Simulation of Magnetic Anisotropy Energy Surfaces for Cubic Crystals Using MATLAB in Remote Material Physics Lectures

Authors

  • Himawan Putranta Department of Educational Sciences, Concentration of Physics Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia
  • Heru Kuswanto Department of Educational Sciences, Concentration of Physics Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia
  • Aditya Yoga Purnama Department of Educational Sciences, Concentration of Physics Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia
  • Syella Ayunisa Rani Department of Educational Sciences, Concentration of Physics Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia
  • Sabaruddin Department of Educational Sciences, Concentration of Physics Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta 55281, Indonesia

DOI:

https://doi.org/10.48048/tis.2022.5818

Keywords:

Anisotropy energy, Cubic crystals, Magnetic, MATLAB, Visualization

Abstract

The physical characteristics of a crystal can be viewed from the distribution of magnetic energy contained in it so that a simulation is needed which helps in visualizing the magnetic energy of the crystals. This study aims to present a simple solution for constructing a simulation of the physical properties of cubic crystals using MATLAB software. MATLAB can also be used to help organize physics lectures remotely. This is because MATLAB can be operated by anyone, anytime, and anywhere without being connected to the internet network to help visualize the material magnetization concept in the form of 2- and 3-dimensional graphics. The simulation aims to visualize the magnetic anisotropy energy surface for cubic crystals. This simulation can visualize the magnetic anisotropy energy surface of 3 types of cubic crystals, namely simple cubic, body-centered cubic, and face-centered cubic. In this simulation, the difference between the 3 types of crystals is the anisotropy energy constant. The mathematical equations in the magnetic anisotropy energy surface for cubic crystals are quite complicated. This simulation can help students in exploring mathematical equations and studying the magnetic anisotropy energy surface for cubic crystals more. Students can also develop mathematical representation skills and creative visual representations of the magnetic anisotropy energy surface for cubic crystals.

HIGHLIGHTS

  • The uniaxial anisotropy energy equation is closely related to helping to realize the behavior of materials
  • Visualization of magnetic anisotropy energy surfaces in a crystal is influenced by the shape of the crystal and the value of its anisotropy constant
  • Physics lectures during the Covid-19 pandemic require a variety of lectures that utilize the computational program-based physics equation simulation media
  • Magnetic anisotropy energy surfaces on a crystal can be visualized through the help of computational programs to support physics lectures during the Covid-19 pandemic
  • MATLAB can be used to visualize magnetic anisotropy energy surfaces because it can be operated by anyone, anytime, and anywhere without being connected to the internet network


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References

M Long, P Wang, H Fang and W Hu. Progress, challenges, and opportunities for 2D material-based photodetectors. Adv. Funct. Mater. 2019; 29, 180-6.

M Fang, Z Yu, and W Zhang. Dynamic analysis of material motion using MATLAB/Simulation environment and test verification. Adv. Mech. Eng. 2020; 12, 168-74.

X Zhao, Y Cui, Z Guo and Z Hao. An energy-efficient coverage enhancement strategy for wireless sensor networks based on a dynamic partition algorithm for cellular grids and an improved Vampire bat optimizer. Sensors 2020; 20, 619-28.

W Huang, H Ji, J Qiu and L Cheng. Wave energy focalization in a plate with imperfect two-dimensional acoustic black hole indentation. J. Vibra. Acoust. 2016; 138, 178-89.

N Balta. A new approach to solving physics problems related to freely falling objects. Phys. Edu. 2018; 53, 55-62.

BM Notaroš, R McCullough, SB Manić and AA Maciejewski. Computer‐assisted learning of electromagnetics through MATLAB programming of electromagnetic fields in the creativity thread of an integrated approach to electrical engineering education. Comp. Appl. Eng. Edu. 2019; 27, 271-87.

D Marti, RN Aasbjerg, PE Andersen and AK Hansen. MCMATLAB: An open-source, user-friendly, MATLAB-integrated three-dimensional Monte Carlo light transport solver with heat diffusion and tissue damage. J. Biomed. Opt. 2018; 23, 121-7.

J Lämsä, R Hämäläinen, P Koskinen and J Viiri. Visualizing the temporal aspects of collaborative inquiry-based learning processes in technology-enhanced physics learning. Int. J. Sci. Edu. 2018; 40, 1697-717.

TL Manh, F Caleyo, JM Hallen, JH Espina-Hernández and JA Pérez-Benitez. Model for the correlation between magnetocrystalline energy and Barkhausen noise in ferromagnetic materials. J. Magn. Magn. Mater. 2018; 454, 155-64.

N Pushpavathi, KL Sandhya and SK Prasad. Effect of graphene flakes, titanium dioxide, and zinc oxide nanoparticles on the birefringence, I-V characteristics, and photoluminescence properties of liquid crystal. J. Molec. Liq. 2020; 302, 112-8.

EK Wati and N Widiansyah. Design of learning media: Modeling & simulation of building thermal comfort optimization system in building physics course. J. Indonesian Sci. Edu. 2010; 9, 257-66.

S Heitmann, MJ Aburn, and M Breakspear. The brain dynamics toolbox for MATLAB. Neurocomp. 2018; 315, 82-8.

HAJ AL-Ziarjawey and I Çankaya, Heart rate monitoring and PQRST detection based on the graphical user interface with MATLAB. Int. J. Inf. Elec. Eng. 2015; 5, 311-25.

AMS Mahdy, MS Mohamed, KA Gepreel, A AL-Amiri and M Higazy. Dynamical characteristics and signal flow graph of a nonlinear fractional smoking mathematical model. Cha. Solit. Fract. 2020; 141, 110-6.

TOB Odden, E Lockwood and MD Casphereero. Physics computational literacy: An exploratory case study using computational essays. Phys. Rev. Phys. Edu. Res. 2019; 15, 201-8.

M Holt, D Gillen, SD Nandlall, K Setter, P Thorman, SA Kane and C Supalo. Making physics courses accessible for blind students: Strategies for course administration, class meetings, and course materials. Phys. Teac. 2019; 57, 94-8.

AK Salkeeva, AS Kusenova, KB Kopbalina, GB Turebaeva and AY Davydova. Using the application programs for the decision of tasks on physics. Asian J. Res. Comp. Sci. 2020; 75, 27-35.

A Septian, RF Suwarman, E Monariska, and R Sugiarni. Somatic, auditory, visualization, intellectually learning assisted by GeoGebra to improve student's mathematical representation skills. J. Phys.: Conf. Series. 2020; 1657, 120-6.

ZR Ridlo, IK Mahardika, IWA Terra and E Wardani. Student profile multiple representations skills under the implementation of the OBSIM (observation and simulation) model of teaching in improving creative thinking skills. J. Phys.: Conf. Series. 2020; 1563, 126-34.

S Sulasteri, NN Sari and A Sriyanti. The development of teaching materials for subjects of numerical method assisted by MATLAB software in mathematics education department students. J. Phys.: Conf. Series. 2020; 1539, 128-36.

RS Pierce, BG Falzon and MC Thompson. A multi-physics process model for simulating the manufacture of resin-infused composite aerostructures. Compo. Sci. Tech. 2017; 149, 269-79.

AH Ali, HS Hamad and AA Abdulrazzaq. Performance investigation of grid-connected photovoltaic system modeling based on MATLAB simulation. Int. J. Elec. Comp. Eng. 2018; 8, 4847-53.

Q Xing and Y Li. Analysis of single-slit diffraction experiment based on MATLAB simulation. J. Phys.: Conf. Series. 2020; 1622, 129-37.

E Momox and C Ortega De Maio. Computer-based learning in an undergraduate physics course: Interfacing a mobile phone and MATLAB to study oscillatory motion. Americ. J. Phys. 2020; 88, 535-41.

D Lu, M Ramzan, N Ullah, JD Chung and U Farooq. Numerical treatment of radiative nanofluid 3D flow containing gyrotactic microorganisms with anisotropic slip, binary chemical reaction, and activation energy. Sci. Rep. 2017; 7, 1-22.

J Ye, W He, Q Wu, HL Liu, XQ Zhang, ZY Chen and ZH Cheng. Determination of magnetic anisotropy constants in Fe ultrathin film on vicinal Si (111) by anisotropic magnetoresistance. Sci. Rep. 2013; 3, 1-6.

SA Khan, P Blaha, H Ebert, J Minár and O Šipr. Magnetocrystalline anisotropy of FePt: A detailed view. Phys. Rev. B. 2016; 94, 144-9.

S Zhu, J Fu, H Li, L Zhu, Y Hu, W Xia and J Zhang. Direct observation of magnetocrystalline anisotropy tuning magnetization configurations in uniaxial magnetic nanomaterials. ACS Nano. 2018; 12, 3442-48.

R Persico and M Pieraccini. Measurement of dielectric and magnetic properties of materials using a TDR probe: a preliminary theoretical investigation in the frequency domain. Near Surf. Geophys. 2018; 16, 118-26.

X Li, M Sun, C Shan, Q Chen and X Wei. Mechanical properties of 2D materials studied by in situ microscopy techniques. Adv. Materials Interf. 2018; 5, 170-8.

RJ Harrison, X Zhao, P Hu, T Sato, D Heslop, AR Muxworthy and AP Roberts. Simulation of remanent, transient, and induced FORC diagrams for interacting particles with uniaxial, cubic, and hexagonal anisotropy. J. Geophys. Res.: Sol. Earth. 2019; 124, 12404-29.

R Moreno, S Poyser, D Meilak, A Meo, S Jenkins, VK Lazarov and RF Evans. The role of faceting and elongation on the magnetic anisotropy of magnetite Fe3O4 nanocrystals. Scie. Reports. 2020; 10, 1-14.

V Barwal, S Husain, N Behera, E Goyat and S Chaudhary. Growth dependent magnetization reversal in Co2MnAl full Heusler thin alloy films. J. Appl. Phys. 2018; 123, 539-45.

RF Evans, U Atxitia and RW Chantrell. Quantitative simulation of temperature-dependent magnetization dynamics and equilibrium properties of elemental ferromagnets. Phys. Rev. B. 2015; 91, 144-52.

AC Gandhi, R Das, FC Chou and JG Lin. Magnetocrystalline two-fold symmetry in CaFe2O4 single crystal. J. Phys.: Cond. Matt. 2017; 29, 175182.

MA McGuire, H Cao, BC Chakoumakos and BC Sales. Symmetry-lowering lattice distortion at the spin reorientation in MnBi single crystals. Phys. Rev. B. 2014; 90, 174-83.

D Li, X Zhang, J Li, L Zhao, F Wang, and X Chen. Insight into the elastic anisotropy and thermodynamics properties of Tantalum borides. Vacuum 2019; 169, 108-14.

A Khalfallah, JL Alves, MC Oliveira and LF Menezes. Influence of the characteristics of the experimental data set used to identify anisotropy parameters. Simul. Model. Pract. Theo. 2015; 53, 15-44.

JE Hasbun and T Datta. Introductory solid-state physics with MATLAB applications. CRC Press, 2019.

S Mukherjee, J Gomis-Bresco, P Pujol-Closa, D Artigas and L Torner. Topological properties of bound states in the continuum in geometries with broken anisotropy symmetry. Phys. Rev. A. 2018; 98, 638-45.

Y Chen, W Huang, D Zhang and W Chen. An open-source MATLAB code package for improved rank-reduction 3D seismic data denoising and reconstruction. Comp. Geosci. 2016; 95, 59-66.

M Winklhofer, L Chang, and SH Eder. On the magnetocrystalline anisotropy of greigite (Fe3S4). Geochem. Geophys. Geosys. 2014; 15, 1558-79.

M Sadek, M Marchwiany, M Woinska, A Majhofer, J Gosk, A Twardowski and J Szczytko. Effect of the sign of anisotropy constants on the properties of the system of interacting ferromagnetic nanoparticles. Acta Phys. Polon. A. 2016; 129, 189-90.

AA McGhie, C Marquina, K O'Grady and G Vallejo-Fernandez. Measurement of the distribution of anisotropy constants in magnetic nanoparticles for hyperthermia applications. J. Phys. D: Appl. Phys. 2017; 50, 455-63.

K Enpuku, A L Elrefai, T Yoshida, T Kahmann, J Zhong, T Viereck and F Ludwig. Estimation of the effective magnetic anisotropy constant of multi-core based magnetic nanoparticles from the temperature dependence of the coercive field. J. Appl. Phys. 2020; 127, 133-9.

CF Pai, M Mann, AJ Tan and GS Beach. Determination of spin-torque efficiencies in heterostructures with perpendicular magnetic anisotropy. Phys. Rev. B. 2016; 93, 144-8.

RF Evans, WJ Fan, P Chureemart, TA Ostler, MO Ellis and RW Chantrell. Atomistic spin model simulations of magnetic nanomaterials. J. Phys.: Cond. Matt. 2014; 26, 103-9.

J Mejía-López, A Mejía-López and J Mazo-Zuluaga. Uniaxial magnetic anisotropy energy of bimetallic Co-Ni clusters from a first-principles perspective. Phys. Chem. Chem. Phys. 2018; 20, 16528-39.

SN Sahu, PK Baskey, SD Barma, S Sahoo, BC Meikap and SK Biswal. Pelletization of synthesized magnetite concentrate obtained by magnetization roasting of Indian low-grade BHQ iron ore. Powder Tech. 2020; 374, 190-200.

F Matsukura, Y Tokura and H Ohno. Control of magnetism by electric fields. Nature Nanotech. 2015; 10, 209-20.

S Bhatti, R Sbiaa, A Hirohata, H Ohno, S Fukami and SN Piramanayagam. Spintronics based random access memory: A review. Materials Today. 2017; 20, 530-48.

B Novak, B Housen, Y Kitamura, T Kanamatsu and K Kawamura. Magnetic fabric analyses as a method for determining sediment transport and deposition in deep-sea sediments. Marine Geolog. 2014, 356, 19-30.

A Langenberg, K Hirsch, A Ławicki, V Zamudio-Bayer, M Niemeyer, P Chmiela and JT Lau. Spin and orbital magnetic moments of size-selected iron, cobalt, and nickel clusters. Phys. Rev. B. 2014; 90, 184-93.

RJ Harrison and JM Feinberg. Mineral magnetism: Providing new insights into geoscience processes. Elements 2009; 5, 209-15.

B Tudu and A Tiwari. Recent developments in perpendicular magnetic anisotropy thin films for data storage applications. Vacuum 2017; 146, 329-41.

M Baran, A Maskan and S Yasar. Learning physics through project-based learning game techniques. Int. J. Instruc. 2018; 11, 221-234.

YS Meng, SD Jiang, BW Wang and S Gao. Understanding the magnetic anisotropy toward single-ion magnets. Account. Chem. Res. 2016; 49, 2381-9.

S Schwedler and M Kaldewey. Linking the sub-microscopic and symbolic level in physical chemistry: How voluntary simulation-based learning activities foster first-year university students' conceptual understanding. Chem. Edu. Res. Pract. 2020; 21, 1132-47.

Y Daineko, V Dmitriyev and M Ipalakova. Using virtual laboratories in teaching natural sciences: An example of physics courses in the university. Comp. Appl. Eng. Edu. 2017; 25, 39-47.

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Published

2022-08-30

How to Cite

Putranta, H. ., Kuswanto, H. ., Purnama, A. Y. ., Rani, S. A. ., & Sabaruddin, S. (2022). Simulation of Magnetic Anisotropy Energy Surfaces for Cubic Crystals Using MATLAB in Remote Material Physics Lectures. Trends in Sciences, 19(18), 5818. https://doi.org/10.48048/tis.2022.5818