Magnetic properties of M-type strontium ferrites with different heat treatment conditions

Oh, Namji; Park, Seungyeon; Kwon, Hyuk-Min; Kim, Sang-Sub; Lim, Kyoung Mook

doi:10.1007/s12598-019-01251-0

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Equations (1–3) were used to calculate the lattice parameters ( a and c ), unit cell volume ( V cell ) and crystallite size ( D c ) of the samples. In these relations, the values of

θ, λ, β, {normald}_{k h l}

and hkl represent peak position, wavelength of radiation, integral breadth, distance of crystal face and Miller indices respectively [ 2,29 ]

\frac{1}{d_{h k l}^{2}} = \frac{4}{3} (\frac{h^{2} + h k + k^{2}}{a^{2}}) + \frac{l^{2}}{c^{2}}

V_{cell} = \frac{\sqrt{3} a^{2} c}{2}

{normalD}_{normalc} = 0 . 9 λ / β c o s θ

…”

Section: Methodsmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

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Numerous studies have been performed to modify ferrites to achieve the desired magnetic properties for the intended applications. Many variables with interactions between themselves are involved in modifying these properties. This study aims to provide an accurate and effective method for predicting the magnetic properties of M‐type ferrites under the influence of involved variables. For this purpose, ferrites are synthesized by doping 17 different ions and the results of the analysis of samples form the experimental data. The support vector regression (SVR) model is selected for prediction and in order to optimize hyper parameters, genetic, particle swarm optimization, and sequential minimal optimization techniques are used. Based on the comparison between the outcomes of these algorithms, the model with the best performance is used to predict coercivity and residual magnetism in M‐type magnetic ferrites. Furthermore, with the cross validation technique, the accuracy and robustness of the model designed for new samples are evaluated. The results show that the selected SVR model, with an average absolute relative error of 2% and 0.7%, is able to predict coercivity and residual magnetism, respectively. Consequently, the prediction method presented has the capability to accelerate and develop the modification of magnetic properties for different applications.

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“…Equations (1–3) were used to calculate the lattice parameters ( a and c ), unit cell volume ( V cell ) and crystallite size ( D c ) of the samples. In these relations, the values of

θ, λ, β, {normald}_{k h l}

and hkl represent peak position, wavelength of radiation, integral breadth, distance of crystal face and Miller indices respectively [ 2,29 ]

\frac{1}{d_{h k l}^{2}} = \frac{4}{3} (\frac{h^{2} + h k + k^{2}}{a^{2}}) + \frac{l^{2}}{c^{2}}

V_{cell} = \frac{\sqrt{3} a^{2} c}{2}

{normalD}_{normalc} = 0 . 9 λ / β c o s θ

…”

Section: Methodsmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

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show abstract

“…The sample is examined using an X-ray diffractometer to determine the size of the crystallites. Since the c/a ratio is smaller than 3.98, the ferrite is of the M-Type [22]. High-intensity peaks are visible in the XRD pattern at (107) and (114).…”

Section: Xrd Analysismentioning

confidence: 99%

“…Where, dhkl is the crystal face distance and the related Miller indices are h, k, and l [22,25], [26]. The calculated cell parameters are: a = b = 5.869 Å, c = 23.007 Å, volume V = 686 Å 3 and ratio c/a = 3.9.…”

Section: Fig 2 Xrd Pattern Of M-srfe Npsmentioning

confidence: 99%

Single-Domain Hard Ferromagnetic M-SrFe Nanoparticles for Magnetic Data Storage

Rani,

Radhika,

Padma

2024

JMNM

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Single-domain M-type Strontium Ferrite nanoparticles are prepared by the co-precipitation method. The crystallite size of the M-SrFe Nps is 58.1 nm, as determined by the XRD pattern. The SEM micrographs reveal the hexagonal morphology. M-SrFe Nps is depicted in EDS analysis. According to VSM characterization, the sample is a hard magnetic material with high coercivity. With its outstanding magnetic characteristics, hexaferrite is typically employed in permanent magnetic materials and recording devices. The band gap energy is determined to be 1.95 eV from the UV-DRS reflectance data using the Kubelka-Munk plot. The absorbed wavelength with the highest intensity peak in PL analysis is 629.9 nm. The TG-DTA investigations support the remarkable thermal stability of M-SrFe Nps. The resistivity of the sample, 0.312 Ωm is calculated using the four-probe method.

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“…Artificial FM/FE multiferroic heterostructures are of great potential for devices due to their strong magnetoelectric (ME) coupling, that is, E-field regulates magnetism or vice versa. Various characteristics related to magnetics have been dominated by E-field in the multiferroic system, such as Curie temperature (T C ) [18][19][20], magnetic domain [20][21][22], magnetoresistance [23][24][25], and ferromagnetic resonance (FMR) [16,26].…”

Section: Introduction mentioning

confidence: 99%

Modulation of spin dynamics in Ni/Pb(Mg1/3Nb2/3)O3-PbTiO3 multiferroic heterostructure

Wang

et al. 2022

J Adv Ceram

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Motivated by the fast-developing spin dynamics in ferromagnetic/piezoelectric structures, this study attempts to manipulate magnons (spin-wave excitations) by the converse magnetoelectric (ME) coupling. Herein, electric field (E-field) tuning magnetism, especially the surface spin wave, is accomplished in Ni/0.7Pb(Mg1/3}Nb2/3})O3—0.3PbTiO3 (PMN—PT) multiferroic heterostructures. The Kerr signal (directly proportional to magnetization) changes of Ni film are observed when direct current (DC) or alternative current (AC) voltage is applied to PMN—PT substrate, where the signal can be modulated breezily even without extra magnetic field (H-field) in AC-mode measurement. Deserved to be mentioned, a surface spin wave switch of “1” (i.e., “on”) and “0” (i.e., “off”) has been created at room temperature upon applying an E-field. In addition, the magnetic anisotropy of heterostructures has been investigated by E-field-induced ferromagnetic resonance (FMR) shift, and a large 490 Oe shift of FMR is determined at the angle of 45° between H-field and heterostructure plane.

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Magnetic properties of M-type strontium ferrites with different heat treatment conditions

Cited by 15 publications

References 26 publications

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Single-Domain Hard Ferromagnetic M-SrFe Nanoparticles for Magnetic Data Storage

Modulation of spin dynamics in Ni/Pb(Mg1/3Nb2/3)O3-PbTiO3 multiferroic heterostructure

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