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Design of Carbon Quantum Dot Based Fluorescence Sensor for Detection of Toxic Organic Molecules
No full textAlthough extensive work has been done on rapid detection of metal ions, anions and biomolecules using luminescent carbon quantum dots (CD), the available documentation on development of luminescent sensors for the detection of organic pollutants such as pesticides or herbicides is extremely sparse. The main focus of this doctoral research work is to develop new fabrication methods for N-doped carbon dots for sensing some important herbicides and pesticides. We have optimized the fluorescence properties of N-doped carbon dots with respect to precursor concentration. Utilizing the improved emission property of carbon dots we have designed smart nanosensors to detect pesticides in aqueous medium as well as in real agricultural products. We have mainly emphasized on the following issues, 1) low energy and cost effective approach for synthesis of carbon dots, 2) choice of a molecular precursor ensuring the availability of abundant surface functional groups so as to ensure a superior molecular recognition by the target analyte, 3) Incorporating CD in a solid matrix to develop a more stable carbon dot based sensor, 4) to develop suitable fluorescence sensing method for less explored organochlorine pesticides (OC), 5) to develop CD based sensor for detection of pesticides in a microbial cell. In line with our objective we have developed CD based fluorescence nanosensor for detection of two widely used organophosphorus pesticides i.e. glyphosate and quinalphos, one organochlorine pesticide atrazine. The details of sensing conditions, plausible mechanism as well as applications on detection of pesticides in real agricultural samples have been discussed in respective chapters
Ocean-Atmosphere Interactions in Tropical Cyclones over North Indian Ocean: an Investigation through Observation and Modeling Approaches
No full textTropical cyclones (TCs) are regarded as one of the most catastrophic meteorological and oceanic phenomena, in which the Northern Indian Ocean (NIO; including the Bay of Bengal (BoB) and the Arabian Sea (AS)) is one of the active basins worldwide. They usually result in severe property damage and significant loss of life through destructive winds, higher storm surges, torrential rains, and severe floods. The TC evolution mainly depends on multiscale airsea processes starting from largescale to synoptic to vortex scales. The NIO has unique airsea interaction due to peculiar water properties, wind systems, and mesoscale ocean and convective vortices. TCs generally form over the warm oceans where the dense network of observations is limited. These insitu observations address the temporal variability of parameters and can also be useful to validate model outputs, remote sensing data, etc. However, the scarcity of these observations limits the spatial variability assessment of airsea parameters and the representation of the ocean mesoscale vortices. The warm ocean plays a critical role in determining the genesis, intensification, maintenance, and weakening of TCs. Identifying and integrating the ocean atmospheric parameters is extremely beneficial to improve the skill of numerical models. The oceanic parameters such as tropical cyclone heat potential (TCHP), barrier layer thickness (BLT), sea surface temperature (SST), and rainfall are undergone profound spatial and temporal changes in the presence of TCs. Understanding the TC evolution response to these parameters helps in better representation of TC processes in the numerical model. However, the NIO basin lacks the baseline climatology of these parameters and hinders the numerical models’ skill. Therefore, better establishing the relationship of BL, TCHP, SST, and rainfall parameters with TC evolution by developing the composite structures helps in understanding of the TC intensification/weakening processes over the basin. The theoretical and modeling studies provided the SST effect on the TC evolution. Concurrently, the knowledge about the TC size changes and destructive potential under ocean warming conditions is limited and needs to be modelled to minimize the potential threat. The errors in initializing/defining the TCrelated circulation (vortex characteristics) from the largescale environment (global analysis) is identified as one of the reasons for model deficiency. It can overcome by providing the quality of the threedimensional information during the initial TC vortex for the improved TC predictions. The present thesis takes the opportunity to study the airsea interaction processes over the NIO basin based on observations and modeling efforts. Correspondingly, the thesis is framed starting viii from introduction to conclusion chapters along with four working chapters. The working chapters, Chapter3 and Chapter4 elaborates on the response of the TC evolution and rainfall to the ocean parametersTCHP/BL/SST. While, Chapter5 discusses the feedback response of ocean parameters to TC rainfall and wind intensity. However, it may be noted that the feedback of modified ocean parameters (SST change by the TC rainfall) is part of the results of Chapter5, but they are not exclusively studied. The SST in the model is essentially modified by the forecasted wind and rainfall, which can be seen in the temporal evolution of SST in Chapter5. The last working Chapter6 demonstrates the impact of TC vortex initialization and relocation in improving TC track, intensity, and rainfall prediction. It also reveals the importance of TC size on defining the TC initial vortex and subsequent forecast. The climatological SSTwind relation holds well over the NIO basin, except in the northern BoB, where the salinity stratification dominates and hindered it. The presence of strong ACE and its associated increase in the SST by ∼ 3−4 °C triggers an increase of (i) TCHP by ∼ 250 − 280% and (ii) enthalpy fluxes by ∼ 370% over the BoB region showing the positive feedback to the TC. The composite analysis shows that most of the TC intensification occurred when the TCHP ranging between ∼ 50 − 80 kJ cm−2 and BLT of ∼ 10 − 30 m over the BoB. The relationship of SST and TCHPA through the composite analysis revealed that VSCS and above intensity stages produce an SST cooling of >0.8 °C, TCHPA of∼ 25−30 kJ cm−2, and rainfall of >9 mm h−1 respectively. Similarly, considering the translation speed, slowmoving TCs induce a maximum SST cooling of 0.5–1.2 °C, TCHPA of 1520 k J cm−2, and rainfall of ∼ 2 − 4 mm h−1 in the TC inner core (0–100 km) region. The individual basin analysis confirms that BoB TCs produces extremely heavy rainfall (∼910 mm h−1) as compared to the AS TCs (very heavy rainfall: ∼____________7–8 mm h−1) in the inner core region. The sensitive experiments on the response of ocean warming (from 1 to 3 °C) to the TC rainfall, size, and intensity show a linear increase and whereas an exponential growth is seen in the case of destructive potential parameter to SST increase. The vortex initialization method in the ARW model improves the track, intensity, rainfall prediction of landfalling TCs Giri (2010) and Jal (2010) over the BoB. The present dissertation provides (i) a baseline for evaluating numerical models, (ii) understanding/identifying the processes that might not be modeled appropriately, (iii) information to improve vortex initialization in the coupled model environment. It is intended that these efforts helps to achieve better improvements in the TC predictions over the North Indian Ocean
Analysis and Synthesis of Magnetically Negative (MNG) Material using Softcomputing Techniques
Get PDFUnique properties of Metamaterial are widely used in Electromagnetic Engineering, and the metamaterial has gained significant attention to be a major research area. Some of its recent research areas are carpet cloaking and metasurface design. The unique properties of these materials include simultaneous negative electromagnetic property, i.e., both permeability and permittivity are negative, because of which a negative refractive index is generated.Thus there are three primary classes of metamaterials. When only the permittivity is negative, the material is called ENG (Electrical Negative). Similarly material with only negative permeability is known as MNG (Magnetic Negative). Further when both are negative the material is regarded as DNG (Double Negative). Out of these three, the analysis and synthesis of MNG is very complicated and difficult. Therefore, the focus in this work is only on MNG, and the word "metamaterial" refers to MNG unless otherwise mentioned specifically. These type of materials don’t occur in nature and hence manufactured by making array of small unit cells of specific structure(s) made up of conductors. Although the concept of the existence of negative refractive index was proposed in the 1960s by Veselago, it took around 40 years to be verified practically when smith et al. did the experiment in 2001. They used an array of unit cell structures as Split-Ring-Resonators (SRR) and thin wires to verify the concept. Thereafter researchers are working to develop different forms of metamaterial unit cells and for which metamaterial is still an open area of research. However, while designing a metamaterial unit cell, absence of an empirical formula makes the model analysis and synthesis difficult. Although with the help of EM simulation tools this is possible, it usually is too difficult, time consuming and costly. Due to this researchers are motivated to look for alternative methods. In this work, some techniques to develop CAD models are presented based on soft computing techniques for metamaterial analysis and synthesis. Use of different soft computing techniques in the field of microwave engineering is documented in the literature. However, unconventional unit cell structures are difficult to analysis because of unavailability of predefined mathematical formulas and equivalent analysis. This can be done by the complex Modified Nicolson-Ross-Weir (NRW) method with the support of EM simulation tools which are expensive. Frequency dependency of metamaterial characteristics for any kind of unit cell structure follows a similar pattern which is obtained from Lorentz model. The basic idea in this work, which develops CAD Models for metamaterial unit cell of unconventional structures is based on the assumption that each type of unit cell can be mapped to an equivalent SRR structure, for which empirical formula is available. This is done by implementing the concept of Space Mapping technique or surrogate based modeling. Most important contribution of the work is the development of Space Mapped CAD model for analysis of an Ω atom. The developed model is validated with a Deformed-Ω atom, which is developed by integrating the concept of Space Mapping (SM) and Artificial Neural Network. Thereafter, the work progresses with proposing CAD models for synthesis of SRR. The objective is to find the design parameters of SRR for a desired material characteristic and frequency. With the availability of only a complex non-linear analysis formula, the synthesis becomes a reverse engineering problem, which is difficult to process. Three different models are proposed to solve the problem. The first approach is use of Inverse Artificial Neural Network concept, which uses a trained neural network (IANN) to perform output-to-input mapping. The developed CAD model using this approach includes integration of three concepts: IANN, Prior Knowledge Input-Difference (PKI-D) and SM. Although the model is capable of synthesizing a metamaterial unit cell, still it has some disadvantages. To overcome the disadvantages (such as lower convergence rate, lower accuracy and complex programming), use of Evolutionary Algorithms (Genetic Algorithm and Differential Evolution) is proposed. While developing CAD model based on EA, the methodology is first tested by synthesizing Rectangular Microstrip Antenna (RMPA) and then using the same concept, an SRR is synthesized. A comparison shows DE based model to be more efficient than IANN and GA based models in terms of convergence speed, accuracy and robustness
In Silico Studies of Functionalized Aromatic Heterocyclic and Zintl Ion Based Superatom/Alkali/Halogen
No full textAtomic clusters have emerged as a new phase of matter, whose properties are not only dependent on size and composition but also on the geometrical arrangements. One of the major goals of cluster science has been to use clusters as building blocks of materials with tailored properties. Two important sets of clusters that belong to this category are superalkalis and superhalogens. Apart from these molecules, another kind of cluster, known as Zintl cluster has a very special chemistry. Considering the potential applications of these compounds in vivid fields, in this thesis several superalkali and superhalogen molecules as well as Zintl clusters have been designed and their stability, reactivity, aromaticity etc. were investigated in the light of different electron counting rules and Density Functional Theory treatment. The thesis is segregated into eight chapters. Chapter 1 provides a short account of the current status of research in the area of cluster science and the methodologies employed in this thesis. Aromatic heterocyclic superhalogen and superalkali complexes have been designed using Huckel π-aromaticity rule in Chapter 2. Effect of the nature of the ligands in making such super halogen /alkali complexes have been discussed thoroughly in this chapter. Chapter 3 provides information on the possible application of such molecules in making electrolytes for Li-ion battery and super Lewis acid. Comparison with commonly used electrolytes and Lewis acid depicts the potency of the designed organic superhalogen molecules. A new class of superhalogen based on group 14 Zintl ion has been designed in Chapter 4. The bonding pattern, hybridizations, effects of ligands have been thoroughly discussed in this chapter. Chapter 5 tells the story of making superalkali complexes from group 15 Zintl ions. Different aliphatic ligands like, Me, CH2Me, CH(Me)2 and C(Me)3 have been used to make superalkali complexes. In Chapter 6 superatoms have been designed by using Zintl ions Ge9 4- and Ge9 2- as cores and -CHO as ligand. The electron affinities of resulting [Ge9(CHO)3] and [Ge9(CHO)] complexes are found to be comparable to those of chlorine and iodine, respectively. In addition, comparison of MO's of these complexes with those of Cl, I, and Al13 has been performed. The similarity between the MO's further confirms the superatom character of these organo-Zintl complexes. Attempt has been made in Chapter 7 to see the possible effect of doping on deltahedral Zintl cluster Ge9 4- . A possible extension of the concept of Zintl ions has been employed in this chapter to design an all metal version of magnetic Zintl like phases, M3Au5Ni [where M =Na, K]. In Chapter 8, a detailed study revealed a complete reversal in the reactivity trends of the Boron-center in the Zintl based cluster B[Ge9Y3]3 (Y = H, CH3, BO, CN) as compared to that in B(C6X5)3 (X = F, BO, CN). Detailed charge analyses on the various atomic sites using different basis sets and levels of theory showed that the B-center in B[Ge9Y3]3 (Y = CH3, BO, CN) is nucleophili
Development of Learning-Based Techniques for Single-Image Super-Resolution
Get PDFImage acquisition remains a challenging task due to the substandard imaging environment, inaccurate camera settings, vibration in camera-mounting machinery, varying refractive index of atmosphere, and many more. Images thus acquired in such conditions are degraded and possess little scientific value. Furthermore, applications like medical imaging, surveillance, forensic, satellite imaging, etc., require zooming of images for better analysis. And a degraded image, when zoomed, cannot provide any additional information. Therefore, a classical ill-posed inverse problem called super-resolution (SR) has been widely used to reconstruct a high-resolution (HR) image from a given low-resolution (LR) image. Super-resolution techniques have been divided into Single-image SR and Multi-image SR, depending on how many LR images are used for generating the HR image. Both well-known methods to convert an ill-posed problem to a well-posed one and obtain a stable solution have been discussed in Chapter 1. A detailed literature survey has been done in Chapter 2 for both single-image and multi-image SR techniques. It has been inferred that the SR image reconstructed by traditional multi-image SR method produces ringing artifacts near strong edges, where single-image SR schemes have an advantage. However, single-image SR schemes have a lot of challenges and open problems to be resolved. In this thesis, learning-based single-image SR approach has been used for expected HR image generation. In Chapter 3, two frameworks based on locally linear embedding (LLE) have been developed. A new feature vector has been generated in the first framework by combining residual luminance inspired by the Gaussian pyramid and the first gradient. In the second framework, the feature vector is generated by the Zernike moment. Further, the global neighborhood selection approach has been used to find the appropriate neighborhood size k for embedding.
Robust locally linear embedding (RLLE) has been used in place of LLE to improve the embedding in Chapter 4. RLLE uses RPCA to remove the outlier to reduce the artifact generated during HR image reconstruction. This scheme utilizes a neighbor embedding approach and suitably named robust neighbor embedding based super-resolution (RNESR). RNESR is trained using known LR-HR image pairs to generate information with respect to local geometry and neighborhood. Further, it uses histogram matching to select the best LR-HR image pairs for training. Subsequently, the scheme is validated using LR images selected from training pairs as well as images not used during training to generate their corresponding HR image. Here, RLLE played an essential role in generating the best patch pair using RPCA. A global neighborhood selection by local processing has been used to find the best k value for expected HR image generation.
An improved learning-based SR algorithm for texture image to generate an HR image from a single LR image has been suggested in Chapter 5. The scheme utilizes a neighbor embedding technique (manifold learning) and is suitably named improved single-image super-resolution using manifold learning (ISSRM). In this approach, HR patches are reconstructed from the input test LR patches by the prior information fetched from the training LR-HR pairs. Hence, an optimal weight reconstruction has been generated by combining the least square error and non-negative factorization matrix. The pseudo-Zernike moment has been utilized for the feature selection technique. Due to the fixed k neighbors value during HR reconstruction, the manifold learning-based approach often generates artifacts generated due to over-fitting or under-fitting. The consistency constraint is usually ignored during overlap averaging. Prior knowledge of LR-HR pair relation is exploited in Chapter 6 wherein a convolutional sparse coding (CSC) based SR is introduced. In this scheme, slice based dictionary learning is used in the patch-based method to reconstruct the HR image. Five different SR frameworks are suggested in four chapters (3–6) of the thesis. While Chapter 3 presents two variations of a framework, the remaining three chapters present one each. The first framework exploits the first-order gradient and residual luminance generated by the image pyramid. The second framework employs the first-order gradient along with three Zernike moments to preserve the global structure. The third framework is built upon the previous frameworks, wherein a set of best training image pairs are searched, and the outliers issues are handled using a robust locally linear embedding. The fourth framework employs the phase and magnitude of pseudo-Zernike moment along with a collaborative optimal reconstruction weight. The fifth framework adopts dictionary learning where a convolutional sparse coding model is used for LR-HR mapping. All the five suggested frameworks are validated on standard images. Both qualitative and quantitative performance measures like PSNR, SSIM, and FSIM are used to compare with competent schemes. In general, it is observed that the suggested schemes outperform the existing schemes
Synthesis of Activated Carbon from Lignocellulosic Biomass for Iron Removal from Aqueous Phase
No full textThe preparation of activated carbon from an economically sustainable precursor Limonia acidissima shell (an agricultural waste) by chemical activation method has been explored in the present study. This investigation emphasized the application of a statistical tool, RSM, coupled with Box-Behnken design (BBD), to optimize the experimental conditions for the preparation of activated carbon from lignocellulosic biomass activated with H3PO4 and ZnCl2. The chemical activation was conducted at a different combination of impregnation ratios (IR), activation time, and carbonization temperatures, as suggested by the Design-Expert software version 7 (Stat-Ease, Minneapolis, U.S.A). The influences of these parameters on the responses, i.e., yield% and iodine no. of activated carbon were investigated. Simultaneous optimization was performed using the desirability approach in the multi-response optimization technique. The desirability value reported for AC-H3PO4 (0.762) was higher than AC-ZnCl2 (0.653). The carbon yield and iodine adsorption value of AC-H3PO4 approached 42.63% and 951.9 mg/g under the optimal conditions of IR (24.21%), activation time (39.83 min), and carbonization temperature (432.4 oC). N2 adsorption (77K) was carried out to determine the pore characteristics of the optimized AC-H3PO4 and AC-ZnCl2 that showed phosphoric acid produced activated carbon with a higher BET surface area (1863.49 m2/g) compared to zinc chloride activation. Important physicochemical properties of both optimized activated carbons were further confirmed by zero-point charge, FTIR, XRD, and TEM Analysis, etc. The removal efficacy of the adsorbent for total Fe ion was studied in batch mode as a function of pH, contact time, initial concentration, adsorbent dosage, and temperature. The findings indicated that the adsorption procedure could be well-defined by Langmuir isotherm and pseudo-second-order kinetic model because of the high coefficient of determination (R2) value is 0.99. The maximum adsorption capacity of total Fe ion by optimized AC-H3PO4 was determined as 48.5 mg/g. The iron ions adsorbed on the surface of the carbon studied by XPS analysis gives the main band positioned at B.E. of 711.8 eV accompanied by secondary one displaced by 13.2 eV to higher B.E. (724.98 eV) with an area ratio of 1:0.5 which bore a resemblance to the characteristic values of Fe3+ in addition to associated satellite peak at around 715.99~716.0 eV approving the presence of Fe3+ as well as a small fraction of Fe2+ present on the carbon surface. The pilot-scale column was fabricated for fixed-bed adsorption x iron ions from aqueous solution in an up-flow mode. The effect of essential factors such as bed height, inlet concentration, and flow rate on the performance of the column bed was investigated. The adsorption capacity augmented with an increase in bed height and initial adsorbate concentration but declined with an increase in flow rate. The maximum uptake capacity of 209.6 mg/g was achieved at 5 cm bed height, 3.32 mL/min, and 50 mg/L initial concentration. The bed depth service time (BDST) model was used to determine the characteristic parameters of the reactor suitable for designing large-scale column studies. The Adams-Bohart, Thomas, and Yoon-Nelson models were applied to the experimental information to predict breakthrough curves using non-linear regression. The ANN-based model was able to efficaciously predict the column performance using the Levenberg-Marquardt (LM) algorithm. A comparison between the preliminary data and model results contributed to a high degree of correlation
Substitution Induced Structural, Magnetic and Electrical Properties in LaFeO3 Nanoparticle
No full textThis thesis work preferentially aspires to tune the structural, magnetic and electrical properties of undoped and various doped LaFeO3 nanoparticles synthesized by ethylene glycol assisted sol-gel technique. The enhanced properties of the as-synthesized nanoparticles have been explained in terms of cation distribution, structural modifications and exchange interactions. A detail description of the synthesis techniques used to synthesize LaFeO3 nanoparticles and its various substitutions by several magnetic and nonmagnetic metals with different valence. The experimental techniques used for the structural, morphological, dielectric and magnetic characterization of synthesized nanoparticles are systematically documented. Orthorhombic distorted perovskite LaFeO3 undergoes an antiferromagnetic (G-type) transition at ~740 K due to a strong superexchange interaction. This compound also exhibits complex electrical and magnetic behaviour with strong correlation among spin, charge and orbital degrees of freedom. Moreover, in this class of materials, many of the physical properties changes drastically under influence of internal disorder and/or external stimuli. Furthermore, a very few reports are available regarding the spin induced weak ferromagnetism and spin current induced polarization especially in the nanocrystalline form but the understanding of the magnetization, polarization and conduction mechanism in nanocrystalline LaFeO3 is still debatable. Additionally, though this wide-bandgap charge transfer type insulator and its substitutions show a very good dielectric, electric transport properties but the mechanism remains poorly understood. In this work, an effort is made to address such issues in detail and explore few new mechanisms to understand the drastic behavior shown by bare and substituted LaFeO3 nanoparticle. Detail structural, dielectric and magnetic properties of low-dimensional LaFeO3 nanoparticles are studied. For completeness, the related data of ceramic LaFeO3 is also presented. The effect of Na, a monovalent metal, on the magnetic phase transitions and electrical conductions in LaFeO3 nanoparticles is studied extensively. The influence of Zn(ll) on the structural, magnetic and dielectric dynamics of nano-LaFeO3 is documented. The explicit zero field cooled exchange bias effect is studied in Ni substituted LaFeO3. Finally, the dielectric and magnetic properties of co-doping of Na and Mn in their respective sites of LaFeO3 is studied. It is found that, LaFeO3 nanoparticle is having a better functionality than its bulk counterpart, where the former exhibits a weak ferromagnetic behavior. The Na substituted LaFeO3 nanoparticles show a coexistence of superparamagnetic and weak ferromagnetic phase and the ratio of two distinct phases vary with Na. From the dielectric measurement, a p-type polaronic conduction mechanism is found in 25%Na incorporation, which is mainly due to hole hopping between Fe4+ and Fe3+ states. Temperature-dependent magnetization of Zn doped systems show a non-ergodic state at low-T and the incompleteness of the phase transition even at very high-T. Impedance spectra reveals a non-Debye type of relaxation and grain boundary dominates over grain effect with Zn. A substantial exchange bias (EB) field is acquired below spin reorientation transitions which further reduces with higher chemical pressure of Ni. EB field is found to be ~5.71 kOe at 5 K, revealing a large value compared to similar zero field cooled EB systems. Finally, the magnetization data reveals a drastic magnetic phase change of the modified co-doping (Na, Mn) system than LaFeO3. This phase change is attributed to the mixed valence state of Mn ions present in the system due to substitutions. The above features induced due to the alkali metal in orthoferrites enhances further its applications towards spintronics and dielectrics in a wide field and temperature windows. The higher exchange bias effect in this material will certainly help in searching new materials for practical applications related to similar EB effect. Additionally, an order of change in dielectric response along with improved magnetic property makes this doped system a potential candidate for various electromagnetic devices
Studies on Multipurpose Liquid Repelling Functional Coatings for Various Industrial Applications
No full textGlass and metals have many applications in different fields like automobiles, solar panels, and also in the preparation of different industrial equipment. However, these surfaces are prone to many problems such as accumulation of dirt, corrosion, and fogging, due to their water and oil-loving nature. Different treatment methods have been implemented for the removal of oil and water from these surfaces. But these treatment methods have many disadvantages such as high operating costs, reduction in efficiency, and causing of different secondary pollutions which make the work more complicated. To overcome these problems, coatings on the surfaces were planned to be developed which can prevent the accumulation of water and dirt. Besides the above problems, the concern of pollution caused by oil spills on the water surface is also addressed. The oil- polluted water bodies harm marine animals, human life, and yield of the industries as it can reduce the efficiency of the machinery. Different treatment methods are used which have drawbacks of low separation efficiency, poor recyclability, and long processing time. Oil and water pose different wettability behavior for a particular surface. This property of different wettability helps in the separation of oil from water. Therefore in view of aforesaid problems, attempt has been made to develop superhydrophobic and superliquiphobic/superamphiphobic surfaces. In the present work, the superhydrophobic coating has been developed on a glass surface by using SiO2 microparticles modified by octadecyltrichlorosilane using the dip-coating technique. Hydrophilic property of glass surface was changed to superhydrophobic with water contact angle (WCA) of 165°±6° and sliding angle 2° ± 0.5° after a single dip-coating cycle. Superhydrophobic steel mesh was developed by chemically etching the surface in a solution of FeCl3 + HCl and then immersing the etched mesh in hexadecyltrimethoxysilane. Change in WCA was observed after etching as it reduces to 21° ± 4° and after coating with HDTMS WCA increased to 167° ± 3° and sliding angle of 6° ± 1°. Superliquiphobic/ superamphiphobic coatings were prepared on aluminum and steel surfaces by employing dip coating and drop-casting methods respectively using sol-gel of 1H, 1H, 2H, 2H, perfluorooctyltrichlorosilane modified SiO2 nanoparticles. The prepared superliquiphobic/ superamphiphobic surface was observed to repel liquids with surface energy as low as 27 mN/m (oil). Oil contact angles (OCA) were achieved as 155° ± 4° and sliding angle of 6°±0.5° and 157° ± 2° and sliding angle of 6°±1° for aluminum and steel surfaces respectively. The properties of the prepared coatings on different surfaces were examined by the contact angle, surface morphology, and FTIR analysis. Further, the effect of impact velocity of droplets on the coating surface was also investigated. It was observed that with an increase in water impact velocities, the transition from Cassie-Baxter to Wenzel takes place as the pinning of the droplet was seen on the superhydrophobic coated surface and bouncing was observed for all impact velocities for superliquiphobic surface. Similarly, different behaviors were observed for different liquids on superliquiphobic surfaces. Sticking at different impact velocities were observed for glycerol and hexadecane because of high viscosity and low surface energy, respectively. To make these coatings industrially applicable, experiments were carried out to verify the stability and durability of the coated samples. The samples were exposed to high temperatures, different pH solutions, and different mechanical disturbances such as abrasion, adhesion, twisting, bending, and jet impact tests which confirmed the stability and durability of the coatings. Also, mathematical correlations were developed for wettability and surface etching using different system parameters based on dimensional and statistical analysis. Experimental results were compared with the calculated data where the standard deviations were found to be below 10 for all cases thereby confirming the experimental results. The aforesaid prepared samples are thus recommended to be used for different applications such as self-cleaning, anti-fogging, and oil-water separation
Experimental Studies on Machinability Assessment of Difficult-to-Cut alloys (Inconel 718 and Ti-6Al-4V) During Traditional and Non-Traditional Machining
No full textIn the present dissertation, aspects of machinability of ‘difficult-to-cut’ nickel, and titanium based alloys (Inconel 718, and Ti-6Al-4V, respectively) are studied through traditional as well as non-traditional machining. During traditional machining (longitudinal turning operation), extent of machinability is assessed in purview of tangential cutting force magnitude, tool-tip temperature, depth of flank wear, area of crater wear, and machined surface integrity. Dominant tool wear mechanisms along with chip’s macro/ micro morphology are studied in detail. Surface integrity of the machined work part is analyzed which includes surface roughness, white layer thickness, and micro-indentation hardness. In this work, performances of PVD multi-layered TiN/TiCN/TiN coated cermet and PVD TiAlN coated PCBN (Polycrystalline Cubic Boron Nitride) brazed tipped carbide inserts are studied in the context of dry machining of Inconel 718. Results, obtained thereof, are compared to that of conventional uncoated WC-Co tool. It is observed that cermet causes lower cutting force than carbide, and PCBN tool. On the contrary, PCBN tool-tip experiences lesser temperature than remaining two counterparts. Amongst three inserts tested, cermet insert provides superior surface quality which can be described by less-severe feed marks, lower roughness value, and tiny white layer depth. In addition, application potential of microwave post-treated WC-Co insert is examined for dry machining of Inconel 718. It is experienced that, as compared to untreated counterpart, microwave post-treated tool exhibits higher hardness, and better wear resistance which, in turn, cause lower flank wear, and reduced tool-tip temperature. During microwave treatment, favorable microstructural alteration (skeleton-type structure) followed by formation of complex carbides results in improved mechanical properties of the tool material. Additionally, this dissertation includes machinability study of Ti-6Al-4V under Nanofluid Minimum Quantity Lubrication (NFMQL) in which Multi-Walled Carbon Nano-Tubes (MWCNTs) dispersed in commercially available rice bran oil, is utilized as nanocutting fluid. It is experienced that NFMQL outperforms dry machining, and machining under conventional MQL. Under NFMQL machining, ‘unaffected zones’ are distinctly identified over worn-out tool rake face which clearly confirms sustenance of strong film of boundary lubrication for prolonged machining duration; thus, protecting tool substrate. Apart from conventional machining (turning), non-conventional routes like Electro-Discharge Machining (EDM) and Wire Electro-Discharge Machining (WEDM) are attempted to evaluate machinability of Inconel 718 as well as Ti-6Al-4V. Application of MWCNTs added kerosene (as dielectric media) is recommended for EDM of Inconel 718. As compared to conventional (dielectric media with no additives) EDM, better machining performance, in purview of higher material removal efficiency, and lesser extent of electrode wear, is obtained in case of additive-mixed EDM. Improved machined surface integrity (better surface finish, lower crack density, and lesser recast layer thickness) is achieved in additive-mixed EDM when compared to conventional EDM. Finally, the present dissertation work attempts to investigate surface integrity of machined Ti-6Al-4V specimen obtained through multi-cut strategy (one main/ rough cut followed by multiple trim/ finish cut) of WEDM. As compared to main cut, better surface integrity is attributed to WEDMed Ti-6Al-4V specimen obtained through finish cut. Formation of rutile TiO2 over WEDMed surface is detected during finish cut which is expected to improve biocompatibility of Ti-6Al-4V work material
Analysis of Electroencephalogram Signal for P300 Based Brain-Computer Interface Speller
No full textAbrain-computer interface (BCI) speller is a communication medium with the outer world for the patients suffering with neuro-muscular disorders. A P300 speller which translates the brain signal into machine commands provides such communication inter-face to convey their thought without any motor movement. A P300 speller aims to spell characters by using the electroencephalogram (EEG) signal and its performance can be defined by the number of correctly recognised characters. P300 is an event-related potential (ERP) which is appeared in the EEG signal when random stimuli occur to the subject. Various types of BCI paradigms can be used for character spelling which provide stimuli to the subject. Generally, row-column paradigm is used for P300 based character recognition in BCI application due to its simple graphical user interface (GUI). The row and column of the matrix are intensified randomly and successively which generate the stimuli. P300 appears in the EEG signal when the row or column of the desired char- acter intensified and the character is recognised from the detected P300. To detect the P300, various signal processing algorithms have been introduced which analyse the EEG signal and classified them as P300 target or P300 non-target. A P300 based character recognition system consists of the following stages: preprocessing, feature extraction, feature optimization and classification. The amplitude and latency of the EEG signal vary based on the psycho-physiological condition of the subjects and the EEG signal is easily affected by the surrounding noise which will reduce the character recognition performance. Therefore, there is a requirement of feature extraction technique to rep- resent the EEG signal efficiently and to detect the dynamic changes of the EEG signal. The EEG signals are acquired through a multi-channel system with high sampling rate. High dimension of EEG features can create over-fitting problem, and irrelevant features can reduce the classification performance. Therefore, the need of feature optimization algorithms are constantly increasing to overcome the dimensionality problems. Mo- tivated by the above discussion, this thesis focuses on the feature extraction, feature optimization and classification technique for the improvement of P300 based character recognition performance. At first, a feature extraction technique is developed based on principal component analysis (PCA) which extracts the important features from the dataset and removes the redundant and irrelevant features. With these PCA based fea- tures, ensemble of weighted support vector machine (EWSVM) technique is proposed for classification. In EWSVM, a weight is assigned to each classifier, so that better classifiers get more weightage compared to other classifiers. As a result, when the clas- sifiers’ outcomes are averaged out, best classifier provides more impact on the output for the weight assigned to it. Next, for P300 based character recognition, hand-crafted features are not efficient to represent the signal properly due to amplitude variation and nonlinearity of EEG signal. To overcome the limitation of the hand-crafted features, convolutional neural network (CNN) based automated feature extraction technique has been developed as it extracts hierarchical features from the dataset. In the developed technique, two different convolution layers are used to extract the spatial and temporal features from the dataset, respectively. The deep features are extracted from the fully- connected layer of the trained CNN architecture. After extracting deep features from the EEG signals, Fisher’s ratio (F-ratio) based feature selection technique is adopted to find out the optimal features from the extracted features. Subsequently, to improve the P300 based character recognition performance, a feature fusion framework is devel- oped. Autoencoder (AE) technique is proposed which extracts abstract features from the input data. Unlike PCA, an AE uses non-linear transformation with non-linear acti- vation function which extracts abstract information from the EEG signal and temporal features represent the dynamic information of the EEG signal. Therefore, these two features may be partially complementary in nature. It is seen from the experimental results that their combination helps to improve the P300 based character recognition performance. In AE, a sparsity constraint is imposed on the hidden nodes to overcome the over-fitting problem, and the modified AE is referred as sparse autoencoder (SAE). SAEs are stacked together and it is denoted as stacked sparse autoencoder (SSAE). Finally, multiscale convolutional neural network (MsCNN) is proposed which extracts multi-resolution deep features from the data. These features learn diverse information from the acquired EEG signal. To overcome the limited dataset problem and require-ment of long calibration time, transfer learning (TL) technique is proposed in this work for P300 based character recognition. In this process, a network is trained with suffi- cient number of training data of one subject. After training, this pre-trained network is fine-tuned with a new subject. Less data is required for fine-tuning, and as a result, the requirement of calibration time is also less. Experimental results are conducted on two publicly available datasets, BCI Competition II and III datasets to demonstrate the effectiveness of all the proposed techniques