Do you want to publish a course? Click here

Graphene on h-BN: to align or not to align?

62   0   0.0 ( 0 )
 Added by Roberto Guerra
 Publication date 2017
  fields Physics
and research's language is English




Ask ChatGPT about the research

The contact strength, adhesion and friction, between graphene and an incommensurate crystalline substrate such as {it h}-BN depends on their relative alignment angle $theta$. The well established Novaco-McTague (NM) theory predicts for a monolayer graphene on a hard bulk {it h}-BN crystal face a small spontaneous misalignment, here $theta_{NM}$,$simeq$,0.45 degrees which if realized would be relevant to a host of electronic properties besides the mechanical ones. Because experimental equilibrium is hard to achieve, we inquire theoretically about alignment or misalignment by simulations based on dependable state-of-the-art interatomic force fields. Surprisingly at first, we find compelling evidence for $theta = 0$, i.e., full energy-driven alignment in the equilibrium state of graphene on {it h}-BN. Two factors drive this deviation from NM theory. First, graphene is not flat, developing on {it h}-BN a long-wavelength out-of-plane corrugation. Second, {it h}-BN is not hard, releasing its contact stress by planar contractions/expansions that accompany the interface moire structure. Repeated simulations by artificially forcing graphene to keep flat, and {it h}-BN to keep rigid, indeed yield an equilibrium misalignment similar to $theta_{NM}$ as expected. Subsequent sliding simulations show that friction of graphene on {it h}-BN, small and essentially independent of misalignments in the artificial frozen state, strongly increases in the more realistic corrugated, strain-modulated, aligned state.



rate research

Read More

The growth of single layer graphene nanometer size domains by solid carbon source molecular beam epitaxy on hexagonal boron nitride (h-BN) flakes is demonstrated. Formation of single-layer graphene is clearly apparent in Raman spectra which display sharp optical phonon bands. Atomic-force microscope images and Raman maps reveal that the graphene grown depends on the surface morphology of the h-BN substrates. The growth is governed by the high mobility of the carbon atoms on the h-BN surface, in a manner that is consistent with van der Waals epitaxy. The successful growth of graphene layers depends on the substrate temperature, but is independent of the incident flux of carbon atoms.
Automatic generation of textual video descriptions that are time-aligned with video content is a long-standing goal in computer vision. The task is challenging due to the difficulty of bridging the semantic gap between the visual and natural language domains. This paper addresses the task of automatically generating an alignment between a set of instructions and a first person video demonstrating an activity. The sparse descriptions and ambiguity of written instructions create significant alignment challenges. The key to our approach is the use of egocentric cues to generate a concise set of action proposals, which are then matched to recipe steps using object recognition and computational linguistic techniques. We obtain promising results on both the Extended GTEA Gaze+ dataset and the Bristol Egocentric Object Interactions Dataset.
Wetting transitions have been predicted and observed to occur for various combinations of fluids and surfaces. This paper describes the origin of such transitions, for liquid films on solid surfaces, in terms of the gas-surface interaction potentials V(r), which depend on the specific adsorption system. The transitions of light inert gases and H2 molecules on alkali metal surfaces have been explored extensively and are relatively well understood in terms of the least attractive adsorption interactions in nature. Much less thoroughly investigated are wetting transitions of Hg, water, heavy inert gases and other molecular films. The basic idea is that nonwetting occurs, for energetic reasons, if the adsorption potentials well-depth D is smaller than, or comparable to, the well-depth of the adsorbate-adsorbate mutual interaction. At the wetting temperature, Tw, the transition to wetting occurs, for entropic reasons, when the liquids surface tension is sufficiently small that the free energy cost in forming a thick film is sufficiently compensated by the fluid- surface interaction energy. Guidelines useful for exploring wetting transitions of other systems are analyzed, in terms of generic criteria involving the simple model, which yields results in terms of gas-surface interaction parameters and thermodynamic properties of the bulk adsorbate.
Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distributional word embeddings, to enrich the representation of tokens and measure the relatedness between tokens and each learnable cluster centroid. The proposed method jointly learns word clustering centroids and clustering-token alignments, achieving the state of the art results on multiple benchmark datasets and proving that the proposed cluster-token alignment mechanism is indeed favorable to text classification. Notably, our qualitative analysis has conspicuously illustrated that text representations learned by the proposed model are in accord well with our intuition.
Automated speech recognition coverage of the worlds languages continues to expand. However, standard phoneme based systems require handcrafted lexicons that are difficult and expensive to obtain. To address this problem, we propose a training methodology for a grapheme-based speech recognizer that can be trained in a purely data-driven fashion. Built with LSTM networks and trained with the cross-entropy loss, the grapheme-output acoustic models we study are also extremely practical for real-world applications as they can be decoded with conventional ASR stack components such as language models and FST decoders, and produce good quality audio-to-grapheme alignments that are useful in many speech applications. We show that the grapheme models are competitive in WER with their phoneme-output counterparts when trained on large datasets, with the advantage that grapheme models do not require explicit linguistic knowledge as an input. We further compare the alignments generated by the phoneme and grapheme models to demonstrate the quality of the pronunciations learnt by them using four Indian languages that vary linguistically in spoken and written forms.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا