Observation of Exciton-Phonon Sideband in Individual Metallic Single-Walled Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) are quasi-one-dimensional systems with poor Coulomb screening and enhanced electron-phonon interaction, and are good candidates for excitons and exciton-phonon couplings in metallic state. Here we report back scattering reflection experiments on individual metallic SWCNTs. An exciton-phonon sideband separated by 0.19 eV from the first optical transition peak is observed in a metallic SWCNT of chiral index (13,10), which provides clear evidences of excitons in metallic SWCNTs. A static dielectric constant of 10 is estimated from the reflectance spectrum.Comment: 5 pages, 3 figures; typos corrected, references updated, text re-arrange

A cooperative domain model for multiple phase transitions and complex conformational relaxations in polymers with shape memory effect

Shape memory polymers (SMPs) are thermo-rheologically complex materials showing significant temperature and time dependences. Their segments often undergo cooperative phase transitions and conformational relaxations simultaneously along with shape memory effect (SME). In this study, a cooperative domain model is proposed to describe the composition dependence, multiple phase transitions and conformational relaxations of SMPs within their glass transition zones. Variations in local-area compositions and cooperative domains of the amorphous SMPs cause significant differences in their segmental relaxation. At a fixed domain size, both intermolecular activation energy and relaxation time significantly influence the SME and thermomechanical properties of the SMPs. Finally, the model is successfully applied to predict the shape memory behavior of SMPs with one stage SME and triple-SME, and the theoretical results have been validated by the experimental ones. This model could be a powerful tool to understand the working mechanisms and provide a theoretical guidance for the designs of multi-SME in SMPs

A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects

The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Cartesian k-space trajectories. However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid in each iteration of the network training, slowing down the training process and posing inconvenience and delay during training. Multiple iterations of nonuniform Fourier transform in the networks offset the deep learning advantage of fast inference. Current approaches typically either work on image-to-image networks or grid the non-Cartesian trajectories before the network training to avoid the repeated gridding process. However, the image-to-image networks cannot ensure the k-space data consistency in the reconstructed images and the pre-processing of non-Cartesian k-space leads to gridding errors which cannot be compensated by the network training. Inspired by the Transformer network to handle long-range dependencies in sequence transduction tasks, we propose to rearrange the radial spokes to sequential data based on the chronological order of acquisition and use the Transformer to predict unacquired radial spokes from acquired ones. We propose novel data augmentation methods to generate a large amount of training data from a limited number of subjects. The network can be generated to different anatomical structures. Experimental results show superior performance of the proposed framework compared to state-of-the-art deep neural networks.Comment: Accepted at MICCAI 202

A phenomenological model for dynamic response of double-network hydrogel composite undergoing transient transition

We present a phenomenological model for dynamic deformation and mechanical response of double-network (with short-chained ionic network and long-chained covalent network) hydrogel composite based on theory of transient networks. Molecular structures and stress-strain relations of the hydrogel composite were investigated based on thermomechanical properties of the individual network. Constitutive relations were derived for its nonlinear viscoelastic responses and annihilation/reformation rates of active short chains were determined by means of Eyring formula. An extended Volokh model was proposed to separate effects of large strain hysteresis and anomalous viscoelastic relaxation on the hydrogel composite after strain reversal. Experimental results from rate-independent tests are well in agreement with that of the numerical simulations. This study provides a fundamental simulation tool for modelling and predicting mechanics and mechanisms of viscoelastic response and mechanical responses in double-network hydrogel composite