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All Journal International Journal of Engineering, Science and Information Technology
Kadao, Anjali Krushna
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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AI-driven Quantum Dot Transistors for Ultra-Low Power Computing Mishra, Archana; Kadao, Anjali Krushna; Rohilla, Shruti
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1350

Abstract

Since Quantum Dot Transistors (QDTs) provide a transformative approach to ultra-low power computing, yet their optimization is an open problem, a proposed paradigm shift in computing is used as an example application context for creating new processors. The framework of this research is an AI-based approach to dynamically improve the QDT's efficiency and flexibility using reinforcement learning and neuromorphic AI. The intelligent tuning mechanism proposed uses a sample-as-a-service approach to optimize charge transport and lower leakage currents, as well as minimize energy dissipation according to real-time workload. To precisely control and self-adjust from transistor behavior to a varying environmental condition, it integrates a hybrid quantum-classical AI model. Furthermore, the mechanism adopts self-healing features to autonomously reconfigure transistor networks when anomalies are encountered, ensuring fault tolerance and extending device longevity. Simulations are used to validate the proposed methodology, which is shown to improve power efficiency, switching speed, and operational stability a great deal versus conventional low-power transistors. This work takes most of the power of QDTs for next-generation energy-efficient electronics such as IoE, edge computing, and neuromorphic processors by leveraging AI-driven optimization. Their findings provide significant contributions in the emerging field of AI-assisted semiconductor technology toward developing a scalable and intelligent method for designing ultra-low power devices. Future advancements in sustainable computing lie in the performance improvements while decreasing the digital system’s environmental footprint that this research enables.
AI-Assisted 3D-Printed Biomaterial Supercapacitors for Green Energy Storage Kadao, Anjali Krushna; Prashant, Patil Manisha; Sardana, Sunaina
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1361

Abstract

Advancements of biomaterial-based supercapacitors have been fuelled by the growing demand for sustainable and high-performance energy storage solutions. This work suggests the use of artificial intelligence to develop an AI-assisted 3D printed biomaterial supercapacitor, namely comprising electrode materials optimised by artificial intelligence (AI), bio-based electrolytes, and intelligent performance monitoring to increase efficiency and sustainability. It is an AI-driven approach that selects and optimises the biomaterials: high conductivity, low internal resistance, and excellent charge retention. Porous electrodes can be deliberately engineered on microscales by advanced 3D printing techniques; these perform well in facilitating fast ion diffusion and high energy storage capacity. This is achieved through experimental results of a 45% increase in capacitance, 68% reduction in charge transfer resistance, and 18% improvement in cycle stability on conventional supercapacitors. Moreover, AI-powered predictive maintenance increases the life of the device by 60%, thereby reducing unplanned failure by 60%. The involvement of biodegradable and non-toxic inclusion of materials encourages environmental sustainability, and thus, this supercapacitor is a green alternative for next-generation energy storage applications. This solution is suitable for wearable electronics, renewable energy systems, as well as smart devices, with high efficiency, low environmental impact and intelligent monitoring capability. The energy storage technology presents instances where AI, biomaterials, and 3D printers have the potential to transform the energy storage technology into a scalable, eco-friendly, and intelligent supercapacitor for future energy demands, according to this study.
Co-Authors Mishra, Archana Prashant, Patil Manisha Rohilla, Shruti Sardana, Sunaina