No abstract

As a class of stimuli-responsive materials, shape memory polymers (SMPs) have received great attention due to their scientific interest and promising applications in advanced technologies in different areas. [1,2] Dual-SMPs can memorize a programmed temporary shape defined by the applied force and fixed by switching the system from one state to the other. Various molecular relaxations and phase transitions, e.g., vitrification, crystallization, or less commonly used liquid crystal (LC) transition, can be employed to fix the temporary shape. It is also demonstrated that multi-SMPs can be fabricated based on multiple transitions or a broad transition. [3] When the reversible transition is triggered by external stimuli, [1,4] the SMP will recover to its permanent shape defined by a network embedded in the system. The network shall be robust enough to resist the plastic deformation when the temporary shape is programmed.The network can be made of chemical or physical crosslink points, leading to SMPs that are thermoset or thermoplastic. [1] While the former gives more stable shape memory performance, the latter is attractive due to the flexibility of processing. However, physical crosslinks based on noncovalent bond interactions, e.g., chain entanglement, hydrogen bond, and ionic interaction, are often less stable. [5] Chain sliding or reorganization occurred during deformation will result in poor shape memory properties. This is a fatal weakness for many thermoplastic SMPs, particularly when a large shape change is demanded, [6] such as in some biomedical devices [2a] and package materials. [6d] To obtain better SMPs combining thermoplastic and stable network, one elegant approach is to follow the strategy of vitrimer. [7a] One can make the network using dynamic covalent bonds, which can allow the SMP to be reshaped at high temperatures with the aid of a catalyst. [7] On the other hand, new thermoplastic SMPs with pure physical crosslink network are still desirable. For example, an excellent multi-SMP of a compositional gradient copolymer is recently reported, [3d] showing a microphase-separated structure similar to the thermoplastic elastomer of styrene-butadiene-styrene (SBS) triblock copolymer. Nevertheless, to make the thermoplastic SMPs with both ideal shape fixity (R f ) and shape recovery (R r ) for high strain (e.g., strain >400%) remains a great challenge. [5,6b] While the commonly applied physical crosslinks show their limitation, we attempt to find a new type of physical crosslink by utilizing a columnar LC structure. Here we report a novel thermoplastic high strain SMP of hemiphasmid sidechain polynorbornene (P1; Figure 1a). P1 exhibits a hexagonal columnar LC (Φ H ) phase and a broad Φ H -isotropic transition. It renders both the R f and R r approaching 100% for dual-shape memory effect (SME), even when a high strain of ≈600% is applied. It is also a multi-SMP. For triple and quadruple-SME, with the total strain higher than 400%, it can still give R f quite high and R r > 95% at each step....

Structure and morphology evolution of the uniaxially stretched polyamide-6 (PA6) film with the β-form upon heating were investigated mainly by synchrotron twodimensional (2D) wide-angle X-ray diffraction and small-angle X-ray scattering. For comparison, thermal transitions of the oriented PA6 samples with the αand γ-form were also monitored, which undergo "incomplete Brill transition" and direct melting, respectively. Using the results of oriented αand γ-PA6 as the references, we confirm that the stretched β-PA6 is mesomorphic, which can be viewed as a solid mesophase with smectic B-like structure. We identify that upon heating the stretched β-PA6 reorganizes dominantly into γform crystal with the chain orientation unchanged, accompanied by the formation of a little amount of α-PA6 crystallites. Further heating to above the melting temperature of γ-PA6, the α-PA6 crystals grow through a recrystallization process. The lamellae resulting from reorganization exhibit a distribution of orientations, and the α-PA6 lamellae formed at high temperatures have the lamellar basal surface normal parallel to the stretched direction. We consider that the abundant hydrogen bonds in the stretched β-PA6 film construct a network, providing the confinement effect to reduce the chain mobility and thus favor the formation of γform. The lamellae with the basal surface normal tilted relative to the stretched direction can also be attributed to the hydrogenbonded network of oriented chains.

Background Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage–derived sequences in metavirome data is important for elucidating their different roles in interactions with bacterial hosts and regulation of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method, DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage–derived fragment. Findings DeePhage uses a “one-hot” encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on 5-fold cross-validation reaches as high as 89%, nearly 10% and 30% higher than that of 2 similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time vs PhagePred and PHACTS by 245 and 810 times, respectively, under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease. Conclusions DeePhage is a novel tool developed to rapidly and efficiently identify 2 kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.