Structural and magnetic properties of nanocrystalline BaFe12O19 synthesized by microwave-hydrothermal method

Sadhana, K.; Praveena, K.; Matteppanavar, Shidaling; Angadi, Basavaraj

doi:10.1007/s13204-012-0100-1

Cited by 45 publications

(9 citation statements)

References 34 publications

(27 reference statements)

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“…The combination of microwave irradiation with hydro-or solvo-thermal conditions allows reaching ultrafast heating rates up to very high temperatures at relatively low autogenic pressures, which are favorable to access well-crystallized oxide inorganic nanocrystals under conditions [30]. This microwave assisted hydrothermal method has been studied for spinel ferrite particles with various compositions [31] and for BaM hexaferrites [32,33]. Interestingly, the aging of iron oxyhydroxides under microwave irradiation may significantly differ to classical thermal heating [34].…”

Section: Introductionmentioning

confidence: 99%

Microwave-assisted synthesis and magnetic properties of M-SrFe12O19 nanoparticles

Grindi

Beji

Viau

et al. 2018

Journal of Magnetism and Magnetic Materials

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Strontium hexaferrite nanoparticles were synthesized by a microwave-assisted hydrothermal process. The variation of structure, morphology and magnetic properties of the as-produced particles and after annealing temperatures were carefully analysed. Pure M-SrFe12O19 powders were synthesized at T = 200°C using a heating rate of 25 °C.min-1. The particles exhibited a magnetic coercivity of 95 kA.m-1 (µ0Hc = 0.12 T), explained by the shape of the particles that crystallized as very thin platelets with a micrometer size diameter and a very high aspect ratio in which a competition between shape and magnetocrystalline anisotropy takes place. The coercivity was strongly enhanced with Hc = 360 kA.m-1 (µ0 Hc = 0.445 T) by annealing at the optimum temperature of 1000°C. In order to optimize the particle morphology and magnetic properties after annealing, the heating rate of the microwave synthesis was increased. At T = 200°C using a heating rate of 40 °C.min-1 the particle exhibited a size in the range 20-100 nm. The powder crystallized as a mixture of hexaferrite and ferrihydrite. After annealing at 1000 °C, M-SrFe12O19 with a small amount of hematite (<15 %) was obtained. The coercivity was strongly enhanced to reach the value Hc = 465 kA.m-1 (µ0Hc = 0.585 T).

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Section: Introductionmentioning

confidence: 99%

Microwave-assisted synthesis and magnetic properties of M-SrFe12O19 nanoparticles

Grindi

Beji

Viau

et al. 2018

Journal of Magnetism and Magnetic Materials

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“…Hence, the MS technique significantly enhanced the grain size in a much shorter time and at lower sintering temperatures. 30 Figure 3 shows dielectric constant versus frequency plots for the samples sintered at different sintering temperatures. It can be seen that like all other ferrite materials, the samples show frequency dependent phenomenon, i.e.…”

Section: Resultsmentioning

confidence: 99%

Effects of sintering temperature on structural and electromagnetic properties of MgCuZn ferrite prepared by microwave sintering

Reddy

Ramana

Madhuri

et al. 2015

Advances in Applied Ceramics

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Nanocrystalline magnesium-copper-zinc (Mg 0.30 Cu 0.20 Zn 0.50 Fe 2 O 4 ) ferrites were prepared by microwave sintering technique. The effects of the sintering temperature on particle size and magnetic properties were investigated. In this article, optimum sintering temperature required for MgCuZn ferrite system for obtaining good electromagnetic properties, suitable for applications in low temperature co-fired ceramics (LTCC) chip components was studied. The grain size, initial permeability, dielectric constant and saturation magnetisations were found to increase, and dielectric loss was found to decrease with the increasing sintering temperature. Mg-Cu-Zn ferrites with a permeability of m ¼ 1110 (at 1 MHz) were fully densified at the standard LTCC sintering temperature of 9508C.

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“…By calculating the mean error between the actual and estimated values of the output traits and using statistical calculations, [64,65] such as RMSE, R 2 , an overall aspect of the model performance was provided, the results of which are presented in the next section. Equations (12)(13)(14)(15) explain the statistical calculations in this study in which SS Regression and SS Total represent the sum squared of the regression and the total error, N and P are the number of observations and predictors, respectively…”

Section: Assessment Of Model Performancementioning

confidence: 99%

“…It should be noted that such parameters describe the magnetic properties of the ferrites. [9][10][11][12][13][14][15] Clearly, discovering new properties and optimizing them is time and cost consuming and requires various research. As mentioned, the magnetic properties of ferrites are affected by parameters with complex interactions.…”

Section: Introductionmentioning

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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Structural and magnetic properties of nanocrystalline BaFe12O19 synthesized by microwave-hydrothermal method

Cited by 45 publications

References 34 publications

Microwave-assisted synthesis and magnetic properties of M-SrFe12O19 nanoparticles

Microwave-assisted synthesis and magnetic properties of M-SrFe12O19 nanoparticles

Effects of sintering temperature on structural and electromagnetic properties of MgCuZn ferrite prepared by microwave sintering

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

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