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Research of Scientia Naturalis
Published by Yayasan Adra Karima Hubbi
ISSN : 30479932     EISSN : 30479940     DOI : 10.70177/scientia
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Physics
Research of Scientia Naturalis is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the research fields of Mathematics and Natural Sciences. All publications provide breadth of coverage appropriate to a wide readership in Mathematics and Natural Sciences research depth to inform specialists in that area. We feel that the rapidly growing Research of Scientia Naturalis community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 5 (2024)" : 5 Documents clear
Quantum Computing and Its Implications for Complex System Analysis Iqbal, Kiran; Ahmad, Omar; Vandika, Arnes Yuli
Research of Scientia Naturalis Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i5.1579

Abstract

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the reach of classical computing. Its unique properties, such as superposition and entanglement, enable efficient processing of vast datasets, making it especially valuable for analyzing complex systems. This research aims to explore the implications of quantum computing for complex system analysis, particularly in fields such as physics, biology, and finance. The goal is to identify how quantum algorithms can enhance the understanding and modeling of intricate systems. A systematic literature review was conducted, examining recent advancements in quantum algorithms and their applications to complex system analysis. Comparative analyses were performed between classical and quantum computing approaches, focusing on specific case studies to illustrate the advantages of quantum solutions. The findings indicate that quantum computing significantly accelerates certain computations, leading to improved accuracy and efficiency in modeling complex systems. Case studies in quantum simulations of molecular interactions and financial modeling demonstrate substantial performance gains over classical methods. Quantum computing holds great promise for advancing the analysis of complex systems across various disciplines. Continued research and development in this area are essential to fully harness the capabilities of quantum technologies, ultimately leading to breakthroughs in understanding and solving complex problems.
Dielectric Properties of Multiferroics in Next-generation Memory Devices Nam, Le Hoang; Peng, Nam; Vandika, Arnes Yuli
Research of Scientia Naturalis Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i5.1580

Abstract

The advent of next-generation memory devices necessitates materials that exhibit superior dielectric properties. Multiferroics, materials that exhibit simultaneous ferroelectric and magnetic ordering, have emerged as promising candidates for enhancing memory device performance due to their unique attributes. This study aims to investigate the dielectric properties of various multiferroic materials and their implications for next-generation memory applications. The focus is on understanding how these properties can be optimized to improve device efficiency and functionality. A series of multiferroic samples were synthesized using sol-gel and solid-state methods. Dielectric measurements were conducted over a range of frequencies and temperatures to characterize their dielectric constant, loss tangent, and temperature dependence. Comparative analyses with traditional dielectric materials were performed to evaluate performance. The findings reveal that specific multiferroic materials exhibit significantly enhanced dielectric properties compared to conventional dielectrics. Notable improvements in dielectric constant and reduced loss tangent were observed, indicating potential for better energy storage and lower power consumption in memory devices. The research demonstrates that multiferroics possess advantageous dielectric properties that can be harnessed for next-generation memory devices. Continued exploration of these materials is essential for advancing memory technology and developing more efficient, high-performance devices in the future.
Optical Materials for High-efficiency Solar Cells: A Comparative Study Demir, Ahmet; Akbulut, Baran; Yilmaz, Hale
Research of Scientia Naturalis Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i5.1581

Abstract

The demand for renewable energy sources has accelerated research into high-efficiency solar cells. Optical materials play a critical role in enhancing light absorption and overall energy conversion efficiency. Understanding the properties and performance of various optical materials is essential for optimizing solar cell technology. This study aims to compare different optical materials used in solar cells to evaluate their effectiveness in maximizing solar energy conversion. The focus is on identifying materials that offer superior optical characteristics and compatibility with existing solar cell technologies. A comparative analysis was conducted on several optical materials, including silicon dioxide (SiO2), titanium dioxide (TiO2), and organic polymers. The study involved synthesizing these materials and assessing their optical properties using UV-Vis spectroscopy and photoluminescence measurements. Efficiency tests were performed on solar cell prototypes incorporating these materials. The findings reveal that titanium dioxide exhibited the highest light absorption and photonic efficiency compared to silicon dioxide and organic polymers. Solar cells utilizing TiO2 demonstrated a significant increase in overall efficiency, achieving conversion rates of up to 22%. In contrast, organic polymers showed lower performance but offered advantages in flexibility and lightweight applications. This research highlights the importance of selecting appropriate optical materials to enhance solar cell efficiency. Titanium dioxide emerges as a leading candidate for high-performance solar cells, while organic polymers may provide alternative solutions for specific applications. Continued exploration of optical materials will be crucial for advancing solar technology and meeting global energy demands.
Advances in Thin Film Technology for Flexible Display Applications Azzahra, Fatima; Ahmed, Dina; Kamal, Mai
Research of Scientia Naturalis Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i5.1582

Abstract

The rapid growth of flexible display technologies has spurred significant advancements in thin film technology. These innovations are crucial for developing lightweight, durable, and versatile display solutions that can be integrated into various applications, from consumer electronics to wearable devices. This study aims to investigate recent advancements in thin film technologies specifically tailored for flexible display applications. The focus is on identifying key materials and fabrication techniques that enhance performance and flexibility. A comprehensive review of current literature was conducted, analyzing various thin film materials, including organic light-emitting diodes (OLEDs), organic photovoltaics (OPVs), and flexible substrates. The performance metrics of these materials were evaluated based on criteria such as flexibility, transparency, and electrical conductivity. The findings reveal that the integration of novel materials, such as graphene and silver nanowires, significantly improves the electrical and mechanical properties of thin films. Enhanced flexibility and durability were observed in displays utilizing these advanced materials, leading to improved performance in real-world applications. This research highlights the critical role of thin film technology in advancing flexible display applications. The integration of innovative materials and techniques is essential for overcoming current limitations, paving the way for the next generation of flexible and efficient display solutions. Continued exploration in this field will drive further innovations and expand the potential applications of flexible displays.
The Application of Artificial Intelligence in Quantum Mechanics: Challenges and Opportunities Tu, Nguyen Minh; Lan, Tran Thi; Vandika, Arnes Yuli
Research of Scientia Naturalis Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1583

Abstract

The intersection of artificial intelligence (AI) and quantum mechanics represents a frontier of scientific exploration, offering the potential to revolutionize our understanding of quantum systems. Despite the promise, significant challenges remain in effectively integrating AI techniques within quantum mechanics frameworks. This study aims to investigate the applications of AI in quantum mechanics, identifying both the challenges and opportunities presented by this interdisciplinary approach. The focus is on understanding how AI can enhance quantum simulations, optimize computations, and improve experimental designs. A comprehensive literature review was conducted, analyzing recent advancements in AI algorithms applied to quantum mechanics. Case studies were examined to illustrate successful implementations and the limitations encountered. Key metrics for evaluation included computational efficiency, accuracy, and scalability. Findings indicate that AI techniques, particularly machine learning and neural networks, can significantly expedite quantum simulations and enhance predictive accuracy. However, challenges such as data sparsity, interpretability of AI models, and the integration of AI with quantum algorithms were identified as significant barriers to progress. This research highlights the transformative potential of AI in advancing quantum mechanics while acknowledging the inherent challenges. Addressing these challenges will require collaborative efforts across disciplines, paving the way for innovative solutions that leverage AI to deepen our understanding of quantum phenomena and improve technological applications.

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