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Development of Carbon Dot and Related Hybrid Materials for Fluorescence Sensing Applications
No full textAlthough extensive work has been done on rapid detection of metal ions, anions and biomolecules using luminescent carbon quantum dots (CD) based on different mechanism like IFE and PET mechanism. The available documentation on development of luminescent sensors for the visual detection of biomolecules and pollutants is extremely scant. The main focus of this doctoral research work is to design carbon dots for sensing of important biomolecules and environmental pollutants. We aim to incorporate carbon dots in the solid matrix to enhance the fluorescence properties. Utilizing the improved fluorescence property of carbon dots we further want to construct smart nanosensors to detect biomolecules and pollutants in aqueous phase as well as in solid phase. To achieve our aim, we mainly emphasize on the following issues, 1) low energy and cost effective approach for synthesis of carbon dots, 2) choice of a molecular precursor certifying the availability of abundant surface functional groups so as to ensure a superior molecular recognition by the target analyte, 3) selection of proper quencher, 4) Incorporating CD in a solid matrix to develop a more stable carbon dot based sensor, 4) development of solid strip or hydrogel based sensor for visual detection of the analytes. In line with our objective we have developed CD-based fluorescence nanosensor for detection of two important biogenic amines i.e. bilirubin and histamine, one organic pollutant 2,4,6 trinitrophenol and one inorganic pollutant dichromate
Exploration of Marine Microbes for Bioethanol Production from Lignocellulosic Substrates in Seawater Medium
No full textThe growing demand for petroleum-based fuels in the aftermath of rapid growth of world population and climate change concerns has augmented the need for renewable bio-energy resources with minimal greenhouse gas emissions. However, the operational hazard of the transition towards a bio-based economy and running large-scale biomass processing has led to an unprecedented surge in fresh water consumption that renders biofuel a high water footprint product. In the current study, seawater is used as a reaction medium for pretreatment, saccharification and fermentation seeking the reduced consumption of fresh water in bioprocesses. The first objective addresses the feasibility of optimized model of microwaveassisted NaOH pretreatment of lignocellulosic biomass using seawater medium. Response surface methodology (RSM) based Box Behnken design (BBD) was employed to model, predict and validate cellulose release and reducing sugar yield for rice straw which was further validated for sugarcane bagasse and kans grass respectively. The optimized pretreatment conditions (8.50% substrate loading, 1.94% NaOH, 4.09 min and 160 W) in rice straw resulted in a cellulose release of 65.43% and reducing sugar yield of 0.554 g/g. The second objective deals with establishing a seawater-based model of eco-friendly mode of biodelignification by exploiting the efficiency of marine ligninolytic bacterial strains isolated from seawater habitat. The isolated halotolerant strain was identified and named Shewanella chilikensis LDB and used for the biopretreatment of lignocellulosic substrates in seawater medium. Estimation the residue lignin upon biopretreatment revealed a reduction in lignin content from 29.15% to 20.28% in sugarcane bagasse, whereas in case of rice straw and kans grass the observed lignin removal was 23.70% to 16.42% and 25.33% to 22.58% respectively. Further, a similar approach was devised in the third objective, where isolation and screening of a potent marine cellulolytic bacteria Bacillus haynesii CDB3 was carried out and the bacterial cellulase was used for further studies. The crude cellulase was found to be halotolerant, thermostable and exhibited a maximum residual activity of 1.15 U/ml at pH 5 and 45 °C. The bacterial cellulase was employed for the saccharification of alkali pretreated biomass (under conditions optimized in the first objective) in seawater medium which resulted in a sugar release of 3.355 mg/ml for sugarcane bagasse and 3.049 mg/ml and 2.599 mg/ml for rice straw and kans grass respectively. The final objective addressed the feasibility of the sequential pretreatment (cotreatment: microwave-NaOH + ligninolytic LDB1 strain) of lignocellulosic biomass followed by saccharification using crude cellulase from marine cellulolytic CDB3 strain. The lignin content in the cotreated substrates greatly diminished from 10.45% in alkali pretreated rice straw to 6.37% upon cotreatment. Likewise, alkali pretreated sugarcane bagasse contained 13.46% lignin which upon cotreatment reduced to 7.20%, whereas for kans grass the lignin gradually declined from 16.43% to 11.02%. Saccharification of the cotreated substrates using crude cellulase resulted in a reducing sugar release of 6.12 mg/ml for rice straw, 6.98 mg/ml for sugarcane bagasse and 4.65 mg/ml for kans grass respectively. The saccharified hydrolysates were subsequently subjected to fermentation using marine yeast AZ65 strain and the ethanol concentration was estimated by potassium dichromate method. The concentration of ethanol upon cotreatment were 2.76 g/L (kans grass), 3.65 g/L (rice straw) and 4.34 g/L (sugarcane bagasse) respectively. The findings of the study demonstrated the rationale of using a combined biological and chemical mode of pretreatment in seawater medium for lignocellulosic biomass conversion into biofuels
A Machine Learning Approach to Detect Changes in Single-Lead Electrocardiogram Signals Post Coffee Consumption
No full textElectrocardiograms (ECGs) are the most reliable method for detecting changes in heart function. It reflects the heart's electrical activities and can be interpreted using various waves, peaks, and intervals. Several factors influence the heart's functionality, including lifestyle, stress, food, etc. The world's most extensively consumed beverage, coffee, is a vital component of daily living. Caffeine, the primary component of coffee, is believed to influence the physiology of the heart. However, the influence of caffeinated coffee consumption on cardiac electrophysiology, as assessed from morphological features (e.g., peaks, waves, intervals), is ambiguous, as the results are inconsistent. This has led to the investigation of alternative feature extraction approaches for properly detecting changes. The current study focuses on evaluating single-lead electrocardiogram (ECG) data to determine if there are any significant changes in the ECG signal following coffee drinking and then on predicting these changes using various machine learning models. The ECG signals of human participants were obtained both before and after the consumption of coffee. 1260 ECG segments were extracted from the two data groups (control and stimulus) for further investigation. A preliminary investigation on the ability of statistical and entropy characteristics to detect coffee-induced changes was first performed. The statistical analysis yielded favorable results. These sets of features were then extracted from the ECG signals after employing three distinct decomposition techniques: discrete wavelet transform (DWT), wavelet packet decomposition (WPD), and continuous wavelet transform (CWT). Multiple machine learning (ML) models used these extracted features as input, and the classification performance for predicting the coffee-induced alterations in the ECG signal was examined. After utilizing the decomposition method, the classification accuracy appears to have improved. In addition, the CWT approach was found to be more accurate than the other two decomposition methods in predicting the onset of any change in the ECG signal after coffee consumption. In the future, the current work may potentially prove helpful for identifying changes in cardiac activity following consumption of other caffeinated beverages (e.g., tea, cola, soft drinks, etc.), medications, and alcohol
Advancing the Prediction of Severe Thunderstorms Over the Eastern Parts of India Using Satellite Observations and High-Resolution Models
No full textThunderstorms are extreme weather events that result from severe convection, often associated with heavy rainfall, lightning, hail, gusty winds, squall lines, and sometimes even tornados. The pre-monsoon (March to May) season is significant for studying thunderstorms, as most Indian regions, especially the eastern part, experience frequent thunderstorms during this season. Each year, thunderstorms cause heavy damage to lives, properties, and livelihoods in this region. Hence, understanding the nature of thunderstorms in India is a focus of researchers around the country, which could, in turn, lead to better prediction of thunderstorm events. In this regard, numerical weather prediction (NWP) is an essential component of early warning of the occurrence of thunderstorms. However, the performance of the NWP models is highly dependent on the accurate representation of the initial conditions. The present thesis takes the opportunity to study ways to improve the prediction of thunderstorms in eastern India based on observation and modeling efforts. Thunderstorms' low spatial and temporal extent make it challenging to observe in a vast country like India, where not enough Doppler weather radar or in situ observation stations are available. It hampers understanding of thunderstorm attributes' spatial and long-term variation in thunderstorm-prone regions. Therefore, this thesis's first working chapter aims to prepare a long-term thunderstorm database for the pre-monsoon season in India using high-resolution satellite rainfall data obtained from Global Precipitation Measurement (GPM). The high-resolution Integrated Multi-Satellite Retrievals for GPM (IMERG) final product is used to identify thunderstorms from 2001 to 2021. The 93rd percentile of rainfall appears to be the optimum threshold for the detection, with a success ratio of 82% (642 events are confirmed out of 786 detected). The derived thunderstorm database enables the study of spatial and temporal variations of thunderstorms in the region. The study highlights an increasing trend of thunderstorm activity in the eastern parts of the country, focusing the attention of the rest of the thesis on the eastern part of India. Different thermodynamic stability indices often describe the favourable atmospheric conditions for thunderstorms. These indices can provide insights into the characteristics of individual thunderstorms. Hence, the first working chapter also brings out the combined use of radiosonde, reanalysis, and satellite data over eastern India to better understand thermodynamic indices during the pre-monsoon season. In this chapter, it has been demonstrated that the thermodynamic indices derived from the INSAT-3D satellite can reliably capture the instability of the atmosphere during thunderstorm days. The second working chapter tries to understand the role of horizontal resolution and downscaling approach for thunderstorm simulation in the Weather Research and Forecasting (WRF) model. The model is configured with two nested domains with 9 km and 3 km domains (DD3), 6 km and 2 km domains (DD2), and a single domain at 3 km resolution (SD3). The average mean errors of 2-m temperature (T2) and 2-m relative humidity (RH2) in the DD2 experiment are 0.7 ̊C and -6%, respectively, at the mature stage and 0.2 ̊C and -4%, respectively, at the dissipating stage. The error in SD3 and DD3 is relatively higher (9-17% for T2 and 20-60% for RH2) relative to DD2. The DD2 could show slightly higher instability (convective available potential energy, CAPE, 3188 J kg-1) as compared with DD3 (3164 J kg-1) and SD3 (3020 J kg-1). The rainfall timing and magnitude have also improved in 8 and 12 cases, respectively, in the DD2 run. A high critical success index and less RMSE of different rainfall thresholds suggest better simulation of rainfall magnitude in the DD2 run. The results highlight that high resolution with nested configuration yields better simulation skills than the single domain configuration at high resolution. The third working chapter investigates the sensitivity of land use land cover (LULC) initialization in the WRF model simulation of thunderstorms. Three types of land use land cover (LULC) maps have been prepared using supervised machine learning methods such as Classification and Regression Trees (CART), Naive Bayes (NB), and Support Vector Machine (SVM) from Landsat 8 data. A high accuracy score (85%) and kappa coefficient (81%) revealed the best performance of the CART classifier in generating the LULC maps. Model results highlight that the CART experiment exhibits relatively less bias in RH2 (~ -10% to -5%), T2 (<2.5°C), and 2m wind speed (-1 to ~1.8 m s-1). The CART could improve the rainfall with the least error (~ -16 mm) compared to CNTL (~ -33 mm), NB (~ -37 mm), and SVM (~ -38 mm), and supported by the quantitative statistical analysis viz., less false alarm ratio, high detection rate and critical success index for all thresholds. LULC class-wise analysis indicates a higher variation of surface and lower atmospheric parameters over urban, shrubland, and cropland while less variation over barren, forest, and water. The fourth working chapter assesses the thunderstorm predictability by assimilation of INSAT 3D atmospheric profiles into the WRF model initial condition through the 3-dimensional variational data assimilation technique. Two different numerical experiments were conducted, EXP-CN (without assimilation) and EXP-IN (with INSAT-derived profiles assimilation). The results show consistently enhanced performance in the representation and simulation of surface variables such as RH2, T2, and WS10 in EXP-IN compared to EXP-CN. The profiles of RH, Winds, and vertical velocity have improved in the EXP-IN experiment. The higher CSI and lowest false alarm ratio (FAR) make it evident that the assimilation of INSAT data enhances the skill in rainfall prediction in thunderstorm study. The outcome of the thesis is that the prediction of thunderstorms and their stages can be significantly improved by 10-17% by configuring the model at high resolution with nested domain, by 10-18% by representing realistic LULC forcing, and by 30-37% by assimilating atmospheric profiles of temperature and moisture
Towards the Rational Design of Surface Modified Metal Oxide Nanorod-based Catalysts for Efficient Synthesis of Biologically Important Molecules
No full textMetal oxide nanostructures are widely used in various fields such as environmental, biological, electronic, super capacitor, battery, sensing, and catalysis. Among various classes of metal oxide nanostructures, metal oxide nanorods possess particular importance because of their significant physical and chemical properties. In particular, they are potential material for catalysis due to their high surface to volume ratios, shape anisotropy, high coordination domain and also superior electronic properties. However, pristine nanorods have significant stability and leaching issues and hence, surface modifications of these nanorods are of greater scientific interest. Various approaches like compositional effect (decorating on suitable support and varying the acidity), electronic effect (nanoparticle decoration), morphological effect (varying the shapes and crystal planes), etc have proven to be some of the most preferred approaches while using such metal oxide nanostructures in catalysis. Keeping these in mind, the overall objectives of my PhD work was to modify the metal oxide nanorods (WO3, MnO2, Fe2O3 and CeO2) and use them as potential heterogeneous catalysts for efficient synthesis of biologically important molecules like amino alkyl napthol, bisamides, Quinoline, Coumarin, and Pyran. Broadly, the effect of acidity (WO3), the decoration of different nanoparticles (Au, Ag, and Pt on MnO2, Fe2O3 and CeO2), morphology (twin sphere vs. nanorods of Fe2O3) and metal-support interaction on the overall catalytic activity have been thoroughly investigated. Each chapter of this thesis has different studies associated with the main objective of design of highly active and stable metal oxide nanorod-based hybrid catalyst materials. In the first part, it documents the use of acidic WO3 supported on GO for efficient synthesis of Quinoline and derivatives. This work has been published in New Journal of Chemistry, 2022, 46, 4850-4863. Envisioning the importance of metal nanoparticle decoration on a nanorod-based system, in the second objective, gold nanoparticles were decorated on GO-MnO2 nanohybrid and used it as highly active, stable, and recyclable catalyst for synthesis of Bisamides and Betti bases. This work has been published in Molecular Catalysis, 2019, 474, 110415. From the previous objective, decoration of nanoparticles generally increased the activity of material. This could be due to surface defects (oxygen vacancy) and electronic effect (redox potential value). With an eye to see such effects of metal oxide nanorods in the synthesis of biologically important molecule, the third objective involves the detailed studies of such effects in a GO-CeO2 Pt system during the synthesis of Pyran and its derivatives. The final objective documents the morphology and crystal face effect of Fe2O3 (twin sphere vs. nanorods) in the synthesis of Coumarin. All the materials presented in this thesis proved to be stable, efficient, and recyclable system for the above applications, which are of significant biological importance
Deciphering The Role Of Stonin 2 In Autophagy and Autophagic Lysosome Reformation to Control Cell Survival in Oral Squamous Cell Carcinoma
No full textStonin 2 is an endocytic protein encoded by the STON2 gene, also known as STN2 and STNB. This gene is found on human chromosome 14q. It is an endocytic protein that is coupled with clathrin and contributes to the process of sorting endocytic complexes. Here, I report that STON2 expression increases with an increase in the grade of the oral cancer tissue. Further, STON2 overexpressed cells possess a higher proliferation and migration rate in oral cancer cells. STON2 helps maintain lysosomal functions by preserving the lysosomal membrane integrity. It activates the Akt-mTOR axis and retains the mTOR on the membrane of the lysosomes. Trifluoperazine dihydrochloride (TFP), a STON2 inhibitor, disrupts the Akt-mTOR pathway and permeabilizes the lysosomal axis. TFP targets STON2 to reveal its anti-cancer effects in oral cancer cells. Further, I have demonstrated that STON2 is essential for Beclin 1-dependent autophagy induction in oral cancer cells. In CAL33 and FADU cells, STON2 overexpression fails to degrade the autophagic adaptor p62 and increases LC3 puncta accumulation, indicating STON2 alters autophagic flux in oral cancer cells. In addition, STON2 accumulates LC3-LAMP1 dual-positive autolysosomes. Autophagic lysosome reformation (ALR) regenerates lysosomes from autolysosomes. In basal and starved CAL33 cells, STON2 induces tubulation events of ALR. In contrast, STON2 depletion does not trigger ALR under 10 h starvation, proving its vital role in ALR. To determine the role of STON2 in the late stages of ALR, I examined tubulation events in the presence of dynasore, a dynamin 2 inhibitor. The generation of larger tubules in dynasore-treated cells confirmed the role of STON2 in early ALR events. Further, STON2 reactivates mTOR under basal and deprived conditions to replenish the nutrient status of the oral cancer cells, and I found that STON2 does not trigger ALR in the presence of Rapamycin. Moreover, STON2 fails to initiate ALR in clathrin-depleted cells, confirming that clathrin is essential for STON2-induced ALR progression. The crucial mechanism of STON2-dependent endocytosis/autophagy in maintaining lysosomal homeostasis must be explored in detail. In future, STON2 could function as a biomarker for the early detection and diagnosis of oral cancer progression
Investigation of Multifunctional Off-Board EV Battery Charger with Solar PV Grid Interface
No full textIncreasing environmental pollution and limiting petroleum fuels resulted in two significant changes in the world. The first change is adopting renewable energy sources (RESs) by replacing conventional energy sources, and the second is adopting electric vehicles (EVs) by replacing combustion engine-based vehicles. However, in the current scenario, an enormous increase in EVs resulted in the massive installation of charging stations powered mainly by the grid. As a result, grid power quality (PQ) issues, such as voltage and current distortions, appear in the power system. Therefore, designing a multifunctional charging infrastructure that can be used for EV charging and simultaneously improve grid PQ is essential. Considering the PQ issues, this thesis deals with designing, controlling, and implementing the multifunctional EV charging system to power the EV batteries and simultaneously provide grid support services. Furthermore, integrating Solar PV and storage systems into the EV Charging system reduces the energy needed from thermal generating stations. It promotes reduced greenhouse gas emissions, charging costs, environmental constraints, and EV grid dependability. The multifunctional chargers are designed to operate in grid connected operation and standalone operation, with smooth and seamless transition from one to another to provide uninterruptable power to EVs and utility loads. In addition, the charger locally mitigates harmonic distortions that appear at the grid side due to EV charging and nonlinear charging station loads to avoid penalty on the charger owner. The charger facilitates bidirectional active power exchange in grid-to-vehicle (G2V) and vehicle-to grid (V2G) operating modes. Furthermore, the charger provides other multifunctional operations in charger-for-grid (C4G) operations like grid current harmonic compensation (GCHC), reactive power compensation (RPC), and reactive power support (RPS) to the grid. Moreover, the charger also supports vehicle-to-home (V2H), Energy storage-to-home (E2H), vehicle-to-energy storage (V2E), and vehicle-to vehicle (V2V) operation, which increases the operational efficiency of the charger. In this thesis, a single excited three-phase seven-level cascaded H-bridge bidirectional AC-DC converter (CHBDC) topology is adopted for the grid converter of the charger. During multifunctional operation, the CHBDC control algorithm manages energy management between sources, maximum power point tracking of solar photovoltaic (PV), DC link voltage regulation, and PQ services. Particularly, different CHBDC control algorithms are presented to achieve efficient multifunctional operation of the charger without compromising grid PQ, irrespective of abnormal system conditions like grid outages, load fluctuations, and nonideal grid voltages (unbalanced and distorted). The presented multifunctional EV charger configurations are modeled in Sim Power System (SPS)/ MATLAB Simulink environment, and the same has been validated by an experimental prototype developed in the laboratory. Furthermore, the thesis analyzes and presents the charger performance during various steady state and dynamic conditions
Experimental Studies of a Low Heat Rejection Engine Run in Dual-Fuel Mode
No full textHydrogen is perceived to be a potential sustainable energy source of the future due to some of its favorable properties such as its carbon-free nature, high calorific value, wider flammability range, and high burning velocity. However, the problems associated with the storage of hydrogen, and the limited availability of hydrogen to meet the demand has compelled the research community to identify other possible solutions. Oxy-hydrogen gas (HHO) is a carbon-free fuel similar to hydrogen which can be produced by the water electrolysis process. Hence, this research work explores the possibility of using HHO gas also known as Brown gas as an alternative to hydrogen in a low heat rejection (LHR) engine under dual-fuel mode (DFM). A biodiesel-diesel blend (JME20) containing 20% Jatropha methyl ester (JME) and 80% diesel on a volume basis is used as a pilot fuel, while HHO gas is used as an inducted fuel in the intake manifold of a compression ignition (CI) engine. For the investigation, a single-cylinder, naturally aspirated, direct injection (DI) diesel engine developing a power output of 4.4 kW at a constant speed of 1500 rpm is converted to DFM. The engine behavior in terms of combustion, performance, and emission parameters of the dual-fuel engine is assessed without and with fuel and engine modifications. HHO gas is generated by the electrolysis of water from an in-house fabricated wet-cell electrolyzer. In the first set of experiments, baseline data of conventional CI engine behavior in terms of the combustion, performance, and emissions are collected for diesel/JME20 at optimized engine conditions in the single fuel mode. In the next set of experiments, the engine is modified to DFM to study the effect of using HHO gas as inducted fuel with diesel/JME20 as pilot fuels. The combustion, performance, and emission characteristics of the dual-fuel engine run on diesel/JME20+HHO are evaluated and compared with baseline data and validated with theoretical combustion analysis of the dual-fuel engine is carried out by using MATLAB coding. The dual-fuel engine is modified to run in the low heat rejection (LHR) mode by replacing the normal piston with the Yitira Stabilized Zirconia (YSZ)/ YSZ+CeO2 (Cerium Oxide) coated pistons to reduce heat loss from the engine. Then experiments are conducted on retrofitted dual-fuel LHR engine. The results of the YSZ+CeO2 coated piston-fitted LHR engine run on JME20+HHO exhibited higher BTE than YSZ coated piston engine operation. The maximum CO, HC, and smoke emissions with the YSZ+CeO2 coated piston-fitted engine operation in the DFM with JME20+HHO, are found to be lower by about 44.1%, 46.7%, and 21.5%, respectively, compared to baseline data at full load while nitric oxide (NO) emissions were higher by 19.17%. In the next stage of experiments, the JME20+HHO dual-fueled LHR engine’s operating conditions will be optimized for best performance. The operating conditions of are optimized by the collated data from experiments at varying compression ratio (CR) from 16.5 to 18.5, varying fuel injection pressure (FIP) from 220 to 240 bar, and varying the start of injection (SOI) / injection timing from 24.5° to 27.5°CA bTDC. The results also reveal that operating the dual-fueled LHR engine with 18.5 CR, 240 bar FIP, and 26°CA bTDC SOI is a good strategy for running the dual-fuel engine on JME20 with HHO induction because of better BTE (6.6% higher than diesel) and combustion characteristics and lower CO, HC, and smoke emissions. In contrast, a penalty in NO emissions is noticed irrespective of the engine operating conditions. Further investigations are carried out to address the higher NO emissions noticed in optimized JME20+HHO duel-fueled LHR engine behavior by doping antioxidants with pilot fuel. Antioxidants at concentrations of 500, 1000, 1500, and 2000 ppm are doped in the pilot fuel (JME20 blend). The pilot fuel was doped with two chemical antioxidants, N-Isopropyl-N'-phenyl-1,4-phenylenediamine (IPPD) and N, N'-Diphenyl-p phenylenediamine (DPPD), as well as two natural antioxidants made from biomass, Alibizia lebbeck leaf powder (ALLP) and Pongamia pinnata leaf powder (PPLP). The combustion, performance, and emissions of the test dual-fuel engine run on antioxidant-doped JME20+HHO are assessed and compared with those of baseline data. The results revealed that a 2000 ppm DPPD-doped JME20+HHO operation shows 5.6% lower BSEC, 32.3% lower CO, 35.3% lower HC, and 13.9% lower smoke as compared to baseline diesel data at full load. Among all the antioxidants, PPLP antioxidant-doped JME20+HHO operation shows a 10% NO emission reduction at 2000 ppm concentration compared to JME20+HHO operation. This research work identified the optimized LHR engine (18.5 CR, 240 bar FIP, and 26°CA bTDC SOI) for HHO gas induction with PPLP antioxidant-doped JME20 operation. The corresponding results showed an 9.13% higher HRR, 6.09% lower BSEC, 30.8% lower CO, 33.3% lower HC, and 8.31% lower smoke compared to baseline diesel data at full load
Geochemistry of Devi River Estuary, East Coast of India: Insights from Distribution of Elements and CO2 Dynamics
No full textEstuaries represent a transition zone at the land-sea interface, and it is characterized by strong geochemical gradient due to the mixing of fresh river- and saline ocean- water. It is a highly dynamic system where behavior and fate of elements have been modified drastically due to large scale variation in different physio-chemical properties. Estuaries also play an important role in modulating the global CO2 budget. Thus, this thesis aims to carry out an extensive geochemical investigation over a period of one year (2018-19) in the tropical mangrove dominated Devi river estuary, formed by a major distributary of the Mahanadi River, which is the third largest river of Peninsular India, in order to understand the behavior of elements in surface water and sediment, and its role in air-water CO2 exchange. The study is based on sampling conducted during May 2018, September 2018 and February 2019 that represent summer, monsoon, and winter periods, respectively. Salinity in the Devi river estuary ranges from 1.1 to 33.1 ppt and 0.1 to 29.5 ppt during summer and winter, respectively. Contrarily, salinity decreases in the monsoon period (0.1-13.6 ppt). The pH of the estuarine water ranges from 6.24 to 8.97. The alkalinity and dissolved inorganic carbon range from 23.5 to 232 mg/l and 44 to 382 mg/l, respectively. The major ion chemistry and nutrient distribution show that Na, Cl, SO4, NO3 and PO4 behave conservatively whereas DSi, Ca and Mg exhibit non-conservative behavior. This is attributed to one or more processes involving mixing of river and ocean water, weathering of minerals, biological processes and carbonate mineral precipitation/dissolution. Nutrient stoichiometric ratios – NO3:PO4, DSi:PO4 and DSi:NO3 – vary spatially and seasonally as a result of differences in geochemical processes prevailing in the fresh- and marine-water environment. The distribution of dissolved trace elements (Sr, Ba, Fe, Mn, Mo, V, Cu, Cr, Co, Ni, Zn, U and Pb) in the estuary is influenced by their respective sources, abundances in the fresh and marine end-members, mixing processes and their ability to take part in various geochemical reactions occurring in the transitional zone. All the trace elements behave non-conservatively in the estuary during at least one season within the studied period. Flocculation related removal of Fe, Mn, Mo, V, Pb and U occurs in the low- to mid- salinity zone. A pronounced mid-estuarine maxima is exhibited by Ni and Zn, whereas Cu shows a poor mid-estuarine maxima. Barium shows peak concentrations in the low salinity zone due to significant addition. A high pCO2 (1565 ± 782 μatm) in comparison to that of atmosphere is found in the estuary. The pCO2 is higher during winter (2071 ± 828 μatm) compared to summer (1675±665 μatm) and monsoon (951 ± 316 μatm). Based on the relationship among DO%, pH, pCO2 and chlorophyll, the process of organic matter degradation is inferred to be a major controlling factor for pCO2. Net annual CO2 efflux from the Devi river estuary is estimated to be 40.1±7.5 mol C m-2 yr-1. The highest efflux was observed during summer season (126±69 mmol C m-2 d-1) in spite of higher pCO2 in winter. This is due to higher temperature and wind speed in summer. The Devi river estuary is estimated to contribute about 0.003–0.006% of the total global CO2 emission from estuaries. Bulk chemical composition of the estuarine sediments show an enrichment of Ba, Nb, Pb, Rb, Th and Zr with respect to the upper crust. Different weathering indices along with the A–CN–K and A-CNK-FM ternary plots suggest that sediments are dominantly derived from a felsic source rock and have undergone low to moderate chemical alterations. High LREE/HREE (11.16±3.68), negative Eu anomalies, and (La/Yb)n and (Tb/Yb)n values confirm that sediments are derived from the Eastern Ghat Group of rocks. Upper estuary sediments show negative Eu anomalies which is similar to that of the source. However, positive Eu anomaly is mostly observed in lower estuary. Contrasting Eu anomalies between upper- and lower-estuarine sediments are uncharacteristic of previously studied major global estuaries. Concentration of REEs, Sc, Fe, Mo, V, Zn, Zr, Nb, U, Ti, Na and P in sediments increases up to 20 ppt salinity, and followed by declining trend towards the mouth. This is mostly due to salinity-induced flocculation of colloidal particles. Gradual decline in concentration of Cr, Co, Ni, Cu, Rb, Sr, Sb, Cs, Ba, Pb, Al, Mn, Mg, Ca and K with increase in salinity is attributed to desorption of elements from sediments. The SiO2 content shows increasing trend towards mouth due to incorporation in diatom frustules in the lower estuary. The findings of this study emphasize the importance of intrinsic physicochemical parameters, mainly salinity, pH and redox condition, on different geochemical processes for governing behaviour of different elements and nutrients. Additionally, it highlights the role of small-scale estuaries in significant contribution of CO2 to the atmosphere. It further contributes towards the increasing dataset of pCO2 and CO2 effluxes from estuaries worldwide in order to accurately estimate the role of estuaries in the global carbon budget
Hydrodynamic Stability of Swirling Flows of Newtonian and Non-Newtonian Fluids
No full textA rigorous linear convective instability analysis has been carried out for swirling flows of Newtonian and non Newtonian fluids near rotating disk. The BEK family of flows are instrumental in turbomachinery and in rotor-stator systems. There are widely applicable turbomachinery applications that involve Newtonian fluids (e.g. water, steam, oil) in various industrial processes. In petroleum industries, non-Newtonian fluids such as Carreau fluid and Bingham fluid are widely used. As a result, study on Newtonian and non Newtonian fluids is highly beneficial in engineering and science, and they have piqued the interest of several academics and experts. The primary goal of this dissertation is to look into the impacts of radial surface stretching, and surface roughness on the hydrodynamic stability of spinning disk boundary layer flows of Newtonian and non-Newtonian fluids. Because the stretching mechanism aids in cooling industrial machine walls, this research is vital from an industrial standpoint. The impacts of the stretching mechanism and surface roughness on the convective instability of the aforementioned swirling flows in the presence of the Coriolis force have been investigated numerically for the very first time in the literature . The Kármán similarity transformations are used to convert the system of PDEs representing the governing equations into a system of highly non-linear, fully coupled ODEs. These system of self-similar equations are then solved numerically to produce the mean flow solutions. Subsequently, a linear convective instability analysis is performed using the Chebyshev collocation method to obtain the neutral stability curves. Based on the stability curves, a radial stretch of the disk has a globally stabilizing impact on both the inviscid Type-I and viscous Type-II instability modes of Bödewadt flow and Ekman flow. However, it has a globally destabilizing influence on the instability modes of Kármán swirling flow in presence of the Coriolis force. The roughness of the disk has a globally destabilizing influence on the Type-I and Type-II modes for the stagnation point flow over a rotating disk. Furthermore, the stability curves reveal that when the disk is expanded radially, shear thinning fluids of the Carreau model show a globally stabilizing effect. In contrast, shear-thickening fluids show a globally destabilizing impact. An energy analysis of the flows has been undertaken simultaneously to verify the above physical occurrences. The acquired results strongly confirm the previously published findings and will be used as a standard for our future research