Anti-EMD (N-terminal) polyclonal antibody

The magnetic domain structure and magnetostrictive properties of TbxHo1-xFe1.9Mn0.1 compounds have been investigated by in-situ Lorentz transmission electron microscopy (LTEM). The magnetocrystalline anisotropy compensation has been realized to be around x = 0.12 based on the easy magnetization direction (EMD) and by evaluating magnetocrystalline anisotropy constant K-1, magnetization and magnetostriction. The EMD at room temperature rotates from the <1 0 0> axis for x <= 0.10 to <1 1 1> axis for x >= 0.12, which is detected directly by electronic holography technique. The magnetic domain wall movements were observed by in-situ magnetic field LTEM, and the critical magnetic field H-cr decreases continuously from 100 Oe for the Tb-rich side of x = 0.16 to 60 Oe for the Tb-poor side of x = 0.12, owing to the decrease in magnetocrystalline anisotropy subjected by anisotropy compensation. A high low-field magnetostriction (lambda(a) similar to 200 ppm at 1 kOe) is achieved at the critical magnetocrystalline anisotropy compensation point of x = 0.12. This work helps to understand the correlation between magnetocrystalline anisotropy, magnetization process, magnetic domain wall motion and magnetostriction in the C15 Laves phase system.

In this paper, a hybrid damage detection technique involving a combination of variational mode decomposition (VMD) and frequency domain decomposition (FDD) has been applied to study the effectiveness of damage detection in presence of heavily noise contaminated environment. Damage of small magnitude has been tested under Gaussian pulse noise ranging from 0 to 100% for damage analysis. FDD, a signal processing algorithm, has system identification capability in the presence of noise but requires the output acceleration data from all the sensors installed in the nodes of a structure to identify damage. To reduce the number of sensor data needed to identify damage, wavelet-based algorithms have been used to obtain intrinsic mode functions (IMFs) from single sensor output. These IMFs are then fed to FDD algorithm to obtain the natural frequencies of the structure. For comparison purpose, the algorithms (empirical mode decomposition (EMD) + FDD, and VMD + FDD) have been applied to ASCE benchmark building, which has been set as a common platform, using sensor data of first storey. It was observed that the VMD + FDD gives satisfactory damage identification results for 100% noise contamination whereas EMD + FDD was unable to identify damage accurately for noise above 20%. The robustness of VMD + FDD has been established for a different type of noise, random-valued impulse noise, applied on the benchmark structure for detecting the structural parameters. The hybrid algorithm was also checked for system identification using the sensor data of fourth storey to establish its robustness against the sensitivity of the sensor location.