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Manoel Souza1, Bento Cabral Jr.1, Gustavo Dias1, Luiz Cótica1, Ivair Santos*1
1Department of Physics, State University of Maringa, Maringá, Paraná, Brazil

Abstract Body: High entropy oxides (HEO) are characterized by presenting crystallinity (rather than amorphization, phase separation or segregation), are single-phase and show significant configurational disorder due to multiple elements inhabiting the same crystallographic site in the crystal structure. On the other side, bismuth ferrite (BFO) is a room temperature rhombohedral (R3c) perovskite structured (ABO3) multiferroic magnetoelectric material that, due to its simultaneous ferroelectric and antiferromagnetic ordering (at room temperature), is one of the most promising candidates for applications in technologically advanced devices. In this way, BFO processed as a HEO can present unique characteristics in the context of investigations of disordered systems and multifunctional compounds. In this paper contribution, high-energy ball cryo-milling and quenching protocols were applied for processing high-entropy BFO nanopowders, resulting in materials with unusual ferroic-related properties that differs from that widely reported for bulk BFO. The role of the configurational disorder on the overall physical properties in pointed out and discussed.

Eduardo Volnistem1, Roger Oliveira2, Gustavo Dias1, Luiz Cótica1, Ivair Santos*1
1Department of Physics, State University of Maringá, Maringá - PR, Brazil; 2PFI, State University of Maringá, Maringá, Paraná, Brazil

Abstract Body: Bismuth ferrite (BFO) is a room temperature rhombohedral (R3c) perovskite structured (ABO3) multiferroic magnetoelectric material that, due to its simultaneous ferroelectric and antiferromagnetic ordering (at room temperature), is one of the most promising candidates for applications in technologically advanced devices. However, and from the magnetic point of view, the presence of a magnetic spin-cycloid with a period of 62 nm suppresses the long-range magnetic response, preventing BFO practical applications. Several strategies can be applied to suppress or break the spin-cycloid to release the BFO magnetization, as doping/substitution of iso or heterovalent ions in specific cationic sites or, alternatively, nanostructuration. In this paper contribution, high-energy ball cryo-milling was applied for synthesizing BFO nanopowders with controlled crystallite sizes and internal strain, resulting in an unusual non-linear and enhanced magnetic response that differs from that widely reported for bulk BFO. The role of the generated dislocations (line defects) on the magnetic properties of multiferroic magnetoelectric nanoparticles is discussed taking the processed BFO nanoparticles as a study model. It is pointed out that the control of the dislocation density can be used as an efficient tool to control physical properties and tailor specific technological applications, mainly the magnetic ones.

Luiz Cótica*1, Hugo Machado1, Victor Vizcarra Ruiz1, Gustavo Dias1, Ivair Santos1, Valdirlei Freitas2, Ruyan Guo3, Amar Bhalla3
1Department of Physics, State University of Maringa, Maringá, Paraná, Brazil; 2Physics, Universidade Estadual do Centro-Oeste - Unicentro, Guarapuava, Paraná, Brazil; 3University of Texas‚ San Antonio, San Antonio, Texas, United States

Abstract Body: This work presents an approach to the study of materials properties by exploring machine learning metodologies. The study begins with compiling a database of a data bank containing crystal structures of various materials, focusing on the Perovskite structure and ferroelectric materials. Another goal of this research is to recognize the capabilities of machine learning to enhance our understanding and prediction of ferroelectric properties, such as electric polarization and dielectric properties, in perovskite materials. Our investigation involves a systematic exploration of different machine learning algorithms and neural network models, specifically focusing on their relevance to materials science. The data-driven machine learning models used in this study are tailored to predict the properties of ferroelectric materials, delivering results that that exhibit remarkable proximity to experimental observations. Additionally, our research focuses on the optimization of structure parameter calculations, improving the precision of predictions by refining the underlying model parameters. By integrating machine learning techniques with advanced materials physics, we aim to present a new methodology to the design and understanding of ferroelectric perovskite materials, potentially accelerating their practical applications.

Victor Vizcarra Ruiz1, Hugo Machado2, Ruyan Guo3, Amar Bhalla3, Luiz Cótica*1
1Department of Physics, State University of Maringa, Maringá, Paraná, Brazil; 2Departamento de Física, Universidade Estadual de Maringá, Maringá, Paraná, Brazil; 3University of Texas‚ San Antonio, San Antonio, Texas, United States

Abstract Body: The use of machine learning to study new materials is a promising strategy for predicting properties of various materials, particularly the ferroelectrics. This research explores the use of different neural network algorithms in machine learning to develop innovative models for accurate prediction of properties in ferroelectric materials. The investigation conducts a comprehensive exploration of various neural network architectures to enhance predictive capabilities. These models are designed to decipher complex patterns and relationships from large datasets, ensuring accurate property predictions. The neural network models derived from this study open new pathways, expanding the limits of our comprehension of ferroelectric materials. To fulfill the purpose of this study, and shows the predictive advantage of neural networks, accurate estimations of electric polarization and dielectric properties for ferroelectric perovskites were conducted. By bridging the gap between machine learning and ferroelectric material science, our research not only deepens our understanding of these unique materials but also facilitates rapid materials design and discovery.

Lilian Pereira1, Julio Pastoril1, Gustavo Dias1, Ivair Santos1, Ruyan Guo2, Amar Bhalla2, Luiz Cótica*1
1Department of Physics, State University of Maringa, Maringá, Paraná, Brazil; 2University of Texas‚ San Antonio, San Antonio, Texas, United States

Abstract Body: Magnetic field sensors have become essential in various technological applications, and the magnetoelectric effect has emerged as a promising mechanism for high-sensitivity sensor development. However, the achievement of magnetoelectric sensors, especially those based on sintered magnetoelectric composites, has faced challenges, primarily related to their high electrical conductivity, which limits their technological exploration. This study addresses these issues through the innovative synthesis of cobalt ferrite nanoparticles/lead zirconate titanate (PZT) fibers/polymer composites. The characterization of these materials employed scanning electron microscopy to visualize their microstructure and dielectric measurements to assess their electrical properties. Magnetoelectric measurements were utilized to investigate the unique magnetoelectric effect exhibited by the composites. The applied magnetic fields generate resulting electric responses, demonstrating the feasibility and functionality of magnetoelectric sensors.

Gabriel Colombo*2, Ruan Vieira2, Fernando Rodrigues1, Ivair Santos2, Gustavo Dias2, Luiz Cotica2
1Depart of Physics, State University of Maringa, Maringa, Brazil; 2Department of Physics, State University of Maringa, Maringá, Paraná, Brazil

Abstract Body: The development of advanced nanomaterials for biomedical applications has garnered significant attention in materials physics. This work focuses on the synthesis, characterization, and multifunctional applications of magnetite-based nanoparticles, nanorings, and nanotubes, specifically designed for biocompatible drug delivery and cancer treatment via magnetic hyperthermia. The successful synthesis of magnetite nanostructures was confirmed by X-ray diffraction (XRD) and Transmission Electron Microscopy (TEM) techniques. The XRD analysis demonstrates the crystalline nature of the synthesized nanoparticles, while TEM reveals their distinct morphological features. To evaluate the magnetic properties of the nanomaterials, magnetization as a function of temperature measurements were conducted. These measurements revealed the superparamagnetic behavior of the magnetite nanorings and nanotubes, an essential characteristic for controlled drug delivery and hyperthermia applications. Furthermore, Magnetic Hyperthermia experiments were performed using the magnetite nanostructures, demonstrating their potential for localized cancer treatment. The efficient heat generation under the influence of an external magnetic field at clinically relevant frequencies showcases their promise in cancer therapy via magnetic hyperthermia.