We report magnetic alignment of hexagonal boron nitride (hBN) platelets and the outstanding material properties of its polymer composite. The magnetically responsive hBN is produced by surface modification of iron oxide, and their orientations can be controlled by applying an external magnetic field during polymer curing. Owing to the anisotropic properties of hBN, the epoxy composite with aligned hBN platelets shows interesting properties along the alignment direction, including significantly reduced coefficient of thermal expansion, reaching ∼28.7 ppm/°C, and enhanced thermal conductivity, 104% higher than that of unaligned counterpart, both of which are observed at a low filler loading of 20 wt %. Our modeling suggests the filler alignment is the major reason for these intriguing material properties. Finite element analysis reveals promising applications for the magnetically aligned hBN-based composites in modern microelectronic packaging.

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Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and softtissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images.

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