Large direct and inverse electrocaloric effects in lead-free Dy doped 0.975KNN-0.025NBT ceramics

Aissa, Mohamed A.; Zannen, Moneim; Benyoussef, Manal; Belhadi, Jamal; Spreitzer, Matjaž; Kutnjak, Zdravko; Marssi, M. El; Lahmar, Abdelilah

doi:10.1016/j.ceramint.2021.08.001

Cited by 14 publications

(2 citation statements)

References 82 publications

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“…Strategy. The KNN classification algorithm mainly consists of three elements: K value, distance metric, and classification rule, and its classification principle is mainly to classify the samples to be tested by the category and classification rule of the K samples closest to the samples to be tested [19]. In the KNN algorithm, the selection of K value is very important; if the K value is too small, the classification model will become complex, and the overall accuracy of the classification model will be reduced; if the K value is too large, it will lead to the participation of samples that are not too similar to the sample to be tested and reduce As shown in Figure 3(a), the number of both triangles and circles in the near samples of the data set is 2, which makes it impossible to determine whether the samples to be tested are classified as triangles or circles.…”

Section: Improved Knn Algorithm Based On K Value Selectionmentioning

confidence: 99%

Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm

Qiu,

Liu,

Zhou

2023

Journal of Applied Mathematics

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At present, the gap of agricultural talents in China is continuously widening, and most enterprises lack agricultural core talents, which has caused great impact on the social economy. To solve this problem, an improved AHP-KNN algorithm is proposed by combining the analytic hierarchy process (AHP) and the optimized K-nearest neighbor algorithm, and an agricultural talent training model is proposed based on this algorithm. The results show that the classification accuracy and classification time of the improved AHP-KNN algorithm are 96.2% and 27.5 seconds, respectively, both of which are superior to the comparison algorithm. The result shows that the classification accuracy of agricultural talents can be improved by using this algorithm. Therefore, the model can be used to classify agricultural talents with the same characteristics into one class, carry out targeted training, and train all-round agricultural talents efficiently and quickly, so as to improve the serious shortage of agricultural talents at present.

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Section: Improved Knn Algorithm Based On K Value Selectionmentioning

confidence: 99%

Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm

Qiu,

Liu,

Zhou

2023

Journal of Applied Mathematics

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“…To realize solid-state cooling based on the EC effect, various material systems have been explored so far. Besides lead-containing EC material systems such as PbZr 0.95 Ti 0.05 O 3 (PZT) [ 2 ], PbSc 0.5 Ta 0.5 O 3 (PST) [ 3 ] and (1 − x)Pb(Mg 1/3 Nb 2/3 )O 3 –xPbTiO 3 (PMN-PT) [ 4 ], investigations on lead-free EC material systems based on ferroelectric BaTiO 3 [ 5 , 6 , 7 , 8 ] as well as the relaxor ferroelectrics Bi 0.5 Na 0.5 TiO 3 [ 9 , 10 , 11 ] and K 0.5 Na 0.5 NbO 3 [ 12 , 13 ] have also been widely reported.…”

Section: Introductionmentioning

confidence: 99%

Influence of Grain-Growth Inhibitors on Modified (Ba,Sr)(Sn,Ti)O3 for Electrocaloric Application

Li,

Molin,

Gebhardt

2024

Materials

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The paper reports on effect of grain-growth inhibitors MgO, Y2O3 and MnCO3 as well as Ca modification on the microstructure, dielectric, ferroelectric and electrocaloric (EC) properties of Ba0.82Sr0.18Sn0.065Ti0.935O3 (BSSnT). Furthermore, the effects of the sintering time and temperature on the microstructure and the electrical properties of the most promising material system Ba0.62Ca0.20Sr0.18Sn0.065Ti0.935O3 (BCSSnT-20) are investigated. Additions of MgO (xMgO = 1%), Y2O3 (xY2O3 = 0.25%) and MnCO3 (xMnCO3 = 1%) significantly decreased the mean grain size of BSSnT to 0.4 µm, 0.8 µm and 0.4 µm, respectively. Ba0.62Ca0.20Sr0.18Sn0.065Ti0.935O3 (BCSSnT-20) gained a homogeneous fine-grained microstructure with an average grain size of 1.5 µm, leading to a maximum electrocaloric temperature change |ΔTEC| of 0.49 K at 40 °C with a broad peak of |ΔTEC| > 0.33 K in the temperature range from 10 °C to 75 °C under an electric field change of 5 V µm−1. By increasing the sintering temperature of BCSSnT-20 from 1350 °C to 1425 °C, the grain size increased from 1.5 µm to 7.3 µm and the maximum electrocaloric temperature change |ΔTEC| increased from 0.15 K at 35 °C to 0.37 K at 20 °C under an electric field change of 2 V µm−1. Our results show that under all investigated material systems, BCSSnT-20 is the most promising candidate for future application in multilayer ceramic (MLC) components for EC cooling devices.

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Electrocaloric Effect in Ferroelectric Ceramics

Zhang,

Zou,

Jiang

2024

Piezoelectric Materials

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Large direct and inverse electrocaloric effects in lead-free Dy doped 0.975KNN-0.025NBT ceramics

Cited by 14 publications

References 82 publications

Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm

Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm

Influence of Grain-Growth Inhibitors on Modified (Ba,Sr)(Sn,Ti)O3 for Electrocaloric Application

Electrocaloric Effect in Ferroelectric Ceramics

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