimate depth values in the selected regions of the model domain. The computational costs of the POD based calibration method are dominated by the generation of an ensemble of forward model simulations where the simulation period of the ensemble is equivalent to the timescale of the original model. It has also been found in the study that it is not needed to use a full simulations of the original model for the generation of the ensemble. The POD based calibration method has also been implemented for the estimation of the water depth and space varying bottom friction coefficient values in a very large-scale DCSM model. The recently designed large-scale spherical grid based water level model for the northwest European continental shelf (around 1000000 computational grid points) has been used for this purpose. This has been the first application of the POD based calibration method to a very large-scale model and with real data. Results from numerical experiments showed that the calibration method performs very efficiently. An overall improvement of more than 50\% was observed after the calibration in comparison with the initial model. The results also demonstrated that the POD based calibration method offered a very efficient minimization technique compared to the classical adjoint method without the burden of implementation of the adjoint. As a concluding step, to estimate depth values in the model DCSM, a Simultaneous perturbation stochastic approximation (SPSA) method has been used. The method uses stochastic simultaneous perturbation of all model parameters to generate a search at each iteration. SPSA is based on a highly efficient and easily implemented simultaneous perturbation approximation to the gradient. This gradient approximation for the central difference method uses only two objective function evaluations independent of the number of parameters being optimized. The results from experiments showed that SPSA has a lower convergence rate than POD based calibration method, however the computational cost in each iteration of the SPSA method is usually far less then the POD based calibration method. The results also demonstrated that the SPSA algorithm proved to be a promising optimization algorithm for model calibration for cases where adjoint code is not available for computing the gradient of the objective function.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc
Synthesis and Characterization of Azo-Guanidine Based Alcoholic Media Naked Eye DNA Sensor
No full textDNA sensing always has an open meadow of curiosity for biotechnologists and other researchers. Recently, in this field, we have introduced an emerging class of molecules containing azo and guanidine functionalities. In this study, we have synthesized three new compounds (UA1, UA6 and UA7) for potential application in DNA sensing in alcoholic medium. The synthesized materials were characterized by elemental analysis, FTIR, UV-visible, 1H NMR and 13C NMR spectroscopies. Their DNA sensing potential were investigated by UV-visible spectroscopy. The insight of interaction with DNA was further investigated by electrochemical (cyclic voltammetry) and hydrodynamic (viscosity) studies. The results showed that compounds have moderate DNA binding properties, with the binding constants range being 7.2 x 103, 2.4 x 103 and 0.2 x 103 M-1, for UA1, UA6 and UA7, respectively. Upon binding with DNA, there was a change in colour (a blue shift in the lambda(max) value) which was observable with a naked eye. These results indicated the potential of synthesized compounds as DNA sensors with detection limit 1.8, 5.8 and 4.0 ng μl-1 for UA1, UA6 and UA7, respectively
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