In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer perceptron neural network is used to predict the attached mass, which causes a variation of the BNNTs frequency shift with different diameters and lengths. This model is implemented in the form of a component in the ORCAD-PSPICE electric simulator library. The component should reproduce faithfully the biological sensor behavior. Moreover, we have developed an inverse model called intelligent sensor in order to remove the nonlinearity response provided by the sensor. The association of this ANN-based corrector has brought significant improvement for high sensing performance.

In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer perceptron neural network is used to predict the attached mass, which causes a variation of the BNNTs frequency shift with different diameters and lengths. This model is implemented in the form of a component in the ORCAD-PSPICE electric simulator library. The component should reproduce faithfully the biological sensor behavior. Moreover, we have developed an inverse model called intelligent sensor in order to remove the nonlinearity response provided by the sensor. The association of this ANN-based corrector has brought significant improvement for high sensing performance.

In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer perceptron neural network is used to predict the attached mass, which causes a variation of the BNNTs frequency shift with different diameters and lengths. This model is implemented in the form of a component in the ORCAD-PSPICE electric simulator library. The component should reproduce faithfully the biological sensor behavior. Moreover, we have developed an inverse model called intelligent sensor in order to remove the nonlinearity response provided by the sensor. The association of this ANN-based corrector has brought significant improvement for high sensing performance.

In this study, an ultrasensitive biological boron nitride-based nanotube (Bio-BNNT) sensor is modeled and investigated by means of neural approach. The type of configuration studied is a cantilevered BNNT resonator sensor with an attached mass at the tip. The idea behind our resonator sensor is based on the determination of the natural BNNT frequency shift induced by added biological mass. A multilayer perceptron neural network is used to predict the attached mass, which causes a variation of the BNNTs frequency shift with different diameters and lengths. This model is implemented in the form of a component in the ORCAD-PSPICE electric simulator library. The component should reproduce faithfully the biological sensor behavior. Moreover, we have developed an inverse model called intelligent sensor in order to remove the nonlinearity response provided by the sensor. The association of this ANN-based corrector has brought significant improvement for high sensing performance.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.

Iron thin films were deposited on glass substrates using RF magnetron sputtering and their optimal deposition conditions were determined. The structure properties were analyzed using x-ray diffraction (XRD) and their magnetic hysteresis loops were obtained by Vibrating Sample Magnetometer (VSM) at room temperature. In this situation, the magnetic field is either parallel or perpendicular to the substrate plane. The main contribution of this work is to characterize the thin layers and present a mathematical model that can get best fit of the characteristics B(H). By using Preisach model, good agreement was obtained between theoretical and experimental results in both cases.