No Arabic abstract
Data collapse enables comparison of measurement data measured in different laboratories on different samples. In the case of energy losses in Soft Magnetic Materials (SMM) the data collapse is possible to achieved only if the measurement data can be described by the two components formula. For more complicated cases we propose to perform data collapses sequence in the two-dimensional subspaces $L_{i,i+1}$ spanned by the appropriate powers of frequency ${f^{i},f^{i+1}}$. Such approach enables the data comparison in the different two-dimensional subspaces. This idea has been tested with measurement data of the four SMM-s: amorphous alloy textrm{Fe}_{78}textrm{Si}_{13}textrm{B}_{9}$, amorphous alloy $textrm{Co}_{71.5} textrm{Fe}_{2.5} textrm{Mn}_{2} textrm{Mo}_{1} textrm{Si}_{9} textrm{B}_{14}$, crystalline material -- oriented electrotechnical steel sheets 3% Si--Fe, iron--nickel alloy $79% textrm{Ni}-textrm{Fe}$. Intermediate calculations revealed interesting property of the energy losses in the cristalline and amorphous SMM-s which lead to the following hypothesis. Let $P_{tot,1,2}=f_{1,2}(1+f_{1,2})$ be scaled two-components formula for the energy loss in SMM, where $f_{1,2}$ is the corresponding scaled frequency. Then the scaled energy losses values in amorphous SMM are below the second order universal curve $P_{tot,1,2}=f_{1,2}(1+f_{1,2})$, whereas the scaled energy losses values in crystalline SMM are above that universal curve.
A new mathematical model of hysteresis loop has been derived. Model consists in an extansion of tanh($cdot$) by extanding the base of exp function into an arbitrary positive number. The presented model is self-similar and invariant with respect to scaling. Scaling of magnetic hysteresis loop has been done using the notion of homogenous function in general sense.
Assuming that core loss data of Soft Magnetic Materials obey scaling relations, models describing the power losses in materials exposed to non-sinusoidal flux waveforms and DC Bias conditions have been derived. In order to test these models, the measurement data for two materials have been collected and the core losses calculated. Agreement between the experimental data and the model predictions is satisfactory.
Machine learning was utilized to efficiently boost the development of soft magnetic materials. The design process includes building a database composed of published experimental results, applying machine learning methods on the database, identifying the trends of magnetic properties in soft magnetic materials, and accelerating the design of next-generation soft magnetic nanocrystalline materials through the use of numerical optimization. Machine learning regression models were trained to predict magnetic saturation ($B_S$), coercivity ($H_C$) and magnetostriction ($lambda$), with a stochastic optimization framework being used to further optimize the corresponding magnetic properties. To verify the feasibility of the machine learning model, several optimized soft magnetic materials -- specified in terms of compositions and thermomechanical treatments -- have been predicted and then prepared and tested, showing good agreement between predictions and experiments, proving the reliability of the designed model. Two rounds of optimization-testing iterations were conducted to search for better properties.
A mechanistic understanding of adhesion in soft materials is critical in the fields of transportation (tires, gaskets, seals), biomaterials, micro-contact printing, and soft robotics. Measurements have long demonstrated that the apparent work of adhesion coming into contact is consistently lower than the intrinsic work of adhesion for the materials, and that there is adhesion hysteresis during separation, commonly explained by viscoelastic dissipation. Still lacking is a quantitative experimentally validated link between adhesion and measured topography. Here, we used in situ measurements of contact size to investigate the adhesion behavior of soft elastic polydimethylsiloxane (PDMS) hemispheres (modulus ranging from 0.7 to 10 MPa) on four different polycrystalline diamond substrates with topography characterized across eight orders of magnitude, including down to the r{A}ngstrom-scale. The results show that the reduction in apparent work of adhesion is equal to the energy required to achieve conformal contact. Further, the energy loss during contact and removal is equal to the product of intrinsic work of adhesion and the true contact area. These findings provide a simple mechanism to quantitatively link the widely-observed adhesion hysteresis to roughness rather than viscoelastic dissipation.
Conventional optical components are limited to size-scales much larger than the wavelength of light, as changes in the amplitude, phase and polarization of the electromagnetic fields are accrued gradually along an optical path. However, advances in nanophotonics have produced ultra-thin, co-called flat optical components that beget abrupt changes in these properties over distances significantly shorter than the free space wavelength. While high optical losses still plague many approaches, phonon polariton (PhP) materials have demonstrated long lifetimes for sub-diffractional modes in comparison to plasmon-polariton-based nanophotonics. We experimentally observe a three-fold improvement in polariton lifetime through isotopic enrichment of hexagonal boron nitride (hBN). Commensurate increases in the polariton propagation length are demonstrated via direct imaging of polaritonic standing waves by means of infrared nano-optics. Our results provide the foundation for a materials-growth-directed approach towards realizing the loss control necessary for the development of PhP-based nanophotonic devices.