Ilker Kucuk

THERMOLUMINESCENCE CHARACTERISTICS OF Zn(BO2)2:Ce3+ UNDER BETA IRRADIATION

In this study, the thermoluminescence (TL) characteristics of undoped and various Ce(3) (+)-doped Zn(BO2)2 powder samples excited by beta irradiation are reported for the first time. Zn(BO2)2:Ce(3+) powder samples were prepared by the nitric acid method (NAM) using the starting oxides [zinc oxide (ZnO), boric acid (H3BO3) and doped element oxide (CeO2)]. The formations of the obtained samples were confirmed by an X-ray diffraction study. Dose responses of Ce(3) (+)-doped Zn(BO2)2 samples were investigated after the beta irradiation in the dose range from 143 mGy to 60 Gy. All TL measurements were made on using an automated Risø TL/OSL DA-20 reader. TL emission was detected through a filter pack (Schott BG-39 and Corning 7-59) transmitting between 330 and 480 nm. TL glow curves were obtained using a constant heating rate of 5°C s(-1) from room temperature (RT) to 450°C in an N2 atmosphere. The dose response and minimum detectable dose (MDD) values of the samples were determined. The dose responses of all the samples tested exhibited a superlinear behaviour. MDD value of 4 % Ce(3) (+)-doped Zn(BO2)2 sample, which shows a high temperature peak at about 230°C, was determined as 96 mGy. MDD values for 1, 2, 3, 5 and 10 % Ce(3) (+)-doped Zn(BO2)2 samples were also determined as 682, 501, 635, 320 and 824 mGy, respectively. The trap parameters of undoped and 4 % Ce(3) (+)-doped Zn(BO2)2 samples were estimated by the computerised glow curve deconvolution method.

Prediction of dynamic hysteresis loops of nano-crystalline cores

Dynamic hysteresis loops of a range of nano-crystalline cores have been obtained over a wide frequency range (1-50kHz). A dynamic hysteresis model from measurements using an artificial neural network trained by the delta-bar-delta learning algorithm has been developed. The input parameters include the geometrical dimensions of cores, peak magnetic induction and magnetizing frequency. The results show the neural network model has an acceptable estimation capability for dynamic hysteresis loops of toroidal nano-crystalline cores.

Estimation of thermally stimulated current in as grown TlGaSeS layered single crystals by multilayered perceptron neural network

Research highlights? Thermally stimulated current estimation in TlGaSeS layered single crystal. ? The model is fast and more accurately and easily estimated the TSC in TlGaSeS. ? The ANN approach can be useful tool for researcher to assess the traps distribution. This paper presents an artificial neural network approach to compute thermally stimulated current (TSC) in as-grown TlGaSeS layered single crystals. The experimental data have been obtained from TSC measurements. The network has been trained by a genetic algorithm (GA). The results confirmed that the proposed model could provide an accurate computation of the TSC.

Magnetocaloric and magnetoresistance properties in Co-based (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100−xCux (x=0–1) glassy alloys

Abstract The magnetocaloric and magnetoresistance properties of amorphous Co-based (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100−xCux (x = 0, 0.5, 0.75 and 1) ribbons were investigated. Cu additions changed the crystallisation temperature (Tx) and the Curie temperature (TC). The saturation magnetisation (Ms) and coercivity (Hc) for alloys were in the range of 65.51–38.49 emu/g and 1.99–6.84 A/m, respectively. Under an applied magnetic field change of 2.2 T, the (−ΔSM)max for (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100−xCux with x = 0, 0.5, 0.75 and 1 are 0.77, 0.71, 0.89 and 0.67 Jkg−1 K−1, respectively. The values of refrigeration capacity (RC) for the as-spun glassy alloys are comparable with those of previously studied Fe-based metallic glasses such as Fe80B10Zr9Cu1, (Fe0.76B0.24)96Nb4 and Fe82Ni2Zr6B10. In addition, the maximum magnetoresistance (MR) values for (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100−xCux with x = 0, 0.5, 0.75 and 1 are found to be 110, 38, 23 and 1% around the Curie temperatures under an applied magnetic field change of 1 T, respectively. With good RC, negligible hysteresis due to very low coercivity values and large magnetoresistance, these Co-based amorphous alloys can be used as the high temperature magnetic refrigerants and multifunctional applications working in the temperature range of 450–600 K.

Improving magnetocaloric properties of Fe68–xCrxTb5B23Nb4 (x = 0, 2, 4, 6 and 8) metallic glasses having high glass-forming ability with tunable Curie temperature

Abstract The impacts of adding Cr on the Curie temperature (TC), glass-forming ability (GFA), and magnetocaloric effect were studied in Fe68−xCrxTb5B23Nb4 (x = 0, 2, 4, 6 and 8) metallic glasses prepared by suction casting. GFA depends on Cr content in the composition. For Fe68−xCrxTb5B23Nb4 bulk metallic glasses (BMGs), with critical diameters up to ~3 mm can be produced by suction casting and maximum value of GFA was found for x = 6. By exchanging Cr with Fe partially, TC could effectively be adjusted in a quite broad temperature interval from 487 K for x = 0 to 267 K for x = 8, whereas maximum magnetic entropy change decreased from 1.16 to 0.53 Jkg−1 K−1 and refrigeration capacity (RC) changed from 116 to 45.05 J/kg under a low field change of 2 T. Though TC is shifted to room temperature, maximum magnetic entropy change and RC decreased almost half of the base alloy. To enhance these properties (Fe0.62Cr0.06Tb0.05B0.23Nb0.04)100−yCuy (y = 0.75, 1), metallic glasses are prepared. By the help of small addition of Cu, magnetocaloric properties can be effectively increased without changing the TC. These findings show that the successful synthesis of the Fe-based Fe62Cr6Tb5B23Nb4 and (Fe0.62Cr0.06Tb0.05B0.23Nb0.04)100−yCuy (y = 0.75, 1) BMGs near room temperature, could be considered as promising candidates as magnetic refrigerant materials.

Thermal and magnetic characterisation of (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100–xCux bulk metallic glasses

Abstract In this work, Co-based (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100–xCux bulk glassy alloys (BMG) with 2 mm diameters were formed by suction-casting method and effect of Cu in this system’s thermal stability, glass forming ability and magnetic properties were also investigated. The curves of thermal analysis, obtained using differential scanning calorimetry, show that (Co0.402Fe0.201Ni0.067B0.227Si0.053Nb0.05)100-xCux (x = 0–2) has supercooled liquid region (∆Tx) of about 45 K, and reduced glass transition temperature (Tg/Tl) lies in the range from 0.663 to 0.678. The saturation magnetisation (Js) and coercivity (Hc) for as-cast BMG were in the range of 0.46 T–0.65 T and 13 A/m, respectively.

Computational modeling of thermoluminescence glow curves of zinc borate crystals

This work presents a new approach based on multilayered perceptrons (MLPs) to compute the thermoluminescence glow curves in undoped and La-doped zinc borate crystals. The MLP has been trained by a genetic algorithm (GA) and a Levenberg-Marquardt learning algorithm. The results obtained using the MLP models were tested with untrained experimental glow curve data. The comparison has shown that the proposed model can predict more accurately and easily the thermoluminescence glow curves.PACS Codes:78.60.Kn, 87.53.Bn.

Dynamic hysteresis modelling for nano-crystalline cores

This paper presents an artificial neural network approach based on dynamic Preisach model to compute hysteresis loops of nano-crystalline cores. The network has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with experimental results.

Power Loss and Permeability Prediction, Sensitivity Analysis on Toroidal Transformer Cores Using Artificial Neural Networks

In this investigation a multi‐layer perception with a feed‐forward neural network model was used. The input parameters included the outer and inner diameters of the toroidal core, the strip width and thickness o electrical steel, the induction frequency and the peak magnetic flux density. The output parameters were power loss and permeability. Experimental data were collated different combination of core dimensions over 179 samples. A total of 3451 input vectors were available in the training set. The best output results were obtained for models formed by tanh+sig and sig only functions for power loss and permeability respectively. A self‐organising feature map neural network model was also formed for sensitivity analysis. The proposed model was in good agreement with experimental data and can be used for estimation of power loss.