Hasil untuk "Materials Science"

Yu‐Meng You, Weiqiang Liao, Dewei Zhao et al.

M. Atatüre, D. Englund, N. Vamivakas et al.

A. Heeger

Xiaoqin Qian, Yuanyi Zheng, Yu Chen

P. Ma

Abstract Tissue engineering is an interdisciplinary and multidisciplinary field. It has shown great promise in generating living alternatives for harvested tissues and organs for transplantation and reconstructive surgery. Materials and fabrication technologies are critically important for tissue engineering in designing temporary, artificial extracellular matrices (scaffolds), which support three-dimensional tissue formation. This review briefly introduces the concept of tissue engineering, and illustrates the relationship between tissue engineering and materials science and engineering. Important scaffold design principles are described. The most frequently used materials and fabrication technologies for scaffolds are reviewed. Some exciting new developments in scaffold materials and fabrication technologies are also discussed.

M. Dresselhaus, G. Dresselhaus

V. Georgakilas, K. Kordatos, M. Prato et al.

A very general and versatile method for functionalizing different types of carbon nanotubes is described, using the 1,3-dipolar cycloaddition of azomethine ylides. Approximately one organic group per 100 carbon atoms of the nanotube is introduced, to yield remakably soluble bundles of nanotubes, as seen in transmission electron micrographs. The solubilization of the nanotubes generates a novel, interesting class of materials, which combines the properties of the nanotubes and the organic moiety, thus offering new opportunities for applications in materials science, including the preparation of nanocomposites.

Y. Yampolskii

Kobayashi, H. Kaya, K. Goto et al.

Li Zhao, Li-zhen Fan, Meng Zhou et al.

Yiping Cao, R. Mezzenga

Amyloid fibrils have traditionally been considered only as pathological aggregates in human neurodegenerative diseases, but it is increasingly becoming clear that the propensity to form amyloid fibrils is a generic property for all proteins, including food proteins. Differently from the pathological amyloid fibrils, those derived from food proteins can be used as advanced materials in biomedicine, tissue engineering, environmental science, nanotechnology, material science as well as in food science, owing to a combination of highly desirable feature such as extreme aspect ratios, outstanding stiffness and a broad availability of functional groups on their surfaces. In food science, protein fibrillization is progressively recognized as an appealing strategy to broaden and improve food protein functionality. This review article discusses the various classes of reported food protein amyloid fibrils and their formation conditions. It furthermore considers amyloid fibrils in a broad context, from their structural characterization to their forming mechanisms and ensued physical properties, emphasizing their applications in food-related fields. Finally, the biological fate and the potential toxicity mechanisms of food amyloid fibrils are discussed, and an experimental protocol for their health safety validation is proposed in the concluding part of the review.

Ayataka Endo, M. Ogasawara, A. Takahashi et al.

R. Driver, A. Squires, Peter Rushworth et al.

L. A. Kolahalam, I. K. Viswanath, B. S. Diwakar et al.

Abstract The recent past in the technological development evidenced that evolution in Nanotechnology and nanoscience is the key factor. Nanotechnology is multidisciplinary science which deals with physics, chemistry, materials science and other engineering sciences. The applications of Nanotechnology are spreading in almost all the branches of science and technology. The present review article highlighted the types of nanoparticles and their synthesis methods, characterization techniques. There are many techniques and applications are reported in the last five years but here we strictly focused on the general synthetic approaches and applications of the nanomaterials which provide a general idea to the young researchers.

G. Scappucci, Christoph Kloeffel, F. Zwanenburg et al.

In the effort to develop disruptive quantum technologies, germanium is emerging as a versatile material to realize devices capable of encoding, processing and transmitting quantum information. These devices leverage the special properties of holes in germanium, such as their inherently strong spin–orbit coupling and their ability to host superconducting pairing correlations. In this Review, we start by introducing the physics of holes in low-dimensional germanium structures, providing key insights from a theoretical perspective. We then examine the materials-science progress underpinning germanium-based planar heterostructures and nanowires. We go on to review the most significant experimental results demonstrating key building blocks for quantum technology, such as an electrically driven universal quantum gate set with spin qubits in quantum dots and superconductor–semiconductor devices for hybrid quantum systems. We conclude by identifying the most promising avenues towards scalable quantum information processing in germanium-based systems. Germanium is a promising material to build quantum components for scalable quantum information processing. This Review examines progress in materials science and devices that has enabled key building blocks for germanium quantum technology, such as hole-spin qubits and superconductor–semiconductor hybrids.

Dingfeng Xu, Chaoji Chen, Jia Xie et al.

G. Olson

J. Wellington, J. Osborne

Charles H. Ward

Boris Tsvelikhovskiy, Ilya Safro, Yuri Alexeev

Constructing effective mixer Hamiltonians is essential for enhancing the performance of the quantum approximate optimization algorithm (QAOA) in solving combinatorial optimization problems. In this work, we develop a systematic methodology for designing QAOA mixers that align with the symmetries of the classical objective function, with the goal of achieving values (mean, median, and minimum over multiple runs) that are closer to the true optimum. Our main idea is to design QAOA operators that are explicitly adapted to the action of symmetry groups on the Hilbert space. We focus on subgroups of the symmetric group <inline-formula><tex-math notation="LaTeX">$ S_{d}$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$ d = 2^\ell$</tex-math></inline-formula>, to ensure compatibility with qudit-based quantum architectures. In particular, we construct QAOA mixers invariant under the full symmetric group <inline-formula><tex-math notation="LaTeX">$ S_{d}$</tex-math></inline-formula> as well as its cyclic subgroup <inline-formula><tex-math notation="LaTeX">$ \mathbb {Z}_{d} \subset S_{d}$</tex-math></inline-formula>. These constructions are natural in that they respect the decomposition of the Hilbert space into isotypic components under the symmetry group action. Notably, to the best of the authors&#x2019; knowledge, the QAOA algorithm based on the <inline-formula><tex-math notation="LaTeX">$ \mathbb {Z}_{d}$</tex-math></inline-formula>-invariant mixer provides the first example of a QAOA protocol whose dynamics (up to final measurement) are confined entirely within a nontrivial irreducible representation of a symmetry group of the objective function. Although our work does not investigate the benefits of exploiting such subspaces as computational resources, we think that the very realization of a variational algorithm whose evolution is restricted to a nontrivial symmetry-adapted subspace is of fundamental conceptual interest. We provide closed-form expressions for these mixers, together with explicit quantum circuit implementations. To empirically evaluate our approach, we compare QAOA variants employing the standard mixer <inline-formula><tex-math notation="LaTeX">$B = \sum X_{i}$</tex-math></inline-formula> with those using our proposed Hamiltonians <inline-formula><tex-math notation="LaTeX">$H_{M}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$H_\chi$</tex-math></inline-formula> on edge coloring and graph partitioning problems. Across multiple graph instances, our symmetry-adapted mixers consistently yield objective values closer to the optimum, demonstrating statistically significant improvements over classical baselines.