Chemical Reactive Separations In Energy And Environmental Processes

The main objective of this study was to investigate the application of chemical reactive separation (reactive separation, advanced oxidation) in bio-oil upgradation and air pollution control. Aspen simulation for reactive distillation of pyrolysis bio-oil for the reactive distillation with n-butanol is carried out using RADFRAC module and UNIQUAC property method by minimizing Gibbs free energy. The binary and ternary interaction and chemical and phase equilibrium (CPE) between the components were studied to determine the azeotropes and homogeneous region of mixture. The conversions for the esterification reactions were found to be 70-90% for various simulated bio-oils, and water was separated from the ester products. All the distillation column parameters such as condenser and reboiler heat duty, number of stages, reflux ratio with different inlet conditions etc., were studied to develop a completely energy cost minimized RD unit. The removal of nitric oxide (NO) from simulated flue gas in a bubble column reactor at atmospheric pressure is investigated using combined aqueous persulfate (Na2S2O8) and ferrous ethylenediamine tetraacetate (FeII-EDTA) systems. 0.1 M persulfate solution with 0.01 M Fe2+ was used as the optimum amount of absorption-oxidation reagents and molar ratio of Fe2+ and EDTA was found to be 1:1 for maximum NO conversion. NO absorption experiments were carried out at 23-70 0C, and comparative NO concentration profiles and corresponding conversion (%) plotted. The results show significant improvement in NO removal compared with thermally and Fe2+ activated persulfate systems (at lower temperature 25-30% and at higher temperature 5-10%) and almost 100% NO conversion can be achieved at 70 0C. The detailed chemistry and kinetics are discussed. Iron species’ (Fe2+, Fe3+ and FeII-EDTA) concentrations were measured spectrophotometrically to understand the simultaneous and synergistic relationship between persulfate and FeII-EDTA in NO removal

Chemical Reactive Separations In Energy And Environmental Processes

The main objective of this study was to investigate the application of chemical reactive separation (reactive separation, advanced oxidation) in bio-oil upgradation and air pollution control. Aspen simulation for reactive distillation of pyrolysis bio-oil for the reactive distillation with n-butanol is carried out using RADFRAC module and UNIQUAC property method by minimizing Gibbs free energy. The binary and ternary interaction and chemical and phase equilibrium (CPE) between the components were studied to determine the azeotropes and homogeneous region of mixture. The conversions for the esterification reactions were found to be 70-90% for various simulated bio-oils, and water was separated from the ester products. All the distillation column parameters such as condenser and reboiler heat duty, number of stages, reflux ratio with different inlet conditions etc., were studied to develop a completely energy cost minimized RD unit. The removal of nitric oxide (NO) from simulated flue gas in a bubble column reactor at atmospheric pressure is investigated using combined aqueous persulfate (Na2S2O8) and ferrous ethylenediamine tetraacetate (FeII-EDTA) systems. 0.1 M persulfate solution with 0.01 M Fe2+ was used as the optimum amount of absorption-oxidation reagents and molar ratio of Fe2+ and EDTA was found to be 1:1 for maximum NO conversion. NO absorption experiments were carried out at 23-70 0C, and comparative NO concentration profiles and corresponding conversion (%) plotted. The results show significant improvement in NO removal compared with thermally and Fe2+ activated persulfate systems (at lower temperature 25-30% and at higher temperature 5-10%) and almost 100% NO conversion can be achieved at 70 0C. The detailed chemistry and kinetics are discussed. Iron species’ (Fe2+, Fe3+ and FeII-EDTA) concentrations were measured spectrophotometrically to understand the simultaneous and synergistic relationship between persulfate and FeII-EDTA in NO removal

NANOHARVESTING AND DELIVERY OF BIOACTIVE MATERIALS USING ENGINEERED SILICA NANOPARTICLES

Mesoporous silica nanoparticles (MSNPs) possess large surface areas and ample pore space that can be readily modified with specific functional groups for targeted binding of bioactive materials to be transported through cellular barriers. Engineered silica nanoparticles (ESNP) have been used extensively to deliver bio-active materials to target intracellular sites, including as non-viral vectors for nucleic acid (DNA/RNA) delivery such as for siRNA induced interference. The reverse process guided by the same principles is called “nanoharvesting”, where valuable biomolecules are carried out and separated from living and functioning organisms using nano-carriers. This dissertation focuses on ESNP design principles for both applications. To investigate the bioactive materials loading, the adsorption of antioxidant flavonoids was investigated on titania (TiO2) functionalized MSNPs (mean particle diameter ~170 nm). The amount of flavonoid adsorbed onto particle surface was a strong function of active group (TiO2) grafting and a 100-fold increase in the adsorption capacity was observed relative to nonporous particles with similar TiO2 coverage. Active flavonoid was released from the particle surface using citric acid-mediated ligand displacement. Afterwards, nanoharvesting of flavonoids from plant hairy roots is demonstrated using ESNP in which TiO2 and amine functional groups are used as specific binding sites and positive surface charge source, respectively. Isolation of therapeutics was confirmed by increased pharmacological activity of the particles. After nanoharvesting, roots are found to be viable and capable of therapeutic re-synthesis. In order to identify the underlying nanoparticle uptake mechanism, TiO2 content of the plant roots was quantified with exposure to nanoparticles. Temperature (4 or 23 °C) dependent particle recovery, in which time dependent release of ESNP from plant cells showed a similar trend, indicated an energy independent process (passive transport). To achieve the selective separation and nanoharvesting of higher value therapeutics, amine functionalized MSNPs were conjugated with specific functional oligopeptides using a hetero-bifunctional linker. Fluorescence spectroscopy was used to confirm and determine binding efficiency using fluorescently attached peptides. Binding of targeted compounds was confirmed by solution depletion using liquid chromatography–mass spectrometry. The conjugation strategy is generalizable and applicable to harvest the pharmaceuticals produced in plants by selecting a specific oligopeptide that mimic the appropriate binding sites. For related gene delivery applications, the thermodynamic interaction of amine functionalized MSNPs with double-stranded (ds) RNA was investigated by isothermal titration calorimetry (ITC). The heat of interaction was significantly different for particles with larger pore size (3.2 and 7.6 nm) compared to that of small pore particles (1.6 nm) and nonporous particles. Interaction of dsRNA also depended on molecular length, as longer RNA (282 base pair) was unable to load into 1.6 nm particles, consistent with previous confocal microscopy observations. Calculated thermodynamic parameters (enthalpy, entropy and free energy of interaction) are essential to design pore size dependent dsRNA loading, protection and delivery using MSNP carriers. While seemingly diverse, the highly tunable nature of ESNP and their interactions with cells are broadly applicable, and enable facile nano-harvesting and delivery based on a continuous uptake-expulsion mechanism

Evaluating Factors Affecting EFL Learners’ Writing at Undergraduate Level: A Survey

Writing is an important language skill that benefits a language learner in many ways from developing vocabulary knowledge to enhancing overall language competence Unfortunately EFL learners encounter many difficulties during writing that prevents them from developing their writing skills and eventually negatively affect their overall language learning process The goal of the present study was to assess the factors that affect EFL learners writing at the undergraduate level Using a Likert-scale survey sheet the study surveyed of 255 undergraduate students at Bangladesh Agricultural University and found that many factors are responsible for EFL learners poor writing at the undergraduate level Some factors very intensely affect the learners writing while some affect moderately and some have comparatively less effect By evaluating the factors that affect the learners writing the study basically attempted to make the learners aware of those factors they face in writing so that they can overcome them improve their writing skills and become proficient writer

The role of the family in socializing the autistic children: a study in Bangladesh

Autism Spectrum Disorders are increasingly known in developing countries like Bangladesh. The children with autism exhibit significant deficits in social communication, including delayed language development and imitation skills. This disorder is different from other disorders and its characteristics and functions are also different. Families having children with autism spectrum disorder play important role in their socialization and face many challenges as the disorder is associated with disruptive antisocial behavior. The main objective of this research is to know the role of family in socializing autistic children. This research was conducted though survey method by using a semi-structured questionnaire. The data of this study was collected from 78 parents having children with autism spectrum disorder. In our country autism newly introduced and research on autism especially role of the family of the autistic children in socialization are very few. So, this study discusses the role of family in socializing autistic children

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Isolated liver tuberculosis

Isolated liver tuberculosis is still considered a rare condition and its atypical clinical presentation challenges the clinical acumen of the treating physician. There is difficulty in reaching the correct preoperative diagnosis of nodular hepatic tuberculosis that presents as a space-occupying lesion. It is usually unsuspected and confused with primary or metastatic carcinoma of the liver. In this report, we describe a rare case of isolated liver tuberculosis