Showing papers by "Chittagong University of Engineering & Technology published in 2021"

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

Abstract: Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

A review of thermal management for Li-ion batteries: Prospects, challenges, and issues

Abstract: Li-ion batteries are essential component in the current generation of electric vehicles. However, further pushing electric vehicles are concerned with battery life. Since the temperature dictates battery lifetime, it is crucial to manage the heat and keep the temperature at an acceptable range within the battery pack. The benefit of a cooling system is to prevent the premature degradation of battery life. This paper provides a critical review of the so far thermal management strategy dealing with temperature within the cells, module, and packs. This paper reviews the advantages and disadvantages of state of the art (traditional) thermal cooling system. In this paper, we have reviewed separately cell, module, and pack level cooling system. The battery thermal modeling techniques and cooling system design challenges are also reviewed. This paper also reviews the future cooling system for future vehicles with rising fast charge rate and these techniques can improve the limitations of the traditional cooling system. This paper also suggests the best suitable and economically viable technology for the upcoming EVs issues.

Design and analysis of a single elliptical channel photonic crystal fiber sensor for potential malaria detection

Abstract: Malaria is a mosquito-borne disease caused by unicellular hemoparasites of the genus Plasmodium that results in the death of over one million people worldwide every year. Early diagnosis plays a key role in the treatment of infected patients and can reduce the mortality rate. This work proposes a simple designed photonic crystal fiber (PCF) sensor for detecting malarial infection using the refractive index (RI) of red blood cells (RBCs). The initial structure of the PCF sensor consists of double loops of circular air holes arranged in a hexagonal formation. A horizontal elliptical channel in the center of the fiber contains the RBCs sample. The sensor’s response was observed from the shift of the transmission spectra due to change in the RI of RBCs during different life stages of the parasite. Model parameters (transmission length, pitch, air hole diameter, and eccentricity of the elliptical channel) of the proposed sensor were optimized to obtain the best possible response. The highest spectral sensitivities were obtained about 11,428.57 nm/RIU, 9473.68 nm/RIU, and 9655.17 nm/RIU for the ring, trophozoite, and schizont phases of the parasite, respectively. Due to its high sensitivity, easy identification capability, and short transmission length, this sensor can be utilized as a cost-effective and useful device for malaria diagnosis.

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Abstract: Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh

Abstract: Satellite images have been used extensively to identify the land use/land cover (LULC) changes in Bangladesh. However, no study has been conducted to classify LULC changes in the Dhaka Metropolitan Development Plan (DMDP) area using high-resolution commercial satellite images. The study aimed to simulate future LULC scenarios using RapidEye commercial images in the fastest-growing DMDP area. Support Vector Machine algorithm was applied to estimate the LULC scenarios for years 2012, 2015, and 2018. Cellular Automata machine learning algorithm was used to simulate the future LULC scenarios for 2025. The study result revealed that a significant net increase in the urban areas (UAs) by 15.52%, a remarkable decrease in sparse vegetation (SV) by 12.48%, and a transformation of 17.83% green cover (SV and dense vegetation) areas by 14.95% (8.9%/year) UAs were found from 2012 to 2018. Prediction results demonstrated that UAs would likely to be expanded by 53% and SV will be reduced by 13% (28% was in 2012) in 2025. The outcomes of this study will help the city authorities of DMDP in preparing a comprehensive micro-level urban development plan, where planned infrastructural development and supervision, land use planning, natural resource conservation, and environmental sustainability will be ensured.

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Abstract: Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today’s various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on “AI-driven Cybersecurity” that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper’s ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Abstract: Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Abstract: The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Physical properties of new MAX phase borides M2SB (M = Zr, Hf and Nb) in comparison with conventional MAX phase carbides M2SC (M = Zr, Hf and Nb): Comprehensive insights

Abstract: In this article, a detailed study of the recently synthesized MAX phase borides M2SB (M = Zr, Hf and Nb) has been performed via first principles technique. Investigation of mechanical properties, elastic anisotropy, optical properties, dynamical stability and thermal properties are considered for the first time. The estimated values of stiffness constants and elastic moduli are found in good agreement with available results. The Vickers hardness is also calculated using Mulliken population analysis. The electronic density of states and charge density mapping are used to explain the variation of stiffness constants, elastic moduli and hardness parameters among the studied ternary borides. The Nb2SB compound is found to show the best combination of mechanical properties. Mixture of covalent and ionic bonding within these borides is explained using Mulliken population analysis. The direction dependent values of Young's modulus, compressibility, shear modulus and Poisson's ratio are visualized by 2D and 3D representations and different anisotropic factors are calculated. The important optical constants are calculated and analyzed. The metallic nature of the studied borides is confirmed from the density of states (DOS) and optical properties. The reflectivity spectra reveal the potential use of Zr2SB as coating materials to diminish solar heating. The studied borides are dynamically stable as confirmed from the phonon dispersion curves. The characteristic thermodynamic properties have also been calculated and analyzed. The physical properties of corresponding 211 MAX phase carbides are also calculated for comparison with those of the titled ternary borides.

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Abstract: Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today’s smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today’s diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area

Abstract: Climate change is occurring because of an increase in greenhouse gases such as carbon dioxide, methane, and others, which act as a partial blanket for the planet and store solar energy radiation, resulting an increase in land surface temperature (LST). Cities that are already suffering from the urban heat island (UHI) effect , which will withstand the worst of these more extreme heat events. The extent of the impermeable layer and changes in LST are inextricably linked to the severity and commencement of UHI events, which can be measured using the urban thermal field variance index (UTFVI). Land use/Land cover (LULC) change was assessed using support vector machine (SVM) supervised classification, seasonal (summer and winter) LST, and UTFVI variations from Landsat 4–5 TM and Landsat 8 OLI satellite images for the years 2000, 2010, and 2020. Furthermore, in Dhaka, Bangladesh, the cellular automata-based artificial neural network (CA-ANN) algorithm was utilized to forecast LULC, seasonal LST and UTFVI for 2030. From 2000 to 2020, the results demonstrated a large net change in urban areas (+20.52%), whereas vegetation, bare land, and water bodies were all decreased with net changes of -5.72%, -11.19%, and -3.6%, respectively. According to projected LSTs, the net increase in summer and winter temperatures from 2020 to 2030 will be 13% and 20%, respectively, in the highest temperature group (greater than 35 °C). Furthermore, the projected UTFVI showed that in 2030, roughly 72% (up from 58% in 2020) and 69% (up from 47 percent% in 2020) of total area will be covered by stronger and strongest UTFVI zones. Correlation analysis was statistically significant (p value

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Abstract: Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today’s smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today’s diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks

Abstract: Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity The knowledge of artificial intelligence, particularly, the machine learning techniques can be used to tackle these issues However, the effectiveness of a learning-based security model may vary depending on the security features and the data characteristics In this paper, we present “CyberLearning”, a machine learning-based cybersecurity modeling with correlated-feature selection, and a comprehensive empirical analysis on the effectiveness of various machine learning based security models In our CyberLearning modeling, we take into account a binary classification model for detecting anomalies , and multi-class classification model for various types of cyber-attacks To build the security model, we first employ the popular ten machine learning classification techniques , such as naive Bayes, Logistic regression, Stochastic gradient descent , K-nearest neighbors, Support vector machine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient Boosting, as well as Linear discriminant analysis We then present the artificial neural network-based security model considering multiple hidden layers The effectiveness of these learning-based security models is examined by conducting a range of experiments utilizing the two most popular security datasets, UNSW-NB15 and NSL-KDD Overall, this paper aims to serve as a reference point for data-driven security modeling through our experimental analysis and findings in the context of cybersecurity

Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in Chattogram city: A remote sensing and statistical approach

Abstract: Land Cover (LC) is going through a dramatic change due to rapid urbanization, especially in urban areas. The impervious land covers (built-up areas) are replacing the natural land covers (vegetation and waterbody) rapidly, which significantly contributes to the increase of Land Surface Temperature (LST). Increased LST deteriorates the meteorological condition in urban areas and causes Surface Urban Heat Island (SUHI) effect. The vulnerability of the SUHI effect can be described quantitatively and qualitatively by Urban Thermal Field Variance Index (UTFVI) phenomenon. The study investigates the changing pattern of the SUHI and its magnitude with Landsat TM/OLI time-series satellite imageries from 1988 to 2018 and establishes the relationship between LC change and LST variations in Chattogram city. The relationships are defined by correlation analysis, cross-section profiles, and Simple Linear Regression Models (SLRM). The results suggest that vegetation cover, water body, and bare-soil have decreased by 2%, 7%, and 10%, respectively, where the built-up area has increased by 19% in the study area during the study period. The average LST of the study area has increased by approximately 10 °C in the last 30 years. Weightage of the SUHI affected area has also increased over time due to its direct linkage with LST. No area was affected by the SUHI phenomenon in 1988, which was found more than 35% in 2018. Only 1.69% area was found under the strongest UTFVI phenomenon in 1988, which increased significantly by 27.53% in 2018. In statistical analysis, regression models are used to define LC and LST relationship for every LC type. The LC and LST relationship analyses demonstrate a significant positive correlation with the built-up area while it is negative with vegetation, waterbody, and bare soil. The findings of this study will help city officials and policymakers to prepare a sustainable urban land development plan to minimize the negative consequences of unplanned urbanization and heat stress-related issues.

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Abstract: Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer’s opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, is growing enormously among the people. Reader’s opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial naive Bayes with unigram feature outperforms the other techniques with \(84\%\) accuracy on the test set.

A deep learning approach to predict autism spectrum disorder using multisite resting-state fmri

Abstract: Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Traveling wave with beta derivative spatial-temporal evolution for describing the nonlinear directional couplers with metamaterials via two distinct methods

Abstract: This work is reported the analytical solutions for describing the nonlinear directional couplers with metamaterials by including spatial–temporal fractional beta derivative evolution. The auxiliary ordinary differential equation method and the generalized Riccati method with the properties of beta derivative are implemented to secure such solutions. The solutions are obtained in the new forms by involving of some useful mathematical functions. The constraint conditions among the traveling wave parameters are evaluated. Some of the obtained solutions are presented graphically to illustrate the effectiveness of beta derivative parameter and mathematical techniques. It is investigated that the amplitudes of soliton are increased with the increase of fractional beta derivative parameter. The obtained results would be very useful to examine and understand the physical issues in nonlinear optics, especially in twin-core couplers with optical metamaterials.

Bengali text document categorization based on very deep convolution neural network

Abstract: In recent years, the amount of digital text contents or documents in the Bengali language has increased enormously on online platforms due to the effortless access of the Internet via electronic gadgets. As a result, an enormous amount of unstructured data is created that demands much time and effort to organize, search or manipulate. To manage such a massive number of documents effectively, an intelligent text document classification system is proposed in this paper. Intelligent classification of text document in a resource-constrained language (like Bengali) is challenging due to unavailability of linguistic resources, intelligent NLP tools, and larger text corpora. Moreover, Bengali texts are available in two morphological variants (i.e., Sadhu-bhasha and Cholito-bhasha) making the classification task more complicated. The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier. Due to the unavailability of standard corpus, this work develops a large Embedding Corpus (EC) containing 969 , 000 unlabelled texts and Bengali Text Classification Corpus (BDTC) containing 156 , 207 labelled documents arranged into 13 categories. Moreover, this work proposes the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali). Evaluation of 165 embedding models with intrinsic evaluators (semantic & syntactic similarity measures) shows that the GloVe model is more suitable (regarding Spearman & Pearson correlation) than other embeddings (Word2Vec, FastText, m-BERT) in Bengali text. Experimental results on the test dataset confirm that the proposed GloVe + VDCNN model outperformed (achieving the highest 96.96 % accuracy) the other classification models and existing methods to perform the Bengali text classification task.

A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

Abstract: Heart disease, alternatively known as cardiovascular disease, is the primary basis of death worldwide over the past few decades. To make an early diagnosis, a data-driven prediction model considering the associate risk factors in heart disease can play a significant role in healthcare domain. However, to build such an effective model based on machine learning techniques, the quality of the data, e.g., data without “anomalies” or outliers, is important. This research investigates anomaly detection in the healthcare domain to effectively predict heart disease using unsupervised K-means clustering algorithm. Our proposed model first determines an optimal value of K using the Silhouette method to form the clusters for finding the anomalies. After that, we eliminate the identified anomalies from the data and employ the five most popular machine learning classification techniques, such as K-nearest neighbor, random forest, support vector machine, naive Bayes, and logistic regression to build the resultant prediction model. The efficacy of the proposed methodology is justified using a standard heart disease dataset. We also take into account the data plotting to test the exactness of the detection of anomalies in our experimental analysis.

Printable Single-Unit-Cell-Thick Transparent Zinc-Doped Indium Oxides with Efficient Electron Transport Properties.

Abstract: Ultrathin transparent conductive oxides (TCOs) are emerging candidates for next-generation transparent electronics. Indium oxide (In2O3) incorporated with post-transition-metal ions (e.g., Sn) has been widely studied due to their excellent optical transparency and electrical conductivity. However, their electron transport properties are deteriorated at the ultrathin two-dimensional (2D) morphology compared to that of intrinsic In2O3. Here, we explore the domain of transition-metal dopants in ultrathin In2O3 with the thicknesses down to the single-unit-cell limit, which is realized in a large area using a low-temperature liquid metal printing technique. Zn dopant is selected as a representative to incorporate into the In2O3 rhombohedral crystal framework, which results in the gradual transition of the host to quasimetallic. While the optical transmittance is maintained above 98%, an electron field-effect mobility of up to 87 cm2 V-1 s-1 and a considerable sub-kΩ-1 cm-1 ranged electrical conductivity are achieved when the Zn doping level is optimized, which are in a combination significantly improved compared to those of reported ultrathin TCOs. This work presents various opportunities for developing high-performance flexible transparent electronics based on emerging ultrathin TCO candidates.

Heavy metal pollution status and health risk assessment vicinity to Barapukuria coal mine area of Bangladesh

Abstract: Study related to pollution and subsequence health risk assessment adjacent to coal mining region is indispensable since coal mining and combustion have adverse impacts on the soil, subsoil, and plants in the surrounding area with a threat to human health and ecosystem. This study assesses the heavy metal pollution in water and soil as well as uptake in several parts of paddy plants over the three seasons around the year in the vicinity of the Barapukuria coal mine in Bangladesh, with the ultimate goal of estimating human health risks from eating the rice grown in this area. Enrichment factor (EF), pollution load index (PLI), geo-accumulation index (Igeo) and biological accumulation factor (BAF) were estimated for quantifying the pollution status in soils and plants. Collected samples were prepared and analysed by using USEPA recommended methods. PLI showed that soils irrigated by coal mine water were highly polluted by Ni, Fe, and Cr metals. The Igeo indicated that the soil in the study area was moderately contaminated by Cu, Zn and Ni. Amongst all types of samples, soil was found with the highest pollution followed by roots, stems, and grains; however, bioaccumulation was not evident. In the three varieties of rice grains tested, Zn and Fe were present with the highest concentrations. Through the rice grain, people in the area are ingesting Fe in the highest amounts and Cr with the lowest. Even though the concentration of Cr is low it is still above the oral intake level as recommended by USEPA.

Newly synthesized MAX phase Zr2SeC: DFT insights into physical properties towards possible applications

Abstract: A DFT study of the synthesized MAX phase Zr2SeC has been carried out for the first time to explore its physical properties for possible applications in many sectors. The studied properties are compared with prior known MAX phase Zr2SC. The structural parameters (lattice constants, volume, and atomic positions) are observed to be consistent with earlier results. The band structure and density of states (DOS) are used to explore the metallic conductivity, anisotropic electrical conductivity, and the dominant role of Zr-d states to the electrical conductivity at the Fermi level. Analysis of the peaks in the DOS and charge density mapping (CDM) of Zr2SeC and Zr2SC revealed the possible variation of the mechanical properties and hardness among them. The mechanical stability has been checked using elastic constants. The values of the elastic constants, elastic moduli and hardness parameters of Zr2SeC are found to be lowered than those of Zr2SC. The anisotropic behavior of the mechanical properties has been studied and analyzed. Technologically important thermodynamic properties such as the thermal expansion coefficient (TEC), Debye temperature (ΘD), entropy (S), heat capacity at constant volume (Cv), Gruneisen parameter (γ) along with volume (V) and Gibbs free energy (G) are investigated as a function of both temperature (from 0 to 1600 K) and pressure (from 0 to 50 GPa). Besides, the ΘD, minimum thermal conductivity (Kmin), melting point (Tm), and γ have also been calculated at room temperature and found to be lowered for Zr2SeC compared to Zr2SC owing to their close relationship with the mechanical parameters. The value of the ΘD, Kmin, Tm, and TEC suggest Zr2SeC as a thermal barrier coating material. The optical properties such as dielectric constant (real and imaginary part), refractive index, extinction coefficient, absorption coefficient, photoconductivity, reflectivity, and loss function of Zr2SeC are computed and analyzed to reveal its possible applications.