Nanotechnology in Space Study

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Applied Nanotechnology
Received Date: 10/04/2024
Acceptance Date: 10/12/2024
Published On: 2024-10-26
First Page: 39
Last Page: 46

Journal Menu

By: Kazi Kutubuddin Sayyad Liyakat

Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India.

Abstract

The burgeoning field of nanotechnology presents transformative potentials across various scientific disciplines, and its implications for space exploration and research are particularly compelling. The nonconcrete on “Nanotechnology in Space Study” provides a concise yet comprehensive overview of the current advancements and applications of nanotechnology within the context of space missions. This includes the development of lightweight, durable materials that can withstand extreme conditions in space, enhancing the structural integrity of spacecraft while minimizing launch costs. The discussion on nanosensors is particularly insightful, emphasizing how these small-scale devices can monitor the health of both spacecraft systems and astronauts, allowing for real-time data collection that is critical for long-duration missions. It effectively piques interest in the potential of this cutting-edge science to enhance our capabilities in space exploration. Future research should not only focus on harnessing the strengths of nanotechnology but also critically evaluate its limitations and ramifications for sustainable practices in space. Overall, it is an exciting time for both nanotechnology and space exploration, and this study encapsulates the intersection of these fields effectively.

Keywords: Nanotechnology, Space study, Nanosensors, energy

Loading

Citation:

How to cite this article: Kazi Kutubuddin Sayyad Liyakat, Nanotechnology in Space Study. International Journal of Applied Nanotechnology. 2024; 10(02): 39-46p.

How to cite this URL: Kazi Kutubuddin Sayyad Liyakat, Nanotechnology in Space Study. International Journal of Applied Nanotechnology. 2024; 10(02): 39-46p. Available from:https://journalspub.com/publication/ijan/article=11616

Refrences:

1. Halli UM. Nanotechnology in IoT security. J Nanosci Nanoeng Appl. 2022;12(3):11–16.
2. Wale AD, Rokade DR, Adsul SB, Kutubuddin K. Smart agriculture system using IoT. Int J Innov Res Technol. 2019;5(10):493–497.
3. Halli UM. Nanotechnology in E-Vehicle batteries. Int J Nanomater Nanostructures. 2022;8(2):22–27.
4. Sayyad Liyakat KK. Nanotechnology application in neural growth support system. NTS. 2022;24(2):47–55.
5. Mishra SB, Liyakat KS, Liyakat KK. AI-Driven IoT (AI IoT) in thermodynamic engineering. JMTMS. 2024;6(1):1–8.
6. Liyakat KSS. Accepting Internet of nano-things: Synopsis, developments, and challenges. J Nanoscience, Nanoengineering Appl. 2023;13(2):17–26. doi: 10.37591/jonsnea.v13i2.1464.
7. Liyakat KSS, Liyakat KKS. Nanomedicine as a potential therapeutic approach to COVID-19. Int J Appl Nanotechnol. 2023;9(2):27–35.
8. Liyakat KKS. Nanotechnology in precision farming: The role of research. Int J Nanomater Nanostruct. 2023;9(2). doi: 10.37628/ijnn.v9i2.1051.
9. Sayyad Liyakat KK. Smart agriculture based on AI-driven-IoT (AIIoT): A KSK approach. ARCEI. 2024;1(2):23–32.
10. Kazi K. Complications with malware identification in IoT and an overview of artificial immune approaches. Res Rev J Immunol. 2024;14(01):54–62.
11. Liyakat KKS. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Udgata SK, Sethi S, Gao XZ, editors. ICMIB 2023. Lecture Notes in Networks and Systems. Singapore: Springer; 2024. Available from: https://link.springer.com/ chapter/10.1007/978-981-99-3932-9_12. doi: 10.1007/978-981-99-3932-9_12.
12. Kavithamani V, Pradeep G, Janani M, Balasamy K, Rithani B. Advanced grape leaf disease detection using neural network. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) 2023;949–954. IEEE.
13. Liyakat KKS. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. Int Conf Emerg Smart Comput Inform (ESCI). Pune, India. 2023. pp. 1–5. doi: 10.1109/ESCI56872.2023.10099544.
14. Kasat K, Shaikh N, Rayabharapu VK, Nayak M, Sayyad Liyakat KK. Implementation and recognition of waste management system with mobility solution in smart cities using internet of things. 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India. 2023;1661–1665. doi: 10.1109/ICAISS58487.2023.10250690.
15. Liyakat KKS. Machine learning approach using artificial neural networks to detect malicious nodes in IoT networks. In: Shukla PK, Mittal H, Engelbrecht A, editors. Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Singapore: Springer; 2023. doi: 10.1007/978–981–99–4577–1_3.
16. Kazi K. AI-driven IoT (AIIoT) in healthcare monitoring. In Nguyen T, Vo N, editors. Using Traditional Design Methods to Enhance AI-Driven Decision Making. Hershey, Pennsylvania: IGI Global; 2024(a);77–101. doi: 10.4018/979-8-3693-0639-0.ch003 Available at: https://www.igi-global.com/chapter/ai-driven-iot-aiiot-in-healthcare-monitoring/336693.
17. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for E-mobility ecosystems. In: Nagpal N, Kassarwani V, Varthanan G, Siano P, editors. IGI Global; 2024. pp. 295–320. Available from: https://www.igi-global.com/gateway/chapter/full-text-pdf/341172. doi: 10.4018/979-8-3693-2611-4.ch014.
18. Kazi KS. Computer-aided diagnosis in ophthalmology: A technical review of deep learning applications. In: Garcia M, de Almeida R, editors. Transformative Approaches to Patient Literacy and Healthcare Innovation. IGI Global; 2024. pp. 112–135. Available from: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823. doi: 10.4018/979-8-3693-3661-8.ch006.
19. Magadum PK. Machine learning for predicting wind turbine output power in wind energy conversion systems. Grenze Int J Eng Technol. 2024;10(1):2074–2080. Grenze ID: 01.GIJET.10.1.4_1. Available from: https://thegrenze.com/index.php?display=page&view=journal abstract&absid=2514&id=8.
20. Nerkar PM, Dhaware BU. Predictive data analytics framework based on Heart Healthcare System (HHS) using machine learning. J Adv Zool. 2023;44(Spec Issue 2):3673–3686.
21. Neeraja P, Kumar RG, Kumar MS, Liyakat KKS, Vani MS. DL-based somnolence detection for improved driver safety and alertness monitoring. IEEE Int Conf Comput Power Commun Technol (IC2PCT). 2024. Greater Noida, India. 2024; 589–594. Available from: https://ieeexplore. org/document/10486714. doi: 10.1109/IC2PCT60090.2024.10486714.
22. Sayyad Liyakat KK. Explainable AI in healthcare. In: Explainable Artificial Intelligence In Healthcare System. Kamaraj AA, Acharjya DP, editors. ISBN: 979-8-89113-598-7. 2024. doi: 10.52305/GOMR8163.
23. Liyakat KS. ChatGPT: An automated teacher’s guide to learning. In: Bansal R, Chakir A, Ngah H, Rabby F, Jain A, editors. AI Algorithms and ChatGPT for Student Engagement in Online Learning. IGI Global; 2024. pp. 1–20. doi: 10.4018/979-8-3693-4268-8.ch001.
24. Veena C, Sridevi M, Liyakat KKS, Saha B, Reddy SR, Shirisha N. HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. Seventh Int Conf Image Inf Process (ICIIP). Solan, India. 2023. 407–410. Available from: https://ieeexplore.ieee.org/document/10537627. doi: 10.1109/ICIIP61524.2023
25. Prasad KR, Karanam SR, Ganesh D, Liyakat KKS, Talasila V, Purushotham P. AI in public-private partnership for IT infrastructure development. J High Technol Manag Res. 2024;35(1):100496. doi: 10.1016/j.hitech.2024.100496.
26. Nagrale M, Pol RS, Birajadar GB, Mulani AO. Internet of robotic things in cardiac surgery: An innovative approach. Afr J Biol Sci. 2024;6(6):709–725. doi: 10.33472/AFJBS.6.6.2024.709-725.
27. Kazi KSL. IoT driven by machine learning (MLIoT) for the retail apparel sector. In: Tarnanidis T, Papachristou E, Karypidis M, Ismyrlis V, editors. Driving Green Marketing in Fashion and Retail. IGI Global; 2024. pp. 63–81. doi: 10.4018/979-8-3693-3049-4.ch004.
28. Kazi KSL. Machine learning (ML)-based Braille Lippi characters and numbers detection and announcement system for blind children in learning. In: Sart G, editor. Social Reflections of Human–Computer Interaction in Education, Management, and Economics. IGI Global; 2024. pp. 16–39. doi: 10.4018/979-8-3693-3033-3.ch002.
29. Kazi KSL. Artificial intelligence (AI)-driven IoT (AIIoT)-based agriculture automation. In: Satapathy S, Muduli K, editors. Advanced Computational Methods for Agri-Business Sustainability. IGI Global; 2024. pp. 72–94. doi: 10.4018/979-8-3693-3583-3.ch005.
30. Kazi K. Vehicle health monitoring system (VHMS) by employing IoT and sensors. Grenze Int J Eng Technol. 2024;10(2):5367–5374.
31. Kazi K. A novel approach on ML based palmistry. Grenze Int J Eng Technol. 2024;10(2):5186–5193.
32. Kazi K. IoT based boiler health monitoring for sugar industries. Grenze Int J Eng Technol. 2024;10(2):5178–5185.
33. Liyakat KKS. Explainable AI in healthcare. In: Explainable Artificial Intelligence in Healthcare Systems. 2024. pp. 271–284.
34. Shirdale Y, Kazi KS. Analysis and design of capacitive coupled wideband microstrip antenna in C and X band: A survey. J GSD-Int Soc Green Sustain Eng Manag. 2014;1(15):1–7.
35. Shirdale Y, Kazi K, Kazi K. Coplanar capacitive coupled probe fed micro strip antenna for C and X band. Int J Adv Res Comput Commun Eng. 2016;5(4):661–663.
36. Kazi KSL. Machine learning-based pomegranate disease detection and treatment. In: Ul Haq MZ, Ali I, editors. Revolutionizing Pest Management for Sustainable Agriculture. IGI Global; 2024. pp. 469–498. doi: 10.4018/979-8-3693-3061-6.ch019.
37. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. Review of AI in power electronics and drive systems. In: 3rd Int Conf Power Electron IoT Appl Renewable Energy Control (PARC). Mathura, India; 2024. pp. 94–99. doi: 10.1109/PARC59193.2024.10486488.
38. Patil VJ, Khadake SB, Tamboli DA, Mallad HM, Takpere SM, Sawant VA. A comprehensive analysis of artificial intelligence integration in electrical engineering. In: 5th Int Conf Mobile Comput Sustain Inform (ICMCSI). Lalitpur, Nepal; 2024. pp. 484–491. doi: 10.1109/ICMCS 2024.00076.
39. Khadake SB, Kashid PJ, Kawade AM, Khedekar SV, Mallad HM. Electric vehicle technology battery management – review. International Journal of Advanced Research in Science Communication and Technology. 2023;3(2):319–325. doi: 10.48175/ijarsct-13048.
40. Khadake S, Kawade S, Moholkar S, Pawar M. A review of 6G technologies and its advantages over 5G technology. In: Pawar PM, et al. Techno-societal 2022. ICATSA 2022. Springer, Cham; 2024. doi: 10.1007/978-3-031-34644-6_107.