Effect of Cu doping on the structural, magnetic and magnetocaloric properties of La0.7Sr0.25Na0.05Mn1−xCuxO3 manganites

Liu, Z.; Lin, Weicheng; Zhou, Kaiwen; Yan, Jialin

doi:10.1016/j.ceramint.2017.11.021

Cited by 31 publications

(5 citation statements)

References 35 publications

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“…The proposed models in this work were developed using one hundred experimentally measured MMEC of different manganite-based compounds extracted from the literature [7,8,11,15,18,[52][53][54][55][56][57][58][59][60][61][62][63][64]. The descriptors to the model are two sets of data.…”

Section: Dataset Description and Chemical Formula Of The Doped Manganmentioning

confidence: 99%

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.

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Section: Dataset Description and Chemical Formula Of The Doped Manganmentioning

confidence: 99%

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Shamsah

Owolabi

2020

Crystals

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“…These observations agree well with the results obtained by Belous et al 32 Both samples show a rhombohedral crystal structure with the R 3c space Our results are consistent with the structural analysis and recent literature. 20,25,36,37 Measurements of magnetization (M) as a function of temperature (T) were performed in the fieldcooled (FC) protocol to predict the magnetocaloric properties of the two samples. The solid line in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Recent reports have shown that the double-exchange (DE) strength is reduced by doping with transition metal-like Fe, Cu, Co, Ni, Cr, and Ru at B-sites, which results in a decrease in T C ; however, these elements may also confer MCE larger than that of the parent compound. [11][12][13][14][15][16][17][18][19][20][21][22][23] Therefore, investigating the efficiency of transition metal ions in the host material is important because interactions between dopants and the host material play a key role in controlling MCE and T C .…”

Section: Introductionmentioning

confidence: 99%

“…25 Some reports on the MCE properties of Mn sites substituted by Cu ions in La 1Àx Sr x MnO 3 compounds are available. 12,[17][18][19][20] Liu et al 20 observed the influence of Cu on the magnetic and magnetocaloric properties of La 0.7 Sr 0.25 Na 0.05 Mn 1Àx Cu x O 3 (x = 0, 0.05, 0.1, 0.15 and 0.2). The experimental result proved that with a magnetic field change of 20 kOe, the DS max M values reduces linearly from 2.20 J/kg K (x = 0) to 0.67 J/ kg K (x = 0.2).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Magnetocaloric Effects of La0.7Sr0.3Mn1-xCuxO3 Compounds (x = 0.08 and 0.12) Using a Phenomenological Model

Ngan

Dang

Nam

et al. 2020

Journal of Elec Materi

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In the present study, we present our investigation on the structure and magnetocaloric effect (MCE) of La 0.7 Sr 0.3 Mn 1Àx Cu x O 3 (x = 0.08 and 0.12) compounds prepared by a conventional solid-state method. X-ray diffraction patterns indicate that the samples have a rhombohedral structure with the R 3c space group. The Curie temperature decreases with increasing Cu concentration. Using a phenomenological model, the MCE of the two samples was predicted from the experimental magnetization data measured at magnetic fields of 3 kOe, 5 kOe, 8 kOe, and 10 kOe. The type of second-order magnetic phase transition of the samples is confirmed by the collapse of the entire master curve of magnetic entropy change in the same plot. Our investigation on the magnetocaloric behavior of the title compounds suggests that they are suitable candidates for application to near-room temperature magnetic refrigeration.

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“…Therefore, in order for a material to be a good candidate for industrial or domestic cooling power technology at room temperature, it is necessary to reduce its Curie temperature by taking into account the modulation of the relative ratio Mn 3+ /Mn 4+ [14][15][16]. This can be achieved by the substitution of manganese with a non magnetic ion which destroys the FM interactions and enhances the nonmetallic character in the material [17,18]. However, new investigation in this field modification at the B site has attracted surging attention owing to their important effect on the hybridization of the valence states between the oxygen and the B site ion.…”

Section: Introductionmentioning

confidence: 99%

Structural Characterization and Magnetic Interactions of La0.7Sr0.25Na0.05Mn1 - xAlxO3

Zaidi

Elabassi

Selmi

et al. 2020

J Supercond Nov Magn

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The magnetocaloric properties of manganite oxides La0.7Sr0.25Na0.05Mn1 − xAlxO3 (LSNMAlx) synthesized by sol-gel method were studied in detail. X-ray diffraction analyses showed a single rhombohedral phase with the R3-c space group. The inhomogeneous magnetic compartment coupled with the core-shell behavior was used to explain the magnetic properties and the evolution of the paramagnetic-ferromagnetic transition of the materials. Using a phenomenological model, the maximum values of magnetic entropy change (ΔSmax) decreased from 4.92 J kg−1 K for La0.7Sr0.25Na0.05MnO3 to 3.84 J kg−1 K for La0.7Sr0.25Na0.05Mn0.95Al0.05O3 upon an applied magnetic field of μ0H = 5 T, indicating an excellent quality of our samples as compared to many manganite oxides. The high quality of our samples was also checked by the large relative cooling power (RCP) which provides a good performance for industrial technologies in refrigeration devices.

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Effect of Cu doping on the structural, magnetic and magnetocaloric properties of La0.7Sr0.25Na0.05Mn1−xCuxO3 manganites

Cited by 31 publications

References 35 publications

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

Prediction of Magnetocaloric Effects of La0.7Sr0.3Mn1-xCuxO3 Compounds (x = 0.08 and 0.12) Using a Phenomenological Model

Structural Characterization and Magnetic Interactions of La0.7Sr0.25Na0.05Mn1 - xAlxO3

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