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i-manager's Journal on Future Engineering and Technology (JFET)

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Biomaterial Strategies for Immune System Enhancement and Tissue Healing
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A Critical Review on Biodiesel Production, Process Parameters, Properties, Comparison and Challenges
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Most Cited

Volume 19 Issue 2 January - March 2024

Research Paper

Design and Performance Simulation of a Soft Artificial Heart by using the ANSYS Software

Malik F. Elmzughi* , Mohammed A. Hamoudah, Tawfeeq Omar Almuhaddibi*

Department of Mechanical and Industrial Engineering, University of Tripoli, Tripoli, Libya.

Department of Biomedical Engineering, University of Tripoli, Tripoli, Libya.

Department of Mechanical Engineering, University of Tripoli, Tripoli, Libya.

Elmzughi, M. F., Hamoudah, M. A., and Almuhaddibi, T. O. (2024). Design and Performance Simulation of a Soft Artificial Heart by using the ANSYS Software. i-manager’s Journal on Future Engineering & Technology, 19(2), 1-9. https://doi.org/10.26634/jfet.19.2.20487

Abstract

Blood is pumped from the heart, a muscular organ, to various body organs via blood arteries. The aim of this paper is to create a temporary device, such as a pump, for individuals with cardiac diseases for whom survival without a transplant is unfeasible. Until a donor heart becomes available, the patient may have ample time with these makeshift devices. This paper uses engineering principles to explore the idea of an artificial heart. Using SOLIDWORKS 18 and ANSYS 21, numerical simulation and examination of the artificial heart were carried out. A Multiphysics static structural model and fluent fluid flow (CFD) analytical techniques were utilized to ascertain the dynamic response and impacts of pressure. SOLIDWORKS was utilized to model the 3D geometries, and ANSYS Design Modeler was used to import the geometries for preprocessing. The solver used throughout the study is ANSYS FLUENT, a tool used to analyze fluid flow troubles, known as Computational Fluid Dynamics (CFD). Next is mesh generation, which means discretization of the domain to solve governing equations at each cell and later specify the boundary zones to apply boundary conditions for this paper. The simulation results showed that at maximum levels of absolute pressure in air pressurized chambers, the performance of the heart remained secure and suitable for comfortable conditions.

References

[1]. Akshay, P., Lianjun, W., & Yonas, T. (2014). Artificial heart for a humanoid robot. Electroactive Polymer Actuators and Devices, 1, 1-16.

[2]. Amin, N. D., Little, B. P., & Henry, T. S. (2015). Imaging of left ventricular assist devices. Current Radiology Reports, 3, 1-9.

[3]. Baillargeon, B., Rebelo, N., Fox, D. D., Taylor, R. L., & Kuhl, E. (2014). The living heart project: A robust and integrative simulator for human heart function. European Journal of Mechanics-A/Solids, 48, 38-47.

[4]. Carr, C. M., Jacob, J., Park, S. J., Karon, B. L., Williamson, E. E., & Araoz, P. A. (2010). CT of left ventricular assist devices. Radiographics, 30(2), 429-444.

[5]. Cohrs, N. H. (2018). Silicone Elastomers for Artificial Hearts: 3D-Printing, Bioactive Glass and Potential (Doctoral dissertation, ETH Zurich).

[6]. Cohrs, N. H., Petrou, A., Loepfe, M., Yliruka, M., Schumacher, C. M., Kohll, A. X., Starck, C. T., Daners, M. S., Meboldt, M., Falk, V., & Stark, W. J. (2017). A soft total artificial heart-first concept evaluation on a hybrid mock circulation. Artificial Organs, 41(10), 948-958.

[7]. Copeland, J. G. (2013). SynCardia total artificial heart: Update and future. Texas Heart Institute Journal, 40(5), 587.

[8]. Davy, K. P., & Seals, D. R. (1994). Total blood volume in healthy young and older men. Journal of Applied Physiology, 76(5), 2059-2062.

[9]. Elamin, E. M. E. S., & Seory, A. M. (2016). Study of an Artificial Heart: Design and Simulation. Research Gate Publication.

[10]. Gerosa, G., Scuri, S., Iop, L., & Torregrossa, G. (2014). Present and future perspectives on total artificial hearts. Annals of Cardiothoracic Surgery, 3(6), 595.

[11]. Langer, R. M. (2011, May). Vladimir P. Demikhov, a pioneer of organ transplantation. In Transplantation Proceedings, 43 (4), 1221-1222. Elsevier.

[12]. Mitra, M. (2018). Editorial on Advances in Artificial Heart. Ann Heart, 3(1), 51-52.

[13]. Sen, A., Larson, J. S., Kashani, K. B., Libricz, S. L., Patel, B. M., Guru, P. K., & Farmer, J. C. (2016). Mechanical circulatory assist devices: A primer for critical care and emergency physicians. Critical Care, 20, 1-20.

[14]. Silvay, G., & Castillo, J. G. (2013). John Heysham Gibbon and the 60th anniversary of the first successful heart-lung machine: brief notes about the development of cardiac surgery in Europe and Slovakia. Bratisl Lek Listy, 114(5), 247-50.

[15]. Slepian, M. J. (2011). The SynCardia temporary total artificial heart-evolving clinical role and future status. US Cardiol, 8(1), 39-46.

[16]. Slepian, M. J., Alemu, Y., Soares, J. S., Smith, R. G., Einav, S., & Bluestein, D. (2013). The Syncardia™ total artificial heart: In vivo, in vitro, and computational modeling studies. Journal of Biomechanics, 46(2), 266-275.

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Research Paper

Multi-Position and Orientation Analysis of Spatial Parallel Manipulator using DE Algorithm

Meghavathu Roopa* , N. Mohan Rao**

*-** Department of Mechanical Engineering, Jawaharlal Nehru Technological University, Kakinada, India.

Roopa, M., and Rao, N. M. (2024). Multi-Position and Orientation Analysis of Spatial Parallel Manipulator using DE Algorithm. i-manager’s Journal on Future Engineering & Technology, 19(2), 10-21. https://doi.org/10.26634/jfet.19.2.20292

Abstract

Differential Evolution (DE) is an informative and direct approach to optimization with limited control parameters. This paper presents a multi-position and orientation analysis of a Spatial Parallel Manipulator considering the influence of physical constraints, such as limb lengths and spherical joint motion. The synthesis process primarily involves determining the scopes of the permanent base and movable platform relative to the axes of the revolute joints, enabling the moving platform to traverse a predetermined range of positions. The dimensions of the moving platform and fixed base are determined by considering restrictions on joint motion to design the parallel manipulator using the DE algorithm method. The synthesis procedure is illustrated with a numerical example involving eight positions.

References

[1]. Abbasnejad, G., Zarkandi, S., & Imani, M. (2010). Forward kinematics analysis of a 3-PRS parallel manipulator. World Academy of Science, Engineering and Technology, 37, 329-335.

[2]. Antonov, A., Fomin, A., Glazunov, V., Petelin, D., & Filippov, G. (2023). Type synthesis of 5-DOF hybrid (parallel-serial) manipulators designed from open kinematic chains. Robotics, 12(4), 98.

[3]. Babu, S. R., Raju, V. R., & Ramji, K. (2013). Design for optimal performance of 3-RPS parallel manipulator using evolutionary algorithms. Transactions of the Canadian Society for Mechanical Engineering, 37(2), 135-160.

[4]. Chen, Q., Chen, Z., Chai, X., & Li, Q. (2013). Kinematic analysis of a 3-axis parallel manipulator: The P3. Advances in Mechanical Engineering, 5, 589156.

[5]. Dhandapani, C., & Sivaramakrishnan, R. (2014). Modelling, simulation and synthesis of three degrees of freedom triglide parallel manipulator for positioning and orientation. International Journal of Mechanical and Mechatronics Engineering IJMME-IJENS, 14(2), 66-76.

[6]. Fan, Z., Guo, Y. J., & Zhi. L. B. (2008). Kinematic characteristics analysis of 3-PRS parallel manipulator. Journal of Donghua University, 25(1), 78-81.

[7]. Hernandez, A., Ibarreche, J. I., Petuya, V., & Altuzarra, O. (2014). Structural synthesis of 3-DoF spatial fully parallel manipulators. International Journal of Advanced Robotic Systems, 11(7), 101.

[8]. Huang, P., Wang, J., Wang, L., & Qian, J. (2012). Dimensional synthesis for 3-PRS mechanism based on identifiability performance. Chinese Journal of Mechanical Engineering, 25(2), 234-240.

[9]. Lee, K. M., & Shah, D. K. (1988). Dynamic analysis of a three- degrees- of- freedom in- parallel actuated manipulator. IEEE Journal on Robotics and Automation, 4(3), 361-367.

[10]. Li, Q., & Hervé, J. M. (2010). 1T2R parallel mechanisms without parasitic motion. IEEE Transactions on Robotics, 26(3), 401-410.

[11]. Li, Q., Chen, Z., Chen, Q., Wu, C., & Hu, X. (2011). Parasitic motion comparison of 3-PRS parallel mechanism with different limb arrangements. Robotics and Computer-Integrated Manufacturing, 27(2), 389-396.

[12]. Li, Y., & Xu, Q. (2006). Kinematic analysis and design of a new 3-DOF translational parallel manipulator. Journal Mechanical Design, 128(4), 729-737.

[13]. Liu, X. J., Wang, J., & Kim, J. (2006). Determination of the link lengths for a spatial 3-DOF parallel manipulator. Journal Mechanical Design, 128(2), 365-373.

[14]. Lu, S., & Li, Y. (2014, July). Dimensional synthesis of a 3-DOF translational parallel manipulator considering kinematic dexterity property. In 2014 IEEE International Conference on Information and Automation (ICIA) (pp. 7- 12). IEEE.

[15]. Mattila, J., Koivumäki, J., Caldwell, D. G., & Semini, C. (2017). A survey on control of hydraulic robotic manipulators with projection to future trends. iEeE/ASME Transactions on Mechatronics, 22(2), 669-680.

[16]. Nayak, A., Stigger, T., Husty, M. L., Wenger, P., & Caro, S. (2018). Operation mode analysis of 3-RPS parallel manipulators based on their design parameters. Computer Aided Geometric Design, 63, 122-134.

[17]. Nurahmi, L., Caro, S., & Wenger, P. (2015). Design of 3-RPS parallel manipulators based on operation modes. In The 14th IFToMM World Congress in Mechanism and Machine Science (pp. 1-10).

[18]. Patel, Y., & George, P. (2014). Constraint and inverse kinematic analysis of 3-RPS parallel manipulator. In 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) (pp. 1-6).

[19]. Pundru, S. R., & Nalluri, M. R. (2018). Three degree of freedom spatial parallel manipulator inverse kinematic position analysis. International Journal Engineering & Technology (UAE), 7(45), 98-101.

[20]. Pundru, S. R., & Nalluri, M. R. (2021). Synthesis of 3-PRS manipulator based on revolute and cone angle of spherical constraints on range of rotational motion of spherical joints. Journal of the Institution of Engineers (India): Series C, 102, 209-219.

[21]. Qian, J., Li, Y., & Zhuge, L. (2020). An investigation on a Novel3-RCU flexible micromanipulator. Micromachines, 11(4), 423.

[22]. Rao, N, M. (2010). Synthesis of a spatial 3-RPS parallel manipulator based on physical constraints. Sadhana, 35(6), 739-746.

[23]. Rao, N. M. (2011). Synthesis of a spatial 3-RPS parallel manipulator based on physical constraints using hybrid GA simplex method. The International Journal of Advanced Manufacturing Technology, 52, 777-787.

[24]. Rao, N. M., & Rao, K. M. (2009). Dimensional synthesis of a spatial 3-RPS parallel manipulator for a prescribed range of motion of spherical joints. Mechanism and Machine Theory, 44(2), 477-486.

[25]. Rao, P. S. (2022). Calibrating a synthesized 3-PRS manipulator by minimizing the errors in positions of revolute joints. Journal of the Institution of Engineers (India): Series C, 103(5), 1083-1093.

[26]. Shi, Y., Gao, Y., & Guo, L. H. (2016). Kinematic analysis of a 3-DOF parallel machine tool with large workspace. In MATEC Web of Conferences, 44,02075. EDP Sciences.

[27]. Staicu, S. (2009). Dynamics of the 3-PRP planar parallel robot. The Romanian Journal of Technical Sciences: Applied Mechanics, 54(2), 125-142.

[28]. Sun, T., Song, Y., Li, Y., & Liu, L. (2010). Dimensional synthesis of a 3-DOF parallel manipulator based on dimensionally homogeneous Jacobian matrix. Science in China Series E: Technological Sciences, 53(1), 168- 174.

[29]. Tsai, M. S., Shiau, T. N., Tsai, Y. J., & Chang, T. H. (2003). Direct kinematic analysis of a 3-PRS parallel mechanism. Mechanism and Machine Theory, 38(1), 71- 83.

[30]. Urizar, M., Petuya, V., Diez, M., & Hernández, A. (2014). Dimensional Synthesis of a Spatial Orientation 3-DoF Parallel Manipulator by Characterizing the Configuration Space. In Computational Kinematics: Proceedings of the 6th International Workshop on Computational Kinematics (CK2013) (pp. 85-92). Springer Netherlands.

[31]. Yun, Y., Wnag, L., & Guan, L. (2004, October). Dimensional synthesis of a 3-DOF parallel manipulator. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), 6, 5317-5323. IEEE.

[32]. Zarkandi, S., & Esmaili, M. R. (2011). Direct position kinematics of a three revolute-prismatic-spherical parallel manipulator. International Journal of Recent Research and Applied Studies, 7(1), 88-95.

[33]. Zhang, Y., & Ting, K. L. (2013). Design and analysis of a spatial 3-DOF parallel manipulator with 2T1R-type. International Journal of Advanced Robotic Systems, 10(5), 226.

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Research Paper

Advancing Precision Agriculture through Multi-Objective Optimization using Butterfly Algorithm

Grandhimi Lahari Kamakshi* , Tejaswini Thokachichu, Sivalasetty Sindhu Bhargavi*, Nandigama Induja**, Saida Rao Samudrala*, Chintalapudi V Suresh****

*-****** Department of Computer Science and Engineering, Vasireddy Venkatadri Institute of Technology, Numbur, Guntur, Andhra Pradesh, India.

Kamakshi, G. L., Thokachichu, T., Bhargavi, S. S., Induja, N., Samudrala, S. R., and Suresh, C. V. (2024). Advancing Precision Agriculture through Multi-Objective Optimization using Butterfly Algorithm. i-manager’s Journal on Future Engineering & Technology, 19(2), 22-35. https://doi.org/10.26634/jfet.19.2.20481

Abstract

Wireless Sensor Networks (WSNs) are transforming precision agriculture by enabling seamless monitoring and control of key factors such as temperature, humidity, solar radiation, soil moisture, and various dissolved compounds. This technology enhances efficiency and productivity while reducing costs. However, optimizing coverage area and energy efficiency in precision agriculture WSNs presents significant challenges. To address these challenges, our work focuses on developing innovative solutions inspired by advanced algorithms and state-of-the-art techniques for WSNs. Our primary objective is to improve area coverage and reduce energy consumption in precision agriculture WSNs. We are developing algorithms that can adapt to diverse agricultural landscapes. Through simulations, we aim to evaluate the performance and impact of our novel algorithm on precision agriculture applications. These simulations will provide valuable insights into the effectiveness of our algorithm in enhancing coverage area and energy efficiency in WSNs. Furthermore, our research aims to contribute to the broader field of WSNs by providing a detailed analysis of the challenges and opportunities in optimizing coverage area and energy efficiency in agricultural settings. By leveraging advanced algorithms and techniques, we aim to enhance the capabilities of WSNs in precision agriculture, leading to more sustainable and efficient farming practices.

References

[1]. Akram, V. K., Dagdeviren, O., & Tavli, B. (2021). A coverage-aware distributed k-connectivity maintenance algorithm for arbitrarily large k in mobile sensor networks. IEEE/ACM Transactions on Networking, 30(1), 62-75.

[2]. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688-2710.

[3]. Han, G., & Zhang, L. (2018). WPO-EECRP: Energy-efficient clustering routing protocol based on weighting and parameter optimization in WSN. Wireless Personal Communications, 98, 1171-1205.

[4]. Hanh, N. T., Binh, H. T. T., Hoai, N. X., & Palaniswami, M. S. (2019). An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Information Sciences, 488, 58-75.

[5]. He, T., Krishnamurthy, S., Luo, L., Yan, T., Gu, L., Stoleru, R., & Krogh, B. (2006). Vigilnet: An integrated sensor network system for energy-efficient surveillance. ACM Transactions on Sensor Networks (TOSN), 2(1), 1-38.

[6]. Kaushik, A., Indu, S., & Gupta, D. (2019). A grey wolf optimization approach for improving the performance of wireless sensor networks. Wireless Personal Communications, 106, 1429-1449.

[7]. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks, 4, 1942- 1948. IEEE.

[8]. Kong, H., & Yu, B. (2019, May). An improved method of WSN coverage based on enhanced PSO algorithm. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 1294-1297). IEEE.

[9]. Kumar, S., Kumar, N., & Saini, R. K. (2019, November). Energy-saving sensors for precision agriculture in wireless sensor network: A review. In 2019 Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE) (pp. 65-70). IEEE.

[10]. Liang, J., Tian, M., Liu, Y., & Zhou, J. (2022). Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Scientific Reports, 12(1), 11687.

[11]. Liang, J., Tian, M., Liu, Y., & Zhou, J. (2022). Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Scientific Reports, 12(1), 11687.

[12]. Liu, C., Zhao, Z., Qu, W., Qiu, T., & Sangaiah, A. K. (2019). A distributed node deployment algorithm for underwater wireless sensor networks based on virtual forces. Journal of Systems Architecture, 97, 9-19.

[13]. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 102317.

[14]. Mirhosseini, M., Barani, F., & Nezamabadi-pour, H. (2017). Design optimization of wireless sensor networks in precision agriculture using improved BQIGSA. Sustainable Computing: Informatics and Systems, 16, 38-47.

[15]. Morsy, N. A., AbdelHay, E. H., & Kishk, S. S. (2018). Proposed energy efficient algorithm for clustering and routing in WSN. Wireless Personal Communications, 103, 2575-2598.

[16]. Wang, J., Ju, C., Kim, H. J., Sherratt, R. S., & Lee, S. (2019). A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs. Cluster Computing, 22, 1787-1795.

[17]. Wang, Z. X., Zhang, M., Gao, X., Wang, W., & Li, X. (2019). A clustering WSN routing protocol based on node energy and multipath. Cluster Computing, 22, 5811-5823.

[18]. Wu, X., Chen, G., & Das, S. K. (2008). Avoiding energy holes in wireless sensor networks with nonuniform node distribution. IEEE Transactions on Parallel and Distributed Systems, 19(5), 710-720.

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Review Paper

Bioprinting Revolutionizing Regenerative Medicine and Tissue Engineering

Atheena Milagi Pandian S.* , Rashika Murugan, N. Sri Manoj Kumar*, M. Sudherson****

*-**** Atheenapandian Private Limited, India.

Pandian, S. A., Murugan, R., Kumar, N. S. M., and Sudherson, M. (2024). Bioprinting Revolutionizing Regenerative Medicine and Tissue Engineering. i-manager’s Journal on Future Engineering & Technology, 19(2), 36-44. https://doi.org/10.26634/jfet.19.2.20519

Abstract

Bio-printing is a trending technology in the field of tissue engineering and regenerative medicine, creating complex three-dimensional structures. The advancements in bio-printing, including the steps and materials used in bio-printers, have been highlighted. Various bio-printing techniques such as micro-extrusion, inkjet printing, and laser-based approaches are discussed. Moreover, the roles of biomaterials and their importance in bio-printing cannot be overstated. Characteristics such as cell viability, adhesion, differentiation, biodegradability, and biocompatibility are paramount in ensuring the success of bio-printed tissues. Additionally, the interdisciplinary nature of bio-printing research fosters collaborations between biologists, engineers, material scientists, and medical professionals. This synergy facilitates the development of innovative bio-printing technologies and accelerates their translation into clinical applications. Furthermore, the evolution of bio-printing technology is not only reshaping medical practice but also opening new frontiers in scientific exploration. By mimicking the complex architectures and functionalities of native tissues, bio-printed constructs serve as valuable tools for studying tissue development, disease mechanisms, and drug responses. The ability to recreate physiologically relevant microenvironments in vitro provides researchers with unprecedented insights into cellular behavior and tissue dynamics. The bio-printing represents a transformative approach in tissue engineering and regenerative medicine, offering unparalleled opportunities for tissue fabrication, disease modeling, and drug discovery. As research in this field continues to progress, we can anticipate even more remarkable breakthroughs that will revolutionize healthcare and biomedical research.

References

[1]. Aljohani, W., Ullah, M. W., Zhang, X., & Yang, G. (2018). Bioprinting and its applications in tissue engineering and regenerative medicine. International Journal of Biological Macromolecules, 107, 261-275.

[2]. Dzobo, K., Thomford, N. E., Senthebane, D. A., Shipanga, H., Rowe, A., Dandara, C., Pillay, M., & Motaung, K. S. C. M. (2018). Advances in regenerative medicine and tissue engineering: Innovation and transformation of medicine. Stem Cells International, 2018, 1-24.

[3]. Gomes, M. E., Rodrigues, M. T., Domingues, R. M., & Reis, R. L. (2017). Tissue engineering and regenerative medicine: New trends and directions-a year in review. Tissue Engineering Part B: Reviews, 23(3), 211-224.

[4]. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510.

[5]. Iram, D., Riaz, R., & Iqbal, R. K. (2019). 3D Bioprinting: An attractive alternative to t raditional organ transplantation. Biomedical Science and Engineering, 5(1), 7-18.

[6]. Irvine, S. A., & Venkatraman, S. S. (2016). Bioprinting and differentiation of stem cells. Molecules, 21(9), 1188.

[7]. Jammalamadaka, U., & Tappa, K. (2018). Recent advances in biomaterials for 3D printing and tissue engineering. Journal of Functional Biomaterials, 9(1), 22.

[8]. Ma, J., Wang, Y., & Liu, J. (2018). Bioprinting of 3D tissues/organs combined with microfluidics. RSC Advances, 8(39), 21712-21727.

[9]. Maan, Z., Masri, N. Z., & Willerth, S. M. (2022). Smart bioinks for the printing of human tissue models. Biomolecules, 12(1), 141.

[10]. Murphy, S. V., & Atala, A. (2014). 3D bioprinting of tissues and organs. Nature Biotechnology, 32(8), 773-785.

[11]. Nair, K., Gandhi, M., Khalil, S., Yan, K. C., Marcolongo, M., Barbee, K., & Sun, W. (2009). Characterization of cell viability during bioprinting processes. Biotechnology Journal: Healthcare Nutrition Technology, 4(8), 1168-1177.

[12]. Schuurman, W., Khristov, V., Pot, M. W., van Weeren, P. R., Dhert, W. J., & Malda, J. (2011). Bioprinting of hybrid tissue constructs with tailorable mechanical properties. Biofabrication, 3(2), 021001.

[13]. Seol, Y. J., Kang, H. W., Lee, S. J., Atala, A., & Yoo, J. J. (2014). Bioprinting technology and its applications. European Journal of Cardio-Thoracic Surgery, 46(3), 342-348.

[14]. Ude, A., Afi-Leslie, K., Okeke, K., & Ogbodo, E. (2022). Trypan Blue Exclusion Assay, Neutral Red, Acridine Orange and Propidium Iodide. IntechOpen.

[15]. Vijayavenkataraman, S., Yan, W. C., Lu, W. F., Wang, C. H., & Fuh, J. Y. H. (2018). 3D bioprinting of tissues and organs for regenerative medicine. Advanced Drug Delivery Reviews, 132, 296-332.

[16]. Zhang, Z., Jin, Y., Yin, J., Xu, C., Xiong, R., Christensen, K., Ringeisen, B. R., Chrisey, D. B., & Huang, Y. (2018). Evaluation of bioink printability for bioprinting applications. Applied Physics Reviews, 5(4), 1- 21.

[17]. Zhu, Y., Joralmon, D., Shan, W., Chen, Y., Rong, J., Zhao, H., Xiao, S., & Li, X. (2021). 3D printing biomimetic materials and structures for biomedical applications. Bio- Design and Manufacturing, 4, 405-428.

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Review Paper

Graphene & Its Applications as Field Effect Transistors

Amandeep Kaur Rozi* , Harminder Singh, Deepkamal Randhawa*, Anu Sheetal****

, Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Jalandhar, Punjab, India.

Department of Mechanical Engineering, Guru Nanak Dev University, Amritsar, Punjab, India.

**** Department of Electronics and Communication Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India.

Rozi, A. K., Singh, H., Randhawa, D., and Sheetal, A. (2024). Graphene & Its Applications as Field Effect Transistors. i-manager’s Journal on Future Engineering & Technology, 19(2), 45-54. https://doi.org/10.26634/jfet.19.2.20538

Abstract

Graphene, owing to its excellent physical, mechanical, and electrical properties, as well as its outstanding optoelectronic properties, has garnered significant attention. Consequently, it has opened numerous opportunities for various types of future devices and systems. Graphene, a one-atom-thick layer of carbon atoms forming a honeycomb 2D crystal lattice, stands out as one of the most promising candidates in the field of nano- and microelectronics. This review provides an introduction to graphene, detailing its properties and its applications, particularly focusing on its utilization in the realm of Field Effect Transistors (FETs). Specifically, an analytical device model of heterostructure-based FETs is presented, which essentially comprises an array of nano ribbons clad. The model for Graphene Nano Ribbon (GNR) FETs includes Poisson's equation and can be employed to calculate the current-voltage characteristics and spatial distribution of electric potential along the channel.

References

[1]. Acheson, E. G. (1907). Deflocculated graphite. Journal of the Franklin Institute, 164(5), 375-385.

[2]. Alam, S. N., Sharma, N., & Kumar, L. (2017). Synthesis of graphene oxide (GO) by modified hummers method and its thermal reduction to obtain reduced graphene oxide (rGO). Graphene, 6(1), 1-18.

[3]. Avouris, P., Chen, Z., & Perebeinos, V. (2007). Carbon- based electronics. Nature Nanotechnology, 2(10), 605-615.

[4]. Bellier, N., Baipaywad, P., Ryu, N., Lee, J. Y., & Park, H. (2022). Recent biomedical advancements in graphene oxide-and reduced graphene oxide-based nanocomposite nanocarriers. Biomaterials Research, 26(1), 65.

[5]. Chen, J., Yao, B., Li, C., & Shi, G. (2013). An improved Hummers method for eco-friendly synthesis of graphene oxide. Carbon, 64, 225-229.

[6]. Chimot, N., Derycke, V., Goffman, M. F., Bourgoin, J. P., Happy, H., & Dambrine, G. (2007). Gigahertz frequency flexible carbon nanotube transistors. Applied Physics Letters, 91(15), 3111.

[7]. Choi, W., Lahiri, I., Seelaboyina, R., & Kang, Y. S. (2010). Synthesis of graphene and its applications: A review. Critical Reviews in Solid State and Materials Sciences, 35(1), 52-71.

[8]. Chowdhury, S. R., & Maiyalagan, T. (2022). CuCo2S4@ B, N-doped reduced graphene oxide hybrid as a bifunctional electrocatalyst for oxygen reduction and evolution reactions. ACS Omega, 7(23), 19183- 19192.

[9]. Dada, O. J. (2020). Electronic Characteristics of Graphene Oxide and Reduced Graphene. LAP Lambert Academic Publishing.

[10]. Gu, G., Nie, S., Feenstra, R. M., Devaty, R. P., Choyke, W. J., Chan, W. K., & Kane, M. G. (2007). Field effect in epitaxial graphene on a silicon carbide substrate. Applied Physics Letters, 90(25), 253507.

[11]. Guisinger, N. P., & Arnold, M. S. (2010). Beyond silicon: Carbon-based nanotechnology. MRS Bulletin, 35(4), 273-279.

[12]. Han, S. J., Jenkins, K. A., Valdes Garcia, A., Franklin, A. D., Bol, A. A., & Haensch, W. (2011). High-frequency graphene voltage amplifier. Nano Letters, 11(9), 3690- 3693.

[13]. Hayes, W. I., Joseph, P., Mughal, M. Z., & Papakonstantinou, P. (2015). Production of reduced graphene oxide via hydrothermal reduction in an aqueous sulphuric acid suspension and i t s electrochemical behaviour. Journal of Solid State Electrochemistry, 19, 361-380.

[14]. Hibino, H., Kageshima, H., Maeda, F., Nagase, M., Kobayashi, Y., & Yamaguchi, H. (2008). Microscopic thickness determination of thin graphite films formed on SiC from quantized oscillation in reflectivity of low-energy electrons. Physical Review B, 77(7), 075413.

[15]. Kim, K. S., Zhao, Y., Jang, H., Lee, S. Y., Kim, J. M., Kim, K. S., & Hong, B. H. (2009). Large-scale pattern growth of graphene films for stretchable transparent electrodes. Nature, 457(7230), 706-710.

[16]. Liu, G., Stillman, W., Rumyantsev, S., Shao, Q., Shur, M., & Balandin, A. A. (2009). Low-frequency electronic noise in the double-gate single-layer graphene transistors. Applied Physics Letters, 95(3), 033103.

[17]. Meric, I., Dean, C. R., Han, S. J., Wang, L., Jenkins, K. A., Hone, J., & Shepard, K. L. (2011, December). High- frequency performance of graphene field effect transistors with saturating IV-characteristics. In 2011 International Electron Devices Meeting (pp. 2-1). IEEE.

[18]. Mokhtar, M., El Enein, S. A., Hassaan, M., Morsy, M., & Khalil, M. (2017). Thermally reduced graphene oxide: Synthesis, structural and electrical properties. I nternational Journal of Nanoparticles and Nanotechnology, 3(1), 1-9.

[19]. Mombeshora, E. T., & Muchuweni, E. (2023). Dynamics of reduced graphene oxide: Synthesis and structural models. RSC Advances, 13(26), 17633-17655.

[20]. Moon, J. S., Curtis, D., Bui, S., Hu, M., Gaskill, D. K., Tedesco, J. L., & Weng, X. (2010). Top-gated epitaxial graphene FETs on Si-face SiC wafers with a peak transconductance of 600 mS/mm. IEEE Electron Device Letters, 31(4), 260-262.

[21]. Novoselov, K. S., Geim, A. K., Morozov, S. V., Jiang, D. E., Zhang, Y., Dubonos, S. V., & Firsov, A. A. (2004). Electric field effect in atomically thin carbon films. Science, 306(5696), 666-669.

[22]. Oliveira, A. E. F., Braga, G. B., Tarley, C. R. T., & Pereira, A. C. (2018). Thermally reduced graphene oxide: Synthesis, studies and characterization. Journal of Materials Science, 53(17), 12005-12015.

[23]. Rao, K. S., Senthilnathan, J., Liu, Y. F., & Yoshimura, M. (2014). Role of peroxide ions in formation of graphene nanosheets by electrochemical exfoliation of graphite. Scientific Reports, 4(1), 4237.

[24]. Saha, V. C., Sabuj, M. M. A., Shams, P., Rahman, S., Qadir, M. R., Islam, M. R., & Gulshan, F. (2018, October). Synthesis and characterization of reduced graphene oxide reinforced polymer matrix composite. In IOP Conference Series: Materials Science and Engineering, 438(1), 012008. IOP Publishing.

[25]. Schedin, F., Geim, A. K., Morozov, S. V., Hill, E. W., Blake, P., Katsnelson, M. I., & Novoselov, K. S. (2007). Detection of individual gas molecules adsorbed on graphene. Nature Materials, 6(9), 652-655.

[26]. Sengupta, I., Chakraborty, S., Talukdar, M., Pal, S. K., & Chakraborty, S. (2018). Thermal reduction of graphene oxide: How temperature influences purity. Journal of Materials Research, 33(23), 4113-4122.

[27]. Shurman, K. M., & Nasser, H. A. (2014). CVD graphene growth mechanism on nickel thin films. Excerpt from the Proceedings of the 2014 COMSOL Conference in Boston.

[28]. Torrisi, L., Cutroneo, M., Torrisi, A., & Silipigni, L. (2022). Measurements on five characterizing properties of graphene oxide and reduced graphene oxide foils. Physica Status Solidi (A), 219(6), 2100628.

[29]. Verma, D., Gope, P. C., Shandilya, A., & Gupta, A. (2014) . Mechanical-thermal-electrical and morphological properties of graphene reinforced polymer composites: A review. Transactions of the Indian Institute of Metals, 67, 803-816.

[30]. Vladimirovich, G. M., & Melnikov, V. P. (2018). Graphene oxide/reduced graphene oxide aerogels. Graphene Oxide-Applications and Opportunities, 32, 137-144.

[31]. Wang, C., Chien, J. C., Fang, H., Takei, K., Nah, J., Plis, E., & Javey, A. (2012a). Self-aligned, extremely high frequency III–V metal-oxide-semiconductor field-effect transistors on rigid and flexible substrates. Nano Letters, 12(8), 4140-4145.

[32]. Wang, H., Hsu, A. L., & Palacios, T. (2012b). Graphene electronics for RF applications. IEEE Microwave Magazine, 13(4), 114-125.

[33]. Wang, X., Zhi, L., & Müllen, K. (2008). Transparent, conductive graphene electrodes for dye-sensitized solar cells. Nano Letters, 8(1), 323-327.

[34]. Wang, Y., Hu, G., Cao, Y., Peng, Z., & Du, K. (2021). One-pot synthesis of pre-reduced graphene oxide for efficient production of high-quality reduced graphene oxide and its lithium storage application. Materials Chemistry and Physics, 265, 124523.

[35]. Wang, Z., Zhang, Z., Xu, H., Ding, L., Wang, S., & Peng, L. M. (2010). A high-performance top-gate graphene field-effect transistor based frequency doubler. Applied Physics Letters, 96(17), 173104.

[36]. Wu, Y. Q., Ye, P. D., Capano, M. A., Xuan, Y., Sui, Y., Qi, M., & Reifenberger, R. (2008). Top-gated graphene field- effect-transistors formed by decomposition of SiC. Applied Physics Letters, 92(9), 092102.

[37]. Yang, X., Liu, G., Balandin, A. A., & Mohanram, K. (2010). Triple-mode single-transistor graphene amplifier and its applications. ACS Nano, 4(10), 5532-5538.

[38]. Yao, Z., Kane, C. L., & Dekker, C. (2000). High-field electrical transport in single-wall carbon nanotubes. Physical Review Letters, 84(13), 2941-2944.

[39]. Zare, Y., Gharib, N., & Rhee, K. Y. (2023). Influences of graphene morphology and contact distance between nanosheets on the effective conductivity of polymer nanocomposites. Journal of Materials Research and Technology, 25, 3588-3597.

[40]. Zhao, P., & Guo, J. (2009). Modeling edge effects in graphene nanoribbon field-effect transistors with real and mode space methods. Journal of Applied Physics, 105(3), 034503.

i-manager's Journal on Future Engineering and Technology (JFET)

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Volume 19 Issue 2 January - March 2024

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*Department of Mechanical and Industrial Engineering, University of Tripoli, Tripoli, Libya. **Department of Biomedical Engineering, University of Tripoli, Tripoli, Libya. ***Department of Mechanical Engineering, University of Tripoli, Tripoli, Libya.

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Advancing Precision Agriculture through Multi-Objective Optimization using Butterfly Algorithm

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Malik F. Elmzughi* , Mohammed A. Hamoudah, Tawfeeq Omar Almuhaddibi*

Department of Mechanical and Industrial Engineering, University of Tripoli, Tripoli, Libya.

Department of Biomedical Engineering, University of Tripoli, Tripoli, Libya.

Department of Mechanical Engineering, University of Tripoli, Tripoli, Libya.

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