Influence of Al-doping on the structural, magnetic, and electrical properties of La0.8Ba0.2Mn1−Al O3 (0 ⩽ x ⩽ 0.25) manganites

Saleh, J. Ardashti; Sarsari, I. Abdolhosseini; Kameli, P.; Salamati, H.

doi:10.1016/j.jmmm.2018.06.014

Cited by 15 publications

(2 citation statements)

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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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“…13) In actuality, the Griffiths phase is predominantly observed in the doped perovskite-type manganite. Saleh et al 27) showed that Al-doping affected the structural, magnetic and electric properties of La 0.8 Ba 0.2 Mn 1¹x Al x O 3 (0 < x < 0.25) where they found that the Griffiths phase was evident in all samples except for x = 0, 0.1. Further, the decrease in with increasing Al 3+ content suggests an additional decrease in the Griffith phase behavior and a decrease in size of the magnetic cluster.…”

Section: Resultsmentioning

confidence: 99%

Large Magnetocaloric Effect in Cu-Doped La0.7Ca0.3MnO3 Compounds

Ho¹,

Phan²,

Putri

et al. 2023

Mater. Trans.

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The structural characterization, and the electronic, magnetic and magnetocaloric properties of polycrystalline samples of La 0.7 Ca 0.3 Mn 1¹x Cu x O 3 (x = 0, 0.04, 0.06, 0.08) have been investigated. X-ray powder diffraction analysis indicates all samples having an orthorhombic structure, belonging to the Pbnm space group. X-ray absorption fine structure spectra reveal that Mn is in the mixed state of Mn 3+ and Mn 4+ while Cu has divalent state (Cu 2+ ). With the substitution of Cu 2+ for Mn, the Curie temperature, T C , decreases monotonically from 248 K for x = 0 to 156 K for x = 0.08, which is due to weakened exchange interactions. The downturn in the temperature dependencies of the inverse magnetic susceptibility, » ¹1 (T ), curves observed above T C for x = 0 and 0.08 is characteristic of the Griffiths-like phase. The analysis of isothermal magnetization data M(T, H ) based on the Banerjee's criteria has indicated x = 0, 0.04, and 0.06 samples undergoing a first-order magnetic phase transition. However, the x = 0.08 sample, the coexistences of second-order magnetic phase transition at low magnetic fields below 8 kOe and first-order magnetic phase transition at high magnetic fields were observed. The maximum magnetic entropy change measured at a magnetic field span of 50 kOe occurring near the T C decreases from 10.3 to 4.8 J/kg.K with increasing x from 0 to 0.08. However, the relative cooling power (RCP) tends to increase, in which a maximum RCP of 360 J/kg for x = 0.08 that is about 1.3 times greater than that observed for the parent sample (x = 0).

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Structural and Reflection Loss Properties of Fe3+ Substituted Lanthanum Manganite as Microwave Absorbing Material in X-Ku Band

Aritonang

Adi

Juhana

et al. 2021

Lecture Notes in Mechanical Engineering

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Influence of Al-doping on the structural, magnetic, and electrical properties of La0.8Ba0.2Mn1−Al O3 (0 ⩽ x ⩽ 0.25) manganites

Cited by 15 publications

References 52 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

Large Magnetocaloric Effect in Cu-Doped La<sub>0.7</sub>Ca<sub>0.3</sub>MnO<sub>3</sub> Compounds

Structural and Reflection Loss Properties of Fe3+ Substituted Lanthanum Manganite as Microwave Absorbing Material in X-Ku Band

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Influence of Al-doping on the structural, magnetic, and electrical properties of La0.8Ba0.2Mn1−Al O3 (0 ⩽ x ⩽ 0.25) manganites

Cited by 15 publications

References 52 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

Large Magnetocaloric Effect in Cu-Doped La<sub>0.7</sub>Ca<sub>0.3</sub>MnO<sub>3</sub> Compounds

Structural and Reflection Loss Properties of Fe3+ Substituted Lanthanum Manganite as Microwave Absorbing Material in X-Ku Band

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Influence of Al-doping on the structural, magnetic, and electrical properties of La0.8Ba0.2Mn1−Al O3 (0 ⩽ x ⩽ 0.25) manganites