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Exploring the effects of Mn substitution on structural, morphological and electrical properties of Co0.7Zn0.3Fe2O4 ceramics

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Ceramics International 2025年第15期51卷 19845-19859页

作者： Shah, Tuntun Dalai, Biswajit Mohanty, Hari Sankar Bhoi, Krishnamayee Sutar, Bijuni C. Pradhan, Dillip K. Department of Physics School of Sciences Gandhi Institute of Engineering and Technology University Odisha Gunupur765022 India Department of Physics KMBB College of Engineering & Technology Odisha Bhubaneswar752056 India Department of Physics and Astronomy National Institute of Technology Odisha Rourkela769008 India

This report examines the effects of manganese (Mn) substitution on structural, morphological and electrical properties of the sol-gel-fabricated zinc modified cobalt ferrite (Co0.7Zn0.3Fe2O4) ceramics. Co0.7Zn0.3Fe(2-y)MnyO4 (0.05 ≤ y ≤ 0.30) were synthesized employing sol-gel auto-combustion technique. X-ray diffraction (XRD) pattern shows formation of single-phase Co0.7Zn0.3Fe(2-y)MnyO4 (CZFMO) without emergence of any impurity phases. XRD patterns were refined by considering Fd-3m space group in a cubic spinel crystal structure which further confirms formation of single phase Co0.7Zn0.3Fe(2-y)MnyO4. The average particle size of Co0.7Zn0.3Fe(2-y)MnyO4 estimated using Williamson-Hall method, was found to increase from 31.52 nm to 96.55 nm with increasing Mn concentration. Field emission scanning electron microscopy (FESEM) micrographs reveal an even distribution of grain sizes along with existence of minimal amounts of pores across the sample microstructure. The elemental accuracy of these samples was confirmed by EDX characterization. The temperature dependent dielectric properties are studied over wide frequency ranges. Among the prepared compounds, maximum value of dielectric constant is obtained for Co0.7Zn0.3Fe1.75Mn0.25O4 composition. The dielectric relaxation behaviour of the non-Debye type was found using complex impedance and complex modulus spectroscopy methods. Nyquist plot is used to investigate the existence of grain and grain boundary involvement to impedance spectra in modified cobalt ferrite samples. Temperature-dependent of AC conductivity (σac) study also suggests the presence of negative temperature coefficient of resistance (NTCR) behaviour in Co0.7Zn0.3Fe(2-y)MnyO4 ceramics and follows overlapping large polaron tunnelling (OLPT) conduction mechanism. The Jonscher's power law provides a good fit for AC conductivity spectra of all the compounds. The total resistivity of the synthesized compounds is increased by increasing Mn content. The chosen s

关键词： Rietveld refinement

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Experimental investigation on mechanical properties of stir casted aluminum SiC metal matrix composites

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Materials Today: Proceedings 2023年 74卷 1023-1027页

作者： Mishra, Debashis Kumar Nanda, Alok Biju Patnaik Institute of IT and Management Studies Odisha Bhubaneswar 751024 India KMBB College of Engineering and Technology Odisha Bhubaneswar 752056 India

To enhance the mechanical and wear resistance properties trailed by reduction in total weight, ceramics are immersed with aluminum for the fabrication of Al-matrix composites (MMCs) for the wide structural applications in aerospace, automotive and marine industries. The intermixing and preparation of MMCs are quite difficult to produce or carry out. The considerably cost-effective process named stir casting process was used in this experimental investigation to produce Al-SiC MMCs by diffusing particulates of solid SiCs in variable percentages of 8 (480gms), 9 (520gms), and 10 (540gms) in the molten state of aluminum 6082 matrixes. It is being observed that the desired properties were subjective with the proportions of reinforcements and intermixing of liquid matrix and solid particulates. The tensile strength is enhanced from 172.96 MPa to 185.22 MPa with the 9 % (520gms) immersions of SiC into the Aluminum 6082 matrixes. The maximum hardness value was obtained as 56.8HRB in the case of aluminum 6082 base aluminum 6082 alloy matrix specimen without any reinforcements and small reduction with the additions of SiC observed respectively. © 2022

关键词： Silicon carbide

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Improved EEG-Based Epileptic Seizure Classification Using Fusion of Machine Learning Model with Statistical Feature Integration 3

Improved EEG-Based Epileptic Seizure Classification Using Fu...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sadangi, Rakesh Kumar Patra, Biswakalpa Gandhi, Tapan Kumar Sutar, Bijuni Charan Maity, Niladri Pratap Maity, Reshmi Mizoram Central University Dept. of Electronics and Communication Engineering Aizawl India Indian Institute of Technology Dept of Electrical Engineering New Delhi Delhi India Kmbb College of Engineering and Technology Dept. of Electronics and Communication Engineering Khordha India

ISBN: (纸本)9798331528201

Epilepsy is a neurological disorder characterized by recurrent seizures, often diagnosed through the analysis of electroencephalogram (EEG) signals. In this study, we propose a machine learning-based approach to classify EEG data into different brain states, focusing on epileptic seizure detection. Discrete Wavelet Transform (DWT) is employed to extract relevant time-frequency domain features from the EEG signals. Additionally, a set of statistical features - mean, standard deviation, variance, energy, entropy, coefficient of variation, root mean square, skewness, and kurtosis are incorporated to further enhance classification performance. Various machine learning models such as Random Forest, KNN, and LightGBM were evaluated. Results show that combining DWT with statistical features significantly improves the accuracy and robustness of the classification compared to using DWT alone. For example, the Random Forest model achieved an accuracy of 84.85% with DWT alone, which increased to 84.88% when combined with statistical features. Similarly, LightGBM improved from 84.87% to 85.10% in accuracy. This approach could serve as a valuable tool for aiding in the diagnosis and treatment of epilepsy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Experimental investigation on mechanical properties of stir casted aluminum SiC metal matrix composites

Experimental investigation on mechanical properties of stir ...

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作者： Mishra, Debashis Kumar Nanda, Alok Biju Patnaik Institute of IT and Management Studies Odisha Bhubaneswar751024 India KMBB College of Engineering and Technology Odisha Bhubaneswar752056 India

关键词： Silicon carbide

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Energy and Exergy Study of a Compression Ignition Engine Running with Producer Gas 2nd

Energy and Exergy Study of a Compression Ignition Engine Run...

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2nd International Conference on Recent Advances in Mechanical engineering Research and Development, ICRAMERD 2021

作者： Dash, Pradipta Kumar Mishra, Sanjaya Kumar Jena, Shakti Prakash Das, Harish Chandra Department of Mechanical Engineering KMBB College of Engineering and Technology Khurda752056 India Department of Mechanical Engineering SOA Deemed to be University Bhubaneswar751030 India Department of Mechanical Engineering NIT Meghalaya Shillong793003 India

ISBN: (纸本)9789811690563

In the present investigation, the energy and exergy analysis of a dual-fuel diesel engine was performed running with producer gas (PG) to quantify the exergy characteristics under the priority to achieve the highest diesel saving. 10% volume blend of Karanja oil methyl ester with diesel (B10) was considered as pilot fuel. The results showed that the brake-specific energy consumption increased from 14.6 kJ kWh−1 to 17.67 kJ kWh−1 and 18.43 kJ kWh−1 for dual-fuel operation with diesel and B10, respectively. It is also observed that at full-load engine operation the exergy efficiency decreases to 30.37% and 29.04% for dual-fuel operation with diesel and B10, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Combustion Exergy destruction Exergy efficiency Producer gas

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A Review on the Diagnosis of Epilepsy using ML 14

A Review on the Diagnosis of Epilepsy using ML

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sadangi, Rakesh Kumar Patra, Biswakalpa Maity, Reshmi Maity, Niladri Pratap Charan Sutar, Bijuni Mizoram Central University Dept. of Electronics and Communication Aizwal India Mizoram University Dept. of Electronics and Communication Engineering Aizwal India Kmbb College of Engineering and Technology Dept. of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350335095

The most prevalent developing neurological disease is epilepsy that affects many lives around the world. The commonly occuring and incalculable seizures leads to the loss of valuable human life. To diagnose epilepsy from brain signals with naked eye is always challenging. Clinicians need hours of training to understand seizure patterns from electroencephalograms (EEG). Despite the great effort, they sometimes misdiagnose due to human error. In this challenging situation, machine learning (ML) model might play an important role in the prefect diagnosis of the problem. The clinicians, instead of analyzing the 24 hours recordings of complicated EEG signals can take the help of ML algorithms and find out the problems of interest in a few seconds and finally give their expert advice, thus helping in accurate and fast diagnosis of the problem. Hence, in this review article, we have come up with a featured review on the diagnosis of epilepsy using machine learning. The paper begins with an understanding of EEG and the challenges in the diagnosis from collecting and processing the electroencephalography (EEG) data. The comparative analysis of various state of art ML techniques is compared and presented. Finally, we have concluded with the best available approach considering computational complexity. The proposed review will certainly help in developing both offline and online model that will assist healthcare professionals to diagnose the problems with higher accuracy and in less time. © 2023 IEEE.

关键词： Electroencephalography

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Detecting Disaster Related Tweets Using Hybrid Deep Neural Network Models

Detecting Disaster Related Tweets Using Hybrid Deep Neural N...

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2021 International Conference on Advances in technology, Management and Education, ICATME 2021

作者： Paul, Nayan Ranjan Sahoo, Manmohan Hati, Sukanta Kumar Sahoo, Tapaswini KMBB College of Engineering Technology Dept. of CSE Daleiput Khorda India

ISBN: (纸本)9781728185866

People use various social media platforms like twitter to report the disaster and disaster related information during crisis situations such as natural and man-made disasters. Detecting these disaster events can improve situational awareness for general public, response agencies and aid agencies. Many works has done for disaster event detection which uses traditional machine learning techniques but in recent years deep neural network models have demonstrated better results for many problems over traditional machine learning models. It is advantageous of using deep learning models because the model has the capacity to capture more than one layers of information. Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are three such deep neural networks. CNN can recognize local features in a multidimensional field and LSTM and GRU network can learn sequential data as it has the ability of remembering previously read data. In this paper, we use two hybrid deep neural network models named CNN-LSTM and CNN-GRU, which is developed by combining both CNN, LSTM, and CNN, GRU networks for doing event detection during disaster situation on Twitter data. We provide the detailed explanation of both models and perform comparison against Support Vector Machine (SVM), CNN, and LSTM models. Our result shows that both models can successfully identify the presence of disaster related events accurately from twitter data. We found that CNN-LSTM model is the best model achieving significant improvement of 18.74% than regular SVM model. © 2021 IEEE.

关键词： Long short-term memory

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An Autonomous Drone and Optical Network Based Animal Chaser and Multi Purpose Highway Traffic Monitoring System

An Autonomous Drone and Optical Network Based Animal Chaser ...

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2021 International Conference on Advances in technology, Management and Education, ICATME 2021

作者： Pradhan, Rabindra Kumar Dwibedi, Sudhanshu Ranjan Mahapatra, Prakash Kumar Barik, Braja Kishore KMBB College of Engineering. Technology Electronics Communication Engineering Daleiput Bhubaneswar Khordha India

ISBN: (纸本)9781728185866

An autonomous drone assisted imagery system for animal chasing on national highway, along with accident detection and automated emergency service measure and road quality detection for repairing by image processing is the innovative objective of this paper. A dedicated optical network has been proposed to provide high-speed data communication to cameras and drones, which later can also be utilized for various public related mass information services to government and private sectors. The autonomous transport and traffic control system can also track rampant illegal vehicle parking on highways, detect over speeding and overloaded goods vehicle for safety and load violation. In future, on road Wi-Fi service can be provided for commercial and urgent services by incorporating WAP units at specific points. Earlier drone-based systems have been used for survey and surveillance, but this will be a unique concept to facilitate various needs and requirements solely based on highways related problems and common occurring events, which need to be controlled on regular basis. © 2021 IEEE.

关键词： Cameras

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A comprehensive study on utilization of shell wastes in construction industry 4

A comprehensive study on utilization of shell wastes in cons...

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4th Innovative Product Design and Intelligent Manufacturing Systems, IPDIMS 2022

作者： Naik, Bibekananda Paul, Sriparna Choudhury, Susanta Kumar Sarangi, Piyush Paritosh Mustakim, Syed Mohammed Swar, Satya Narayana Jeet, Siddharth Jethy, Bandana CSIR-Institute of Minerals and Materials Technology Odisha Bhubaneswar India Academy of Scientific and Innovative Research Uttar Pradesh Ghaziabad India National Institute of Technology Odisha Rourkela India GIET University Odisha Gunupur India KMBB College of Engineering and Technology Odisha Khurda India

Concrete technology is now focused on acquiring supportable materials for concrete for countering dependency on natural resources. Some of the ingredients employed to make green-concrete usually comes from industrial byproducts. To lessen reliance on virgin resources, attempts have been made to integrate by-products and wastes into concrete. Seashell garbage, such as oyster scallop, and mussel shells are abundant in some areas and is typically abandoned or landfilled with little re-use value. This research highlights prior studies on the seashell trash as a replacement for traditional resources in concrete and correlated products. The features of various forms of shell trash are explored, as well as the implications of putting shells into concrete. Because of the high concentration of calcium oxide in the trash, the material properties indicate that it is inert. However, adequate handling, such as high temperature and crushing to produce fineness, is preferable. Both cement and aggregate can be replaced with seashell trash. The key concern is its durability, as well as its real contribution to cement. According to the analysis, only up to 20% of shell waste may be utilised for concrete building. Copyright © 2023 Elsevier Ltd. All rights reserved.

关键词： Molluscs

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Improved EEG-Based Epileptic Seizure Classification Using Fusion of Machine Learning Model with Statistical Feature Integration

Improved EEG-Based Epileptic Seizure Classification Using Fu...

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Communication, Control and Intelligent Systems (CCIS)

作者： Rakesh Kumar Sadangi Biswakalpa Patra Tapan Kumar Gandhi Bijuni Charan Sutar Niladri Pratap Maity Reshmi Maity Dept. of Electronics and Communication Engineering Mizoram Central University Aizawl India Dept of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Dept. of Electronics and Communication Engineering KMBB College of Engineering and Technology Khordha India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Epilepsy is a neurological disorder characterized by recurrent seizures, often diagnosed through the analysis of electroencephalogram (EEG) signals. In this study, we propose a machine learning-based approach to classify EEG data into different brain states, focusing on epileptic seizure detection. Discrete Wavelet Transform (DWT) is employed to extract relevant time-frequency domain features from the EEG signals. Additionally, a set of statistical features—mean, standard deviation, variance, energy, entropy, coefficient of variation, root mean square, skewness, and kurtosis are incorporated to further enhance classification performance. Various machine learning models such as Random Forest, KNN, and LightGBM were evaluated. Results show that combining DWT with statistical features significantly improves the accuracy and robustness of the classification compared to using DWT alone. For example, the Random Forest model achieved an accuracy of 84.85% with DWT alone, which increased to 84.88% when combined with statistical features. Similarly, LightGBM improved from 84.87% to 85.10% in accuracy. This approach could serve as a valuable tool for aiding in the diagnosis and treatment of epilepsy.

关键词： Time-frequency analysis Accuracy Wavelet domain Epilepsy Transforms Brain modeling Feature extraction Electroencephalography Discrete wavelet transforms Random forests

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