Low-temperature magnetic and magnetocaloric properties of orthorhombic DyNiSn

Synoradzki, Karol

doi:10.1016/j.physb.2023.415300

Cited by 4 publications

(5 citation statements)

References 73 publications

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“…For the Dy 2 Cr 2 C 3 dysprosium-based magnetocaloric compound presented in Figure 4a, an increase in the value of applied magnetic field increases the value of relative cooling power up to the applied magnetic field of 5 T, after which further increases in the applied magnetic field decrease the relative cooling power. A similar value of relative cooling power has been reported experimentally at an applied magnetic field of 5 T [49]. This confirms that the developed model generalizes well using an external dataset or different testing conditions.…”

Section: Predictions Of Sil-based Models For Different Dysprosium-bas...supporting

confidence: 87%

“…The value of the relative cooling power decreases with the increase in the applied magnetic field up to a field of 2 T, after which an increase in the applied magnetic field increases the value of relative power. The experimental value of the relative cooling power for the DyGa [49] magnetocaloric compound at 5 T aligns with the predicted value of the SN-SIL-IR model. The developed model can be adequately employed for the exploration of the potentials of dysprosium-based magnetocaloric compounds for practical implementation in addressing the energy crisis.…”

Section: Predictions Of Sil-based Models For Different Dysprosium-bas...supporting

confidence: 82%

“…The modified chemical formula of dysprosiumbased magnetocaloric samples for model extraction and implementation is presented in Equation (10). Experimentally measured values of relative cooling power employed for model validation are extracted from the literature for different thirty-eight dysprosiumbased magnetocaloric compounds [8,[45][46][47][48][49].…”

Section: Data Extraction and Acquisitionmentioning

confidence: 99%

“…Estimations of the developed SN-SIL-IR, SG-SIL-IR, SN-SIL-EC, and SG-SIL-EC models are presented with the corresponding absolute error after comparison with the measured relative cooling power values. The developed SN-SIL-IR model with ionic radii-based descriptors shows excellent performance and predicts the relative cooling power of most of the dysprosium-based magnetocaloric compounds exactly, except for a few compounds with slight deviations, such as Dy 2 Cu 2 Cd [49], Dy 2 Cu 2 Cd [46], DyNiSi [49], DyB 2 [49], Dy 2 Cu 2 Cd [46], DyCoNi [8], DyNiSn [49] and Dy 2 Co 2 Ga [46]. Predictions of other developed models, such as the SG-SIL-IR, SN-SIL-EC, and SG-SIL-EC models, have slight associated deviations from the measured values.…”

Section: Predictions Of Sil-based Models For Different Dysprosium-bas...mentioning

confidence: 99%

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Single Hidden Layer Intelligent Approach to Modeling Relative Cooling Power of Rare-Earth-Transition-Metal-Based Refrigerants for Sustainable Magnetic Refrigeration Application

Alqahtani

2024

Sustainability

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Solid-state magnetocaloric-based magnetic refrigeration offers green and sustainable refrigeration with improved efficiency, compactness and environmental friendliness compared with commercialized gas compression refrigeration systems. Relative cooling power (RCP) plays a significant role in the candidature of any magnetic material refrigerants in this application, while the tunable physical and magnetic properties of rare-earth-transition-metal-based materials strengthen the potential of these materials to be used in a cooling system. This work develops single hidden layer (SIL) extreme learning machine intelligent models for predicting the RCP of rare-earth-transition-metal-based magnetocaloric compounds using elemental constituent ionic radii (IR) and maximum magnetic entropy change (EC) descriptors. The developed model based on the sine (SN) activation function with ionic radii (IR) descriptors (SN-SIL-IR) shows superior performance over the sigmoid (SG) activation function-based model, represented as SG-SIL-IR, with performance improvements of 71.86% and 69.55% determined using the mean absolute error (MAE) and root mean square error (RMSE), respectively, upon testing rare-earth-transition-metal-based magnetocaloric compounds. The developed SN-SIL-IR further outperforms the SN-SIL-EC and SG-SIL-EC models which employed maximum magnetic entropy change (EC) descriptors with improvements of 45.74% and 24.79%, respectively, on the basis of MAE performance assessment parameters. Estimates of the developed model agree well with the measured values. The dependence of the RCP on an applied magnetic field for various classes of rare-earth-transition-metal-based magnetocaloric compounds is established using a developed SN-SIL-IR model. The improved precision of the developed SN-SIL-IR model, coupled with ease of its descriptors, will strengthen and facilitate the comprehensive exploration of rare-earth-transition-metal-based magnetocaloric compounds for their practical implementation as magnetic refrigerants for promoting a sustainable system of refrigeration that is known to be efficient and environmentally friendly.

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Section: Predictions Of Sil-based Models For Different Dysprosium-bas...supporting

confidence: 87%

Section: Predictions Of Sil-based Models For Different Dysprosium-bas...supporting

confidence: 82%

Section: Data Extraction and Acquisitionmentioning

confidence: 99%

Section: Predictions Of Sil-based Models For Different Dysprosium-bas...mentioning

confidence: 99%

See 2 more Smart Citations

Single Hidden Layer Intelligent Approach to Modeling Relative Cooling Power of Rare-Earth-Transition-Metal-Based Refrigerants for Sustainable Magnetic Refrigeration Application

Alqahtani

2024

Sustainability

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“…Dysprosium -transition metals based magnetocaloric compounds employed for simulation consist of fifty-four different compounds extracted from literature [ 10 , 13 , 42 – 45 ]. The predictors to the models include the applied field, ionic radius of the transition metal and that of other incorporated metals or non-metals.…”

Section: 0 Data Sample Acquisition and Computational Methodologymentioning

confidence: 99%

Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method

Ibn Shamsah

2024

PLoS ONE

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Intermetallic alloy containing rare earth dysprosium ions with the associated unfilled 4f shell electrons and sub-lattice of 3d-transition metal, results into fascinating magnetic properties which are useful for green refrigeration technological application. Magnetocaloric effect remains the fundamental principle upon which magnetic refrigeration technology is based while this cooling technology has advantages of cost effectiveness, high efficiency and environmental friendliness as compared with the existing conventional gas compression systems. Maximum magnetic entropy change (which controls the hugeness of magnetocaloric effect) of intermetallic alloy Dy-T-X (where T = transition metal and X = any other metal or nonmetal) is modeled in this work using hybrid genetic algorithm based support vector regression (GSVR) computational intelligent method with applied magnetic field, ionic concentration and ionic radii descriptors. The developed GSVR-G model with kernel Gaussian function outperforms GSVR-P model with polynomial function with improvement of 85.23%, 78.82% and 78.67% on the basis of the computed correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) on testing sample, respectively. The developed model further investigates the influence of applied external magnetic field on magnetocaloric effect of DyCuAl intermetallic alloy. The developed models in this work circumvent experimental challenges of magnetocaloric effect determination while the recorded precision of the developed model further opens doors for possible exploration of these intermetallic compounds for addressing environmental challenges associated with the present system of cooling.

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