Sathyabama Institute of Science and Technology
Recent publications
The use of second-generation biodiesel has been increasing swiftly in the place of petroleum fuels. This paper explains the influence of chicken waste on a direct injection diesel engine performance, combustion and emission characteristics. The combination of hydrogen and biodiesel derived from waste chicken fat were examined across various engine speeds such as 1000 rpm, 1500 rpm, 2000 rpm, 2500 rpm and 3000 rpm. The hydrogen is a green and efficient fuel that was mixed with the biodiesel at the level of 5 liter per minute. The tested biodiesel blends were C0 (Diesel 100%), C15 (Chicken fat biodiesel 15%+pure diesel 85 %), C30 (Chicken fat biodiesel 30%+ pure diesel 70%), CH5 (Diesel + 5 LPM Hydrogen), C15H5 (Chicken fat biodiesel 15%+pure diesel 85%+ 5LPM), and CF30H5 (Chicken fat biodiesel 30%+pure diesel 70%+ 5LPM Hydrogen). The results proved that the hydrogen enriched biodiesel improved the performance in terms of brake thermal efficiency, brake power and lowered the brake specific fuel consumption. At higher speeds CH5 produced higher brake power, however at lower speeds C15H5 and C30H5. Further the blend C30H5 reported the maximum BTE of 38% at 2000 rpm. With regard to emissions, all the biodiesel blends reported to produce least carbon monoxide (CO) emission. However, all biodiesel blends reported marginal increase in the nitrogen of oxides (NOx) and carbon dioxide (CO 2) irrespective of the engine speeds.
The combustion of fossil fuels is one of the main reasons for the increase in global warming. Exhaust emitted from burning fossil fuels affects both environment and human health. The present study uses non-edible rubber seed oil (RSO) biodiesel with hydrogen in an unmodified diesel engine. The experiments were carried out with 10 %, 20 %, and 30 % biodiesel blends with 10 L/min of constant hydrogen supply. A series of experiments were conducted in the constant engine speed with a varying load of 0–100 % in intervals of 25 %. The hydrogen has been supplied to the combustion chamber via an air intake manifold. Using RSO biodiesel blends with hydrogen increases brake thermal efficiency at lower fraction ratio. Further, the fuel consumption was low when 10 % of biodiesel was used with hydrogen. As the concentration of the blends increased, both brake thermal efficiency and brake specific fuel consumption were massively affected due to the higher viscosity and lower heating values. Due to the effect of heating value, the exhaust gas temperature for the biodiesel blends was higher. The emission of the pollutants such as carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen was reduced due to the addition of biodiesel and hydrogen blends to the diesel. Considering the findings, the blend with 10 % biodiesel with 10 L/min hydrogen can be a potential alternative for diesel.
The influence of hydrogen on the diesel engine has been examined in this study. In addition, the impact of MgO nanoparticles was also analysed by conducting a series of tests on samples such as Diesel (100 % diesel), DN (Diesel-50 ppm MgO), H1N (10 % Hydrogen-50 ppm MgO) and H2N (20 % Hydrogen-50 ppm MgO). Hydrogen was injected through intake manifold at the volume of 10 % and 20 %. Nanoparticles were dispersed using the ultrasonication techniques to accrue stable suspension. The experiments were conducted between 6 N-m to 24 N-m loads on a four-stroke single cylinder engine. The parameters such as brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and heat release rate (HRR) were assessed. In addition to the performance and combustion, the environmental impact of the test blends was also analysed by examining the exhaust with a gas analyser. From the series of tests, it was evident that hydrogen enrichment in the test blends reported lower levels of emissions compared to neat diesel. The formation of the hydrocarbons (HC), nitrogen of oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2) was reduced due to the drop in the carbon atoms and enriched oxygen content in the combustion chamber. With regard to the performance, the hydrogen enriched nanoparticle blends reported peak BTE (37 %) and HRR (75 J/deg) than the other test blends. By assessing all the results, the addition of hydrogen is a potential option to reduce the environmental impact created by the fossil fuel without forfeiting the engine efficiency.
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major eternal blindness in aged people. In this manuscript, Auto-Metric Graph Neural Network (AGNN) optimized with Capuchin search optimization algorithm is proposed for coinciding DR and DME grading (AGNN-CSO-DR-DME). The novelty of this work is to identify the Diabetic retinopathy and diabetic macular edema grading at initial stage with higher accuracy by decreasing the error rate and computation time. Initially, input image is taken from two public benchmark datasets that is ISBI 2018 imbalanced diabetic retinopathy grading dataset and Messidor dataset. Then, the input fundus image is pre-processed by APPDRC filtering method removes noise in input images. Also, the pre-processed images are given to the Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. The extracted features of the DR and DME are fed to AGNN for classifying the grading of both DR and DME diseases. Generally, AGNN not reveal any adoption of optimization methods compute optimum parameters for assuring correct grading of both DR and DME diseases. Thus, CSOA is used for optimizing the AGNN weight parameters. The proposed method is carried out in python, its efficiency is assessed under performances metrics, such as f-measure, execution time and accuracy. The proposed method attains higher accuracy in ISBI 2018 IDRiD dataset 99.57 %, 97.28 %, and 96.34 %, compared with existing methods, like CANet-DR-DME, HDLCNN-MGMO-DR-DME, ANN-DR-DME and 91.17 %, 96.52 % and 97.36 %higher accuracy in Messidor dataset compared with existing methods, like CANet-DR-DME, TCNN-DR-DME, and 2-d-FBSE-FAWT-DR-DME.
Hydrogen is a growing alternative for fossil fuels that may be used to combat the energy shortfall that exists in a variety of industries, most notably the transportation and power generation industries. In this research work, the utilization of solar energy for the generation of electricity and production of hydrogen are thoroughly covered. A hybrid photovoltaic thermal system (PVT) has been used to generate the hydrogen via electrolysis process. To enhance the thermal efficiency of the PVT, graphene oxide nanofluids have been utilized. Graphene oxide nanofluids dispersed at the mass flow rates, such as 0.8 g/s, 1.0 g/s, and 1.2 g/s using sonication technique. A series of tests conducted between 9.00 A.M. to 4.00 P.M. to determine the parameters such as cell temperature, electrical efficiency, thermal efficiency and hydrogen mass flow rate. The procured results of the PVT carried out with the utilization of air and water as coolants were compared with PVT with nanofluids. From the findings it is evident that the performance of the system was significantly enhanced by the utilization of nanofluids at the optimized concentration compared to conventional water and air. With regard to the nanofluids mass flow rate, concentration of 1.2 g/s reported higher electrical (8.6%) and thermal efficiency (33.3%) compared to water. Added to above, there is a profound increase in the mass flow rate of hydrogen that has been observed at 1.2 g/s.
Extensive use of fossil fuels is the main cause for global warming. Burning of fossil fuels increases the air pollution which leads to adverse human health effects. Biodiesel is one the promising source of the energy to replace fossil fuel. The current study focused on one of the most sustainable microalgae biodiesel blends in the diesel engine. Further, the nanoparticles such as TiO2 and Ce2O3 were sonicated with the blends at the rate of 50 ppm to increase the brake thermal efficiency with least production of the pollutants. In addition to above, the hydrogen is also used as the secondary fuel to enhance the performance and combustion characteristics of the spirulina biodiesel. The constant hydrogen flow rate of 10 L/min was maintained throughout the study. Compared to the diesel fuel, biodiesel blends reported higher BTE due to the oxygenated additives and hydrogen addition. The maximum thermal efficiency for blend B30TH was found to be 29.5 % and minimum specific fuel consumption has been obtained for B30CH at maximum brake power conditions. In all test conditions, the biodiesel blends with hydrogen reported higher in-cylinder pressure and heat release rate. With regard to the emission, adding the biodiesel blends increases the combustion rates which leads to the reduction of accumulation of pollutants such as carbon monoxide, carbon dioxide, hydrocarbons, nitrogen of oxides and smoke. Among the various blends B20CH reported a massive reduction in the emission than B20TH.
The devices of mobile like smartphones are getting utilized by millions of users all over the globe. This makes chance in styling mobile image applications among many imaging applications like health care which have drawn immense attention in recent times. In the existing design where the detecting system is enhanced to execute the resource-constrained with the smartphone, our system localizes the skin texture by binding lightweight techniques to detect the affected areas by using K-means segmenting method which is a fast segmentation approach. In the proposed system, statistical confidence intervals based segmentation model is implied; from this time forward, the grouping is additionally done by resilient neural network (RNN) to help the norm of arrangement. A morph legitimate review is been created here for profound comprehension of the image highlights and so forth.
Emission Pongamia methyl ester Heat release rate A B S T R A C T In this work, SICI mode (Split Injection Compression Ignition) was tested, using diesel as a split injected fuel along with the diesel and biodiesel main injection with various split injected ratios of 0.20, 0.40 and 0.60. Re-circulated Exhaust Gas has been used to analyse the variation in performance and emission, with the exhaust gas rate was varied from 10% to 30% to control nitrogen di oxide and nitrous oxide emissions. The experimental results compared with conventional diesel injection was done in the main chamber. It has been established that hydrocarbons, soot emissions, and carbon monoxide increase as per the experiments carried out on a CIDI engine. With up to 20% recirculated exhaust gas, soot emission decreases and increases when recirculated exhaust gas was increased beyond 20%. So, 20% recirculated exhaust gas would be the optimum use for SICI mode (Split Injection Compression Ignition) with a Split Injected ratio of 0.20%. Compared to CIDI mode, brake thermal efficiency has been slightly decreased. Consequential reduction in NOx can be achieved with the SICI combustion mode due to the lean operation.
If the blood circulation of the heart is not adequate then it causes arrhythmias and Congestive Heart Failure (CHF) which requires immediate medical attention or else it leads to the loss of one's life. An Electrocardiogram (ECG) is a golden standard to diagnose the fatal complications in the heart caused by arrhythmias and it comprises a massive information related to the heartbeat rhythm. The main challenge focused in this paper is to extract the crucial information present in the ECG signal by visual analysis and classify the different abnormalities exhibited in the ECG signal. This paper presents a Honey Badger Algorithm optimized Faster Region-based Convolutional Neural Network (HBA-FRCNN) for CHF prediction with higher diagnostic accuracy. The noisy input ECG signals such as muscle contraction, electrode touch noise, and different noise artifacts are preprocessed using the Delayed Normalized Least Mean Square (DNLMS). The electrocardiographic complex (QRS complex) consisting of the Q, R, and S waves are extracted using the Discrete Cosine Transform (DCT) and fast Fourier transforms (FFT). The target detection box and the anchor parameter for the FRCNN model are tuned using the HBA algorithm to overcome the missed target detection, overfitting, and computational cost. The ECG signals for this study were obtained from Beth Israel Deaconess Medical Center (BIDMC) Congestive Heart Failure Database and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Normal Sinus Rhythm Database. The proposed methodology offers an accuracy, positive predictive value, sensitivity, and specificity score of 98.65%, 97.81%, 98.5%, and 98.2% respectively when evaluated with the ECG signals of the two datasets. For the Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR) classes present in the MIT-BIH dataset, the proposed model offers an accuracy of 99%, 100%, and 98% respectively and for the classes such as CHF severe and CHF normal in the BIDMC dataset, it offers an accuracy of 98% and 97%. The study mainly demonstrates the effectiveness of the FRCNN technique in predicting arrhythmias and CHF in patients by taking the increased number of features in the ECG signal. It also serves as a promising solution for physicians for long-time surveillance of patients prone to CHF with abnormal heartbeat rhythm.
In this study, nano zinc oxide particles with Tungsto phosphoric acid is used to catalyse the trans-esterification of used cooking oil biodiesel. The trans-esterification reaction parameters were optimized based on the trials. After adjusting the reaction parameters, an optimal set of parameters was found, with a maximum conversion rate of 94 % of waste cooking oil to biodiesel. For engine testing and analysis, synthetic biodiesel is employed as a blend of B100, B20 and B10. The susceptibility to bio-deterioration was assessed by carrying out the studies on their storage stability. The fuel properties for prepared fuel blends are tested within the ASTM standards. XRD profile of the prepared additives shown the formation of ZnO Nano particles is evident. Increase in biodiesel concentration in the blend has increased the peroxide values. Nano zinc oxide particles with Tungsto phosphoric acid catalyzed biodiesel blends lowered the peroxide values hence increase in the stability.
A circular bioeconomy is concerned with environmental sustainability and waste valorization. Utilization of leftovers and trashes while elevating the value of biomass through the cascade is receiving greater attention in recent years. Biomass is expected to play a significant role in fixing global alternative fuel ambitions. Azolla, a type of macroalgae which grows very quickly, is employed to develop a self-sustaining Azolla biorefinery model. Tannery effluents are treated using the phytoremediation approach, and the algal oil extracted from the cultivated Azolla is employed to produce bio-diesel as an alternative energy source. Biodiesel is produced via the transesterification technique. N2 sorption isotherms, scanning electron microscopy (SEM), FT-IR, and X-ray diffraction studies are used to evaluate the catalyst. The IR spectral analysis with the absorption bands corresponding to alkyl and ester stretching and bending modes of vibrations confirm biodiesel production. The reaction mechanism involved in the conversion process is proposed, and the process parameters such as the reaction temperature, oil-to-methanol molar ratio, and catalyst quantity are optimized. The results show that a catalyst loading of 2 % (by weight) and a molar ratio of 1:9 produce a high biodiesel yield with GO as the catalyst at temperatures between 60 and 65 °C. Its ability to catalyze allowed for numerous cycles of repetitive use. The results show that employing GO as a catalyst to trans esterify Azolla oil to biodiesel is cost-effective and reliable.
The present study used Zinc oxide nanoparticles mixed with Tungsto phosphoric acid (TPA) as a catalyst for the preparation of biodiesel from waste cooking oil. 10 wt% of Zinc oxide nanoparticles with 90 wt% of TPA is used in this study. The transesterification reaction parameters were optimized based on the trials performed. The optimum reaction parameters are 1:6 methanol to oil ratio at 60 °C to 65 °C and catalyst addition of 4 wt%, which gave a maximum ester conversion of 94 %. The properties of the synthesized biodiesel were compared to the ASTM standards and most of the properties met the specifications and requirements. Synthesized biodiesel is used as a blend of B10, B20 and B100 for engine testing and analysis. The performance characteristics show that the solid acid catalyst blends of transesterified biodiesel have performed better than any other fuel. Carbon emissions are low compared to diesel, the CO and HC emission of diesel is very high. In contrast to this result the NOx emissions are high for biodiesel blends.
Hydrogen as the secondary fuel in the existing diesel engines may improvise the performance and emission characteristics. Significant research has been conducted recently on hydrogen as the auxiliary fuel. In this current study, the diesel engine is tested with the hydrogen blends. The hydrogen is mixed with the diesel in the fractions of 10%, 20% and 30%, tested by varying the engine speeds and engine loads. Trail runs were made on the multicylinder water-cooled diesel engine to determine the brake thermal efficiency and the brake specific fuel consumption. In addition to the above, the emissions of pollutants such as NOx, HC and CO were also determined. The results releveled that addition of the hydrogen amplified the performance of the engine by reporting superior BTE and reduced fuel consumptions rates. Further, the emissions of the NOx and HC were dropped significantly as the concentration of the hydrogen in the diesel was increased. Based on the recorded value 30% hydrogen blends reported the maximum BP and BTE of 7.65 kW and 49.2%. With regard to the emissions compared to diesel, hydrogen addition reduces the NOx by 2.8%, 4.5% and 15% for the H10, H20 and H30 respectively. Based on the series of evidence it is evident that hydrogen addition to the CI engine can be the substantial substitute for fossil fuels.
A great deal of research indicates that the duality of agricultural production can be reduced because of various factors. Plant diseases are among the most critical aspects of this category. As a result, reducing plant diseases allows for significant improvements in quality. The article uses Grape Leaf Disease Detection Technique (GLDDT) with Faster Region based Convolutional Neural Network (FRCNN) - GLDDT-FRCNN techniques to automatically diagnose plant diseases. Once trained, the software can diagnose plant leaf disease without requiring any experimentation. The primary focus of this research is grape leaf disease. The basic technique manipulates H & colour histograms 25 channels 24. Excluding the final 26 phases, where the person decides which channel (H or a) offers the best separation, the algorithm method is fully automated. A GLDDT is also proposed in this article, which uses two-pronged processes for the evaluation, recognition and categorization of traits. The analysis process, testing on a benchmark set of data reveals that the disease diagnosis system might be a better fit than existing methods because it recognizes and identifies infected/diseased areas. The researchers achieved a precision rate of 99.93% for the detection of Isariopsis, black rot and Esca using the proposed disease detection method.
Biodiesel emits lesser harmful pollutant emissions than renewable and biodegradable ones compared diesel. Research confirms that edible products and crops are the major sources of biofuel production. Excessive usage of these crops leads to higher production costs, economic imbalance, and depletion of food supply. Biofuel production from inedible sources shall lower the drawbacks of edible products and crops. Inedible feedstocks are the sustainable source of biofuel production as they are mostly grown on waste/abandoned land, produce similar or higher yields than edible feedstocks, and are fairly cost-effective. Hence this present work reviews the challenges and possibilities of employing inedible oil and products as a potential feedstock for biofuel production. Salient features of inedible oil such as production technologies, cost and benefits, fatty acid and physicochemical properties and oil extraction technology are reviewed from the latest literature. The outcome of this study suggests that there is a huge prospect of utilizing inedible oil as a reliable feedstock for biofuel generation. Among various production processes, scCO2 extraction technology proved to reduce inedible oil's moisture by 70% and FFA content by 62%, with a higher conversion rate of about 97%, as methanol in supercritical conditions has lesser interaction with the FFA of inedible oil. Inedible feedstocks are effective, non-toxic and safe in biofuel production. However, there exists a challenge in restricting its development in large-scale commercialization.
The vital food crop in the agriculture field is rice, but rice growing is impacted by numerous maladies. The Bacterial Leaf Blight (BLB) disease influences rice standards. Prevailing research practices have less accuracy and could not surmount the noise of the images. This paper proposed Moore-Penrose pseudo-inverse Weight-related Deep Convolutional Neural Network (MPW-DCNN) overcomes such complications for the BLB disease identification system. The proposed method encompasses six phases. First, the Image Acquisition (IA) procedure is accomplished, next the pre-processing is carried out, in which the noise of the input rice leaf image is mitigated using the Hybrid Gaussian-Weiner (HGW) filter, and the image pixel is normalized by utilizing min-max normalization. Then, the segmentation of the input image is done by Improved Fuzzy C-Means (IFCM). Next, the features, such as entropy, energy, correlation, contrast, homogeneity, colour histogram, and Scale-Invariant Feature Transforms (SIFT) are extracted from the segmented image. Then, the efficient features are chosen by applying the Exhaustiveness and Brownian Motion-related Elephant Herding Optimization (EBM-EHO) algorithm. Then, these selected features are fed to the MPW-DCNN classifier, which categorizes the image as 'BLB malady' or ‘normal’ or else 'chances being influenced by other maladies'. Lastly, the proposed MPW-DCNN performance is equated with the prevailing classification algorithm, and better accuracy is proffered by the proposed work than the prevalent algorithms. The accuracy of the implemented approach is 2.36, 3.08, 4.62, 5.13, and 6.15% improved than the existing methods, such as Support Vector Machine (SVM) + Deep features, AlexNet, Deep Convolutional Neural Network (DCNN), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), respectively.
Silver nanoparticles (Ag NPs) have gained considerable attention for use in various applications. This work presents a novel technique for the preparation of homogeniouse Ag NPs and fabricated most efficient super-capacitor (SC) and ultrafast photocatalytic degradation of textile dyes. The particles size was controlled by using sodium borohydride (NaBH 4) as a reducing agent and polyvinylpyrrolidone (PVP) as a stabilizing agent. As a result, highly homogeneous Ag nanoparticles were prepared by the facile chemical method. The crystalline and morphological information and particle size (~10-30 nm) of Ag NPs are determined via XRD, TEM and SEM analyses. The electrochemical performance of the Ag NPs exhibits pseudocapacitive behavior and a high specific capacitance of 396 F/g, achieving 89% of capacitance retention over 3,000 cycles. In addition, the Ag NPs demonstrate excellent performance for photocatalytic degradation of bromophenol blue (BP: ~88% efficiency) and fast green (FG: ~89% efficiency) dyes, respectively. Results clearly show that Ag NPs can be used for both energy storage as well as photocatalytic applications.
The genus Gracilaria is an economically important group of seaweeds as several species are utilized for various products such as agar, used in medicines, human diets, and poultry feed. Hence, it is imperative to understand their response to predicted ocean acidification conditions. In the present work, we have evaluated the response of Gracilaria foliifera and Gracilaria debilis to carbon dioxide (pCO2) induced seawater acidification (pH 7.7) for two weeks in a controlled laboratory conditions. As a response variable, we have measured growth, productivity, redox state, primary and secondary metabolites, and mineral compositions. We found a general increase in the daily growth rate, primary productivity, and tissue chemical composition (such as pigments, soluble and insoluble sugars, amino acids, and fatty acids), but a decrease in the mineral contents under the acidified condition. Under acidification, there was a decrease in malondialdehyde. However, there were no significant changes in the total antioxidant capacity and a majority of enzymatic and non-enzymatic antioxidants, except for an increase in tocopherols, ascorbate and glutathione-s-transferase in G. foliifera. These results indicate that elevated pCO2 will benefit the growth of the studied species. No sign of oxidative stress markers indicating the acclimatory response of these seaweeds towards lowered pH conditions. Besides, we also found increased antimicrobial activities of acidified samples against several of the tested food pathogens. Based on these observations, we suggest that Gracilaria spp. will be benefitted from the predicted future acidified ocean.
The aim of this investigation is to produce and characterize biosurfactant from Streptomyces sp. HRB1 and to evaluate its biomedical and bioremediation potential. Biosurfactant producing property of Streptomyces sp. HRB1 isolated from petroleum contaminated soil was confirmed by hemolytic and oil spread assays. Based on the results of FT‐IR spectral and GC–MS analysis, the biosurfactant was confirmed as glycolipid type. Biosurfactant from Streptomyces sp. HRB1 exhibited 71% inhibition against Pseudomonas aeruginosa biofilm formation, 77.33% quorum sensing inhibition property against Chromobacterium violeceum MTCC 2656, more than 80% inhibition in antioxidant assays namely, DPPH, ABTS, and metal chelation, promising anti‐proliferative activity against leukemia and myeloma cells with low IC50 values, 96% decolorization of malachite green within 48 h of reaction time, and minimal toxicity against normal cell lines in dose‐dependent manner. The taxonomic position of the potential strain HRB1 was further confirmed as Streptomyces enissocaesilis HRB1 based on their phenotypic and molecular characteristics. To conclude, Streptomyces enissocaesilis HRB1 isolated from petroleum‐contaminated soil is a promising source for low‐cost production of glycolipid biosurfactant with potential biomedical and environmental applications such as antiphytofungal, antibiofilm, anti‐quorum sensing, antioxidant, anticancer, and dye degradation properties.
This research describes the impact of hydraulic retention time (HRT) and bacterial mass concentration (MLSS) in sequencing batch reactor process (SBRP) and batch reactor process (BRP) for the removal of pollutants from the dairy wastewater. The operational conditions used were variable volume exchange ratio up to 75%, hydraulic retention time (4–8 h), and initial MLSS concentration up to 5150 mg/L. It was found that the SBRP increased the removal efficiencies of biological oxygen demand (BOD5), chemical oxygen demand (COD), and total suspended solids (TSS). The higher percent removals of BOD, COD, TP, TN, and SS were obtained in the bacterial mass concentration (MLSS) of 2100 mg/L which were in the order of 88, 96, 82, 92, and 75% for SBRP and were in the order of 84, 93, 70, 91, and 70% for BRP, respectively. The optimum level of MLSS was found to be 2100 mg/L at the retention time of 6 h for both SBRP and BRP. Compared to the conventional process, the SBR reduced the aeration step and achieved higher removal efficiency. Moreover, it reduced the excess sludge by about 25%. Interestingly, the results revealed that lower MLSS brought about better removal efficiencies for both SBRP and BRP.
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1,025 members
Inbakandan Dhinakarasamy
  • Centre for Ocean Research (DST-FIST Sponsored Centre)
Brijitta Joseph
  • Centre for Nanoscience and Nanotechnology
Alex Anand
  • Department of BioInformatics & The Centre for Molecular Data Science and Systems Biology
Anima Nanda
  • Department of BioMedical Engineering
Masilamani Selvam
  • Department of BioTechnology
Chennai, India
Head of institution