Research of Scientia Naturalis
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.
Articles
60 Documents
<![CDATA[Polymers and Composites for Energy Storage Applications ]]>
<![CDATA[Mei, Chen]]>;
<![CDATA[Jing, Wang]]>;
<![CDATA[Wei, Sun]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i4.1576
<![CDATA[The increasing demand for efficient energy storage solutions has driven research into polymers and composites. These materials offer unique advantages, such as lightweight properties, flexibility, and tunable conductivity, making them ideal candidates for energy storage applications. The exploration of innovative polymers and composites is essential for improving energy density and cycle life in storage devices. This research aims to evaluate the performance of various polymers and composites in energy storage applications. The focus is on understanding their electrochemical properties and how modifications can enhance their performance in batteries and supercapacitors. A systematic review of recent advancements in polymer and composite materials was conducted, alongside experimental assessments of selected materials. Performance metrics such as conductivity, energy density, and stability were evaluated using electrochemical testing methods, including cyclic voltammetry and galvanostatic charge-discharge tests. The findings indicate that specific polymers and composites exhibit enhanced performance in energy storage applications. Notable improvements in conductivity and energy density were observed, particularly with the incorporation of conductive fillers. Additionally, the stability of the materials under cycling conditions showed promising results, suggesting their potential for practical applications.The research highlights the significant potential of polymers and composites in advancing energy storage technologies. Continued exploration and optimization of these materials can lead to the development of more efficient and durable energy storage solutions, addressing the growing demands for sustainable energy systems.]]>
<![CDATA[Inorganic Nanoparticles for Drug Delivery Systems: Design and Challenges ]]>
<![CDATA[Hasyim, Dadang Muhammad]]>;
<![CDATA[Fujita, Miku]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 4 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i4.1578
<![CDATA[Inorganic nanoparticles have gained attention in drug delivery systems due to their unique properties, including high surface area, biocompatibility, and the ability to encapsulate therapeutic agents. These characteristics make them promising candidates for enhancing drug efficacy and targeting. This research aims to explore the design parameters and challenges associated with inorganic nanoparticles in drug delivery applications. The focus is on understanding how modifications in nanoparticle design can optimize performance and address existing limitations. A comprehensive literature review was conducted alongside experimental assessments of various inorganic nanoparticle formulations. Key parameters such as size, surface charge, and drug loading capacity were evaluated to assess their impact on drug delivery efficiency. In vitro studies were performed to analyze drug release profiles and cellular uptake.The findings indicate that specific design modifications significantly influence drug delivery performance. For example, smaller nanoparticles with positive surface charges exhibited enhanced cellular uptake and higher drug loading capacities. However, challenges such as stability, scalability, and regulatory hurdles remain prevalent in the field. Inorganic nanoparticles hold great potential for advancing drug delivery systems, but addressing associated design challenges is crucial. Continued research in this area will facilitate the development of more effective and safer drug delivery solutions, ultimately improving therapeutic outcomes for patients. ]]>
<![CDATA[Quantum Computing and Its Implications for Complex System Analysis ]]>
<![CDATA[Iqbal, Kiran]]>;
<![CDATA[Ahmad, Omar]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i5.1579
<![CDATA[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.]]>
<![CDATA[Dielectric Properties of Multiferroics in Next-generation Memory Devices ]]>
<![CDATA[Nam, Le Hoang]]>;
<![CDATA[Peng, Nam]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i5.1580
<![CDATA[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.]]>
<![CDATA[Optical Materials for High-efficiency Solar Cells: A Comparative Study ]]>
<![CDATA[Demir, Ahmet]]>;
<![CDATA[Akbulut, Baran]]>;
<![CDATA[Yilmaz, Hale]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i5.1581
<![CDATA[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.]]>
<![CDATA[Advances in Thin Film Technology for Flexible Display Applications ]]>
<![CDATA[Azzahra, Fatima]]>;
<![CDATA[Ahmed, Dina]]>;
<![CDATA[Kamal, Mai]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i5.1582
<![CDATA[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.]]>
<![CDATA[The Application of Artificial Intelligence in Quantum Mechanics: Challenges and Opportunities ]]>
<![CDATA[Tu, Nguyen Minh]]>;
<![CDATA[Lan, Tran Thi]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 5 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i6.1583
<![CDATA[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.]]>
<![CDATA[The Role of Applied Statistics in Drug Development and Clinical Trials ]]>
<![CDATA[Rahmah, Sitti]]>;
<![CDATA[Fawait, Aldi Bastiatul]]>;
<![CDATA[Hasyim, Dadang Muhammad]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 6 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i6.1584
<![CDATA[Background: The integration of applied statistics in drug development and clinical trials is essential for ensuring the efficacy and safety of new pharmaceuticals. Statistical methods play a critical role in designing studies, analyzing data, and interpreting results, thereby influencing regulatory decisions and clinical practices. Objective: This study aims to examine the role of applied statistics in the drug development process, particularly within clinical trials. The focus is on identifying key statistical techniques and their impact on trial outcomes and decision-making. Methodology: A comprehensive review of literature was conducted, analyzing various statistical methods employed in clinical trials, including sample size determination, randomization techniques, and data analysis methods. Case studies were included to illustrate the application of these methods in real-world scenarios. Results: Findings indicate that robust statistical methodologies significantly improve the reliability of clinical trial results. Proper sample size calculations ensure adequate power to detect treatment effects, while randomization techniques minimize bias. Additionally, advanced data analysis methods enhance the interpretation of trial outcomes, leading to more informed regulatory approvals. Conclusion: This research highlights the indispensable role of applied statistics in drug development and clinical trials. Emphasizing the importance of sound statistical practices not only improves trial integrity but also contributes to the overall success of new drug therapies. Continued advancements in statistical methods will further enhance the efficiency and effectiveness of clinical research.]]>
<![CDATA[Mathematical Physics and the Study of Complex Quantum Systems ]]>
<![CDATA[Batubara, Ana Uzla]]>;
<![CDATA[Ali, Zainab]]>;
<![CDATA[Vandika, Arnes Yuli]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 6 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i6.1585
<![CDATA[The study of complex quantum systems is a fundamental aspect of modern physics, providing insights into the behavior of matter at microscopic scales. Mathematical physics plays a crucial role in developing the theoretical frameworks necessary for understanding these systems, yet challenges remain in applying these concepts to real-world scenarios. This research aims to investigate the application of mathematical techniques in analyzing complex quantum systems. The focus is on identifying effective mathematical models and methods that can enhance our understanding of quantum phenomena. A comprehensive literature review was conducted, analyzing various mathematical approaches utilized in quantum mechanics, including perturbation theory, group theory, and numerical simulations. Case studies were examined to illustrate the successful application of these methods in real-world quantum systems. Findings indicate that advanced mathematical techniques significantly improve the modeling and analysis of complex quantum systems. The application of perturbation theory and numerical simulations provided deeper insights into system behaviors, while group theory facilitated a better understanding of symmetry properties. This research highlights the indispensable role of mathematical physics in the study of complex quantum systems. By emphasizing the integration of mathematical techniques, the study contributes to the advancement of theoretical physics and offers pathways for future research in quantum mechanics.]]>
<![CDATA[Mathematical Biology: Modeling the Dynamics of Ecosystems and Biodiversity ]]>
<![CDATA[Akbar, Khoironi Fanana]]>;
<![CDATA[Nishida, Daiki]]>;
<![CDATA[Sitopu, Joni Wilson]]>
<![CDATA[ Research of Scientia Naturalis Vol. 1 No. 6 (2024) ]]>
Publisher : <![CDATA[Yayasan Adra Karima Hubbi ]]>
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DOI: 10.70177/scientia.v1i6.1586
<![CDATA[Background: Mathematical biology plays a crucial role in understanding the dynamics of ecosystems and biodiversity. By employing mathematical models, researchers can analyze complex biological interactions and predict changes within ecosystems over time. This approach is vital for addressing environmental challenges and informing conservation strategies. Objective: This study aims to develop mathematical models that accurately represent the dynamics of ecosystems and the factors influencing biodiversity. The focus is on identifying key interactions between species and their environment, as well as the implications of these interactions for ecosystem stability. Methodology: A combination of differential equations and computational simulations was employed to model various ecological scenarios. Data from field studies and ecological surveys were utilized to parameterize the models, allowing for realistic representations of species interactions and environmental influences. Results: Findings indicate that specific species interactions, such as predation and competition, significantly affect biodiversity and ecosystem dynamics. The models revealed thresholds beyond which ecosystems could shift to alternative stable states, emphasizing the importance of maintaining biodiversity for ecosystem resilience. Conclusion: This research highlights the value of mathematical modeling in the study of ecosystems and biodiversity. By providing insights into the intricate relationships between species and their environment, the study contributes to a better understanding of ecological dynamics and informs effective conservation strategies.]]>
