Hasil untuk "Property"

D. Papageorgiou, I. Kinloch, R. Young

Abstract In this present review, the current status of the intrinsic mechanical properties of the graphene-family of materials along with the preparation and properties of bulk graphene-based nanocomposites is thoroughly examined. The usefulness of Raman spectroscopy for the characterization and study of the mechanical properties of graphene flakes and their composites is clearly exhibited. Furthermore, the preparation strategies of bulk graphene-based nanocomposites are discussed and the mechanical properties of nanocomposites reported in the literature are analysed. In particular, through the analyse of several hundred literature papers on graphene composites, we have found a unique correlation between the filler modulus, derived from the rule of mixtures, and the composite matrix. This correlation is found to hold true across a wide range of polymer matrices and thus suggests that the common assumption that the filler modulus is independent of the matric is incorrect, explaining the apparent under performance of graphene in some systems. The presence of graphene even at very low loadings can provide significant reinforcement to the final material, while the parameters that affect the nanocomposite strongly are thoroughly reviewed. Finally, the potential applications and future perspectives are discussed with regard to scale up capabilities and possible developments of graphene-based nanocomposite materials.

Ankur Gupta, H. Burak Eral, T. Hatton et al.

Nanoemulsions are kinetically stable liquid-in-liquid dispersions with droplet sizes on the order of 100 nm. Their small size leads to useful properties such as high surface area per unit volume, robust stability, optically transparent appearance, and tunable rheology. Nanoemulsions are finding application in diverse areas such as drug delivery, food, cosmetics, pharmaceuticals, and material synthesis. Additionally, they serve as model systems to understand nanoscale colloidal dispersions. High and low energy methods are used to prepare nanoemulsions, including high pressure homogenization, ultrasonication, phase inversion temperature and emulsion inversion point, as well as recently developed approaches such as bubble bursting method. In this review article, we summarize the major methods to prepare nanoemulsions, theories to predict droplet size, physical conditions and chemical additives which affect droplet stability, and recent applications.

J. Manser, Jeffrey A. Christians, P. Kamat

J. Lewandowski, M. Seifi

K. Gilroy, A. Ruditskiy, Hsin‐Chieh Peng et al.

J. Pennebaker, Ryan L. Boyd, Kayla Jordan et al.

M. Yáñez-Mó, P. Siljander, Zoraida Andreu et al.

In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

A. Togo, I. Tanaka

Abstract Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized. Phonopy is an open source code for such calculations launched by the present authors, which has been world-widely used. Here we demonstrate phonon properties with fundamental equations and show examples how the phonon calculations are applied in materials science.

R. Daneman, A. Prat

K. Yue, G. Trujillo‐de Santiago, M. M. Álvarez et al.

R. Ramakrishnan, Pavlo O. Dral, Pavlo O. Dral et al.

Computational de novo design of new drugs and materials requires rigorous and unbiased exploration of chemical compound space. However, large uncharted territories persist due to its size scaling combinatorially with molecular size. We report computed geometric, energetic, electronic, and thermodynamic properties for 134k stable small organic molecules made up of CHONF. These molecules correspond to the subset of all 133,885 species with up to nine heavy atoms (CONF) out of the GDB-17 chemical universe of 166 billion organic molecules. We report geometries minimal in energy, corresponding harmonic frequencies, dipole moments, polarizabilities, along with energies, enthalpies, and free energies of atomization. All properties were calculated at the B3LYP/6-31G(2df,p) level of quantum chemistry. Furthermore, for the predominant stoichiometry, C7H10O2, there are 6,095 constitutional isomers among the 134k molecules. We report energies, enthalpies, and free energies of atomization at the more accurate G4MP2 level of theory for all of them. As such, this data set provides quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships. Design Type(s) in silico design • data integration Measurement Type(s) Computational Chemistry Technology Type(s) quantum chemistry computational method Factor Type(s) level of theory Design Type(s) in silico design • data integration Measurement Type(s) Computational Chemistry Technology Type(s) quantum chemistry computational method Factor Type(s) level of theory Machine-accessible metadata file describing the reported data (ISA-Tab format)

Kyunghyun Cho, B. V. Merrienboer, Dzmitry Bahdanau et al.

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.

Zhen Wang, Jianwen Zhang, Jianlin Feng et al.

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.

Yong Zhang, T. Zuo, Zhi Tang et al.

S. Jacques

Christian Szegedy, Wojciech Zaremba, I. Sutskever et al.

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

A. Ferrari, D. Basko

Raman spectroscopy is an integral part of graphene research. It is used to determine the number and orientation of layers, the quality and types of edge, and the effects of perturbations, such as electric and magnetic fields, strain, doping, disorder and functional groups. This, in turn, provides insight into all sp(2)-bonded carbon allotropes, because graphene is their fundamental building block. Here we review the state of the art, future directions and open questions in Raman spectroscopy of graphene. We describe essential physical processes whose importance has only recently been recognized, such as the various types of resonance at play, and the role of quantum interference. We update all basic concepts and notations, and propose a terminology that is able to describe any result in literature. We finally highlight the potential of Raman spectroscopy for layered materials other than graphene.

H. Blanco‐Canqui

Sinemyiz Atalay, Iwona Jarocka-Karpowicz, E. Skrzydlewska

Cannabidiol (CBD) is one of the main pharmacologically active phytocannabinoids of Cannabis sativa L. CBD is non-psychoactive but exerts a number of beneficial pharmacological effects, including anti-inflammatory and antioxidant properties. The chemistry and pharmacology of CBD, as well as various molecular targets, including cannabinoid receptors and other components of the endocannabinoid system with which it interacts, have been extensively studied. In addition, preclinical and clinical studies have contributed to our understanding of the therapeutic potential of CBD for many diseases, including diseases associated with oxidative stress. Here, we review the main biological effects of CBD, and its synthetic derivatives, focusing on the cellular, antioxidant, and anti-inflammatory properties of CBD.

Roya Abka-Khajouei, Latifa Tounsi, N. Shahabi et al.

Alginate is a hydrocolloid from algae, specifically brown algae, which is a group that includes many of the seaweeds, like kelps and an extracellular polymer of some bacteria. Sodium alginate is one of the best-known members of the hydrogel group. The hydrogel is a water-swollen and cross-linked polymeric network produced by the simple reaction of one or more monomers. It has a linear (unbranched) structure based on d-mannuronic and l-guluronic acids. The placement of these monomers depending on the source of its production is alternating, sequential and random. The same arrangement of monomers can affect the physical and chemical properties of this polysaccharide. This polyuronide has a wide range of applications in various industries including the food industry, medicine, tissue engineering, wastewater treatment, the pharmaceutical industry and fuel. It is generally recognized as safe when used in accordance with good manufacturing or feeding practice. This review discusses its application in addition to its structural, physical, and chemical properties.