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Single-crystal metal foils with high-index facets are currently being investigated owing to their potential application in the epitaxial growth of high-quality van der Waals film materials, electrochemical catalysis, gas sensing, and other fields. However, the controllable synthesis of large single-crystal metal foils with high-index facets remains a great challenge because high-index facets with high surface energy are not preferentially formed thermodynamically and kinetically. Herein, single-crystal nickel foils with a series of high-index facets are efficiently prepared by applying prestrain energy engineering technique, with the largest single-crystal foil exceeding 5×8 cm2 in size. In terms of thermodynamics, the internal mechanism of prestrain regulation on the formation of high-index facets is proposed. Molecular dynamics simulation is utilized to replicate and explain the phenomenon of multiple crystallographic orientations resulting from prestrain regulation. Additionally, large-sized and high-quality graphite films are successfully fabricated on single-crystal Ni(012) foils. Compared to the polycrystalline nickel, the graphite/single-crystal Ni(012) foil composites show more than five-fold increase in thermal conductivity, thereby showing great potential applications in thermal management. This study hence presents a novel approach for the preparation of single-crystal nickel foils with high-index facets, which is beneficial for the epitaxial growth of certain two-dimensional materials.
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BACKGROUND: With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic. OBJECTIVE: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem. METHODS: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation. RESULTS: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance. CONCLUSIONS: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.
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Dislocations are important for their effects on the chemical, electrical, magnetic, and transport properties of oxide materials, especially for electrochemical devices such as solid fuel cells and resistive memories, but these effects are still under-studied at the atomic level. We have developed a quantum mechanical/molecular mechanical (QM/MM)-based multiscale simulation program to reveal the diffusion properties of protons on ã100ã edge dislocations in BaZrO3 perovskite oxide. We find that the large free space and the presence of hydrogen bonds in the dislocation core structure lead to significant trapping of protons. The diffusion properties of protons in dislocation cores were investigated, and no evidence of pipeline diffusion was found from the calculated migration energy barriers, which not only did not accelerate ion diffusion but rather decreases the conductivity of ions. The proton diffusion properties of Y-doped BaZrO3 (BZY), with a dislocation core structure (BZY-D) and with a grain boundary structure (BZY-GB) were also compared. In all three structures, local lattice deformation occupies an essential part in the proton transfer and rotation processes. The change in bond order is calculated and it is found that the interaction with oxygen and Zr ions during proton transfer and rotation controls the energy barrier for local lattice deformation of the O-B-O motion, which affects the proton diffusion in the structure. Our study provides insight into proton diffusion in dislocations in terms of mechanical behavior, elucidates the origin of the energy barrier associated with proton diffusion in dislocations, and provides guidance for the preparation and application of proton conductors.
