Analytical modeling of demagnetizing effect in magnetoelectric ferrite/PZT/ferrite trilayers taking into account a mechanical coupling

In this paper, we investigate the demagnetizing effect in ferrite/PZT/ferrite magnetoelectric (ME) trilayer composites consisting of commercial PZT discs bonded by epoxy layers to Ni-Co-Zn ferrite discs made by a reactive Spark Plasma Sintering (SPS) technique. ME voltage coefficients (transversal mode) were measured on ferrite/PZT/ferrite trilayer ME samples with different thicknesses or phase volume ratio in order to highlight the influence of the magnetic field penetration governed by these geometrical parameters. Experimental ME coefficients and voltages were compared to analytical calculations using a quasi-static model. Theoretical demagnetizing factors of two magnetic discs that interact together in parallel magnetic structures were derived from an analytical calculation based on a superposition method. These factors were introduced in ME voltage calculations which take account of the demagnetizing effect. To fit the experimental results, a mechanical coupling factor was also introduced in the theoretical formula. This reflects the differential strain that exists in the ferrite and PZT layers due to shear effects near the edge of the ME samples and within the bonding epoxy layers. From this study, an optimization in magnitude of the ME voltage is obtained. Lastly, an analytical calculation of demagnetizing effect was conducted for layered ME composites containing higher numbers of alternated layers (). The advantage of such a structure is then discussed

Giant coercivity of dense nanostructured spark plasma sintered barium hexaferrite

Due to the limited rare-earth elements resources, ferrite magnets need to be improved drastically. Ideally, for a true hard magnet, the coercive field should be larger than the saturation magnetization, which is not yet realized for ferrites. Thus, an alternative can be found in making very fine grain ferrite magnets, but it is usually impossible to get small grains and dense material together. In this paper, it is shown that the spark plasma sintering method is able to produce approximately 80% of dense material with crystallites smaller than 100 nm. The as-prepared bulk sintered anisotropic magnets exhibits coercive field of 0.5 T which is approximately 60% of the theoretical limit and only a few percentage below that of loose nanopowders. As a result, the magnets behave nearly ideal (-1.18 slope in the BH plane second quadrant) and the energy product reaches 8.8 kJ m-3, the highest value achieved in the isotropic ferrite magnet to our knowledge

Uniaxial anisotropy and enhanced magnetostriction of CoFe2_2O4_4 induced by reaction under uniaxial pressure with SPS

In this study, we have compared magnetic and magnetostrictive properties of polycrystalline CoFe$_2$O$_4$ pellets, produced by three different methods, focusing on the use of Spark Plasma Sintering (SPS). This technique allows a very short heat treatment stage while a uniaxial pressure is applied. SPS was utilized to sinter cobalt ferrite but also to make the reaction and the sintering (reactive sintering) of the same ceramic composition. Magnetic and magnetostrictive measurements show that the reactive sintering with SPS induces a uniaxial anisotropy, while it is not the case with a simple sintering process. The induced anisotropy is then expected to be a consequence of the reaction under uniaxial pressure. This anisotropy enhanced the magnetostrictive properties of the sample, where a maximum longitudinal magnetostriction of $-229$~ppm is obtained. This process can be a promising alternative to the magnetic-annealing because of the short processing time required (22 minutes)

A study of the 2014 Freshman Connection: Adventure participants and their involvement at Rowan University

The primary purpose of this study was to explore student involvement patterns and satisfaction of 40 participants of the Freshman Connection: Adventure program held by Rowan University in August of 2014. Data were collected from 33 subjects who participated in the survey, answering items on demographics, involvement, academic achievement, and the Freshman Connection: Adventure programs impact on their college experience. Data analysis suggested that students who participated in Freshman Connection: Adventure are widely involved on campus in different aspects. Data also suggested that the program gave students a positive outlook transitioning into college and gave them the necessary confidence to create long lasting friendships and positive influences at Rowan University. In regards to academic achievement, Freshman Connection participants reported that the program had low impact on their academic achievement

Transfer Learning Approach to Multiclass Classification of Child Facial Expressions

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a dearth of literature in child facial expression analysis. Recent advances in transfer learning methods have enabled the use of deep learning architectures, trained on adult facial expression images, to be tuned for classifying child facial expressions with limited training samples. The network will learn generic facial expression patterns from adult expressions which can be fine-tuned to capture representative features of child facial expressions. This work proposes a transfer learning approach for multi-class classification of the seven prototypical expressions including the ‘neutral’ expression in children using a recently published child facial expression data set. This work holds promise to facilitate the development of technologies that focus on children and monitoring of children throughout their developmental stages to detect early symptoms related to developmental disorders, such as Autism Spectrum Disorder (ASD)