scholarly journals Large Scale Self-Organization of 2D Hexagonal Ge and Au Nanodots on Patterned TiO2 for Optoelectronic Applications

2019 ◽  
Vol 2 (4) ◽  
pp. 2026-2035 ◽  
Author(s):  
Thomas Bottein ◽  
Mohammed Bouabdellaoui ◽  
Jean-Benoît Claude ◽  
Luc Favre ◽  
Thomas David ◽  
...  
Keyword(s):  
Large Scale ◽  
Self Organization ◽  
Optoelectronic Applications ◽  
Au Nanodots

scholarly journals Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering

2014 ◽  
Vol 5 ◽  
pp. 1203-1209 ◽  
Author(s):  
Hind Kadiri ◽  
Serguei Kostcheev ◽  
Daniel Turover ◽  
Rafael Salas-Montiel ◽  
Komla Nomenyo ◽  
...  
Keyword(s):  
Large Scale ◽  
The Self ◽  
Surface Topology ◽  
Self Organization ◽  
Hydrogen Silsesquioxane ◽  
Polystyrene Beads ◽  
Structure Topology ◽  
Self Organized ◽  
Novel Method ◽  
Long Range Ordering

Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ) posts, on the self-organization of polystyrene beads (PS) dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.

Controllable growth of multilayered XSe2 (X=W and Mo) for nonlinear optical and optoelectronic applications

2D Materials ◽  
2021 ◽  
Author(s):  
Kun Ye ◽  
Lixuan Liu ◽  
Liying Chen ◽  
Wenlong Li ◽  
Bochong Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Second Harmonic ◽  
Transition Metal Dichalcogenides ◽  
Chemical Vapor ◽  
Fast Response ◽  
Peak Position ◽  
Metal Dichalcogenides ◽  
Controllable Growth ◽  
Optoelectronic Applications ◽  
Layered Transition Metal

Abstract The layered transition metal dichalcogenides (TMDs) exhibit the intriguing physical properties and potential application in novel electronic devices. However, controllable growth of multilayer TMDs remains challenging. Herein, large-scale and high-quality multilayer prototype TMDs of W(Mo)Se2 were synthesized via chemical vapor deposition. For Raman and PL measurements, 2H and 3R multilayer WSe2 crystals displayed significant layer-dependent peak position and intensity feature. Besides, different from the oscillatory relationship of SHG intensity for odd-even layer numbers in 2H-stacked multilayer WSe2, the second harmonic generation intensity of 3R-stacked ones parabolically increased with the thickness due to the absence of inversion symmetry. For device application, photodetectors based on WSe2 with increasing thickness exhibited p-type (bilayer), ambipolar (trilayer), and n-type (4 layers) semiconductor behaviors, respectively. Furthermore, photodetectors based on the as-synthesized 3R-stacked WSe2 flakes displayed an excellent responsivity (R) of 7.8×103 mA/W, high specific detectivity (Da*) of 1.7×1014 Jones, outstanding external quantum efficiency (EQE) of 8.6×102 %, and fast response time (τRise=57 ms and τFall=53 ms) under 532 nm illumination with bias voltage of Vds=5 V. Similar results have also been achieved in multilayer MoSe2 crystals. All these findings indicate great potential of 3R-stacked TMDs in two-dimensional optoelectronic applications.

scholarly journals Activity-dependent myelination: A glial mechanism of oscillatory self-organization in large-scale brain networks

2020 ◽  
Vol 117 (24) ◽  
pp. 13227-13237 ◽  
Author(s):  
Rabiya Noori ◽  
Daniel Park ◽  
John D. Griffiths ◽  
Sonya Bells ◽  
Paul W. Frankland ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Phase Synchronization ◽  
Brain Networks ◽  
Self Organization ◽  
Conduction Delay ◽  
Neural Communication ◽  
Wide Range ◽  
The Impact ◽  
Activity Dependent

Communication and oscillatory synchrony between distributed neural populations are believed to play a key role in multiple cognitive and neural functions. These interactions are mediated by long-range myelinated axonal fiber bundles, collectively termed as white matter. While traditionally considered to be static after development, white matter properties have been shown to change in an activity-dependent way through learning and behavior—a phenomenon known as white matter plasticity. In the central nervous system, this plasticity stems from oligodendroglia, which form myelin sheaths to regulate the conduction of nerve impulses across the brain, hence critically impacting neural communication. We here shift the focus from neural to glial contribution to brain synchronization and examine the impact of adaptive, activity-dependent changes in conduction velocity on the large-scale phase synchronization of neural oscillators. Using a network model based on primate large-scale white matter neuroanatomy, our computational and mathematical results show that such plasticity endows white matter with self-organizing properties, where conduction delay statistics are autonomously adjusted to ensure efficient neural communication. Our analysis shows that this mechanism stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands, making phase-locked states more resilient to damage as reflected by diffuse decreases in connectivity. Critically, our work suggests that adaptive myelination may be a mechanism that enables brain networks with a means of temporal self-organization, resilience, and homeostasis.

Facile and large scale aqueous synthesis of CdS nanoparticles at room temperature towards optoelectronic applications

2018 ◽  
Vol 5 (10) ◽  
pp. 105003 ◽  
Author(s):  
R Murugan ◽  
M Rajesh Kumar ◽  
D Sathish Chander ◽  
S Chandra Kishore ◽  
X Lei ◽  
...  
Keyword(s):  
Large Scale ◽  
Room Temperature ◽  
Cds Nanoparticles ◽  
Aqueous Synthesis ◽  
Optoelectronic Applications

scholarly journals Thermodynamic Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 256
Author(s):  
Todd Hylton
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Open Systems ◽  
Self Organization ◽  
Small Scale ◽  
Edge States ◽  
Fluctuation Dissipation ◽  
Multiscale Dynamics ◽  
Physical Quantities ◽  
Open Thermodynamic Systems

A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. Isolated networks show multiscale dynamics, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model integrates concepts of conservation, potentiation, fluctuation, dissipation, adaptation, equilibration and causation to illustrate the thermodynamic evolution of organization in open systems. A key conclusion of the work is that the transport and dissipation of conserved physical quantities drives the self-organization of open thermodynamic systems.

scholarly journals Active contraction of microtubule networks

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Peter J Foster ◽  
Sebastian Fürthauer ◽  
Michael J Shelley ◽  
Daniel J Needleman
Keyword(s):  
Large Scale ◽  
Quantitative Agreement ◽  
Self Organization ◽  
Cellular Processes ◽  
Network Geometry ◽  
Initial Network ◽  
Fluid Theory ◽  
Scale Structures ◽  
Density Inhomogeneities ◽  
Microtubule Networks

Many cellular processes are driven by cytoskeletal assemblies. It remains unclear how cytoskeletal filaments and motor proteins organize into cellular scale structures and how molecular properties of cytoskeletal components affect the large-scale behaviors of these systems. Here, we investigate the self-organization of stabilized microtubules in Xenopus oocyte extracts and find that they can form macroscopic networks that spontaneously contract. We propose that these contractions are driven by the clustering of microtubule minus ends by dynein. Based on this idea, we construct an active fluid theory of network contractions, which predicts a dependence of the timescale of contraction on initial network geometry, a development of density inhomogeneities during contraction, a constant final network density, and a strong influence of dynein inhibition on the rate of contraction, all in quantitative agreement with experiments. These results demonstrate that the motor-driven clustering of filament ends is a generic mechanism leading to contraction.

Adaptive Self-Organization in Distributed Tree Topologies

2014 ◽  
Vol 5 (3) ◽  
pp. 24-57 ◽  
Author(s):  
Evangelos Pournaras ◽  
Martijn Warnier ◽  
Frances M.T. Brazier
Keyword(s):  
Large Scale ◽  
Overlay Networks ◽  
Adaptation Strategies ◽  
Self Organization ◽  
Self Organized ◽  
Trade Offs ◽  
Organization Process ◽  
Tree Topologies ◽  
Hierarchical Communication ◽  
Message Overhead

Tree topologies are often deployed in large-scale distributed systems to structure a hierarchical communication. Building and maintaining overlay networks self-organized in tree topologies is challenging to achieve in dynamic environments. Performance trade-offs between resilience to failures and message overhead need to be considered. This paper introduces eight adaptation strategies that provide a higher abstraction, modularity and reconfigurability in the tree self-organization process. Performance can be further enhanced by dynamically changing strategies during system runtime. Experimental evaluation illustrates the performance trade-offs and properties of adaptation strategies.

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