Results 121 to 130 of about 542,793 (319)
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Advanced Functional Materials, EarlyView.Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
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Retracing Argument for Distance-Regular Graphs
Journal of Combinatorial Theory, Series B, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
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A PEG Construction of LDPC Codes Based on the Betweenness Centrality Metric
Advances in Electrical and Computer Engineering, 2016 Progressive Edge Growth (PEG) constructions are usually based on optimizing the distance metric by using various methods. In this work however, the distance metric is replaced by a different one, namely the betweenness centrality metric, which was ...
BHURTAH-SEEWOOSUNGKUR, I. +2 more
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Handicap Labelings of 4-Regular Graphs
Advances in Electrical and Electronic Engineering, 2017 Let G be a simple graph, let f : V(G)→{1,2,...,|V(G)|} be a bijective mapping. The weight of v ∈ V(G) is the sum of labels of all vertices adjacent to v. We say that f is a distance magic labeling of G if the weight of every vertex is the same
Petr Kovar +3 more
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An All‐Optical Driven Bio‐Photovoltaic Interface for Active Control of Live Cells
Advanced Functional Materials, EarlyView.Bio‐photovoltaic Interface (BIO‐PV‐I) for live cell manipulation is presented. BIO‐PV‐I can be activated non‐invasively and remotely to control the spatial motility, adhesion, and morphology of cells adhering to it. BIO‐PV‐I uses a patterned light‐induced electric potential in iron‐doped lithium niobate crystals whose light‐driven and reversible nature,
Lisa Miccio +8 more
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Some Inequalities in Distance-Regular Graphs
Journal of Combinatorial Theory, Series B, 1993 For integers \(r\) \((>0)\) and \(a\), let \(n_ r(a)\) denote the following value \[ n_ r(a)= 1+ a+ a(a- 1)+\cdots+ a(a-1)^ i+\cdots+ a(a-1)^ r. \] In this paper, we prove the following theorem and give several corollaries. Theorem. Let \(G\) be a distance-regular graph with girth \(g\) and diameter \(d\).
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Advanced Functional Materials, EarlyView.A chiral photodetector capable of selectively distinguishing left‐ and right‐handed circularly polarized light is experimentally demonstrated. The device, which features a nanopatterned electrode inverse‐designed by a genetic algorithm within a metal–dielectric–metal nanocavity that incorporates a vacuum‐deposited small‐molecule multilayer, exhibits ...
Kyung Ryoul Park +3 more
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Distance spectrum of Indu–Bala product of graphs
AKCE International Journal of Graphs and Combinatorics, 2016 The D-eigenvalues μ1,μ2,…,μn of a graph G of order n are the eigenvalues of its distance matrix D and form the distance spectrum or D-spectrum of G denoted by SpecD(G). Let G1 and G2 be two regular graphs. The Indu–Bala product of G1 and G2 is denoted by
G. Indulal, R. Balakrishnan
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Spectral Characterization of the Hamming Graphs [PDF]
We show that the Hamming graph H(3; q) with diameter three is uniquely determined by its spectrum for q ¸ 36. Moreover, we show that for given integer D ¸ 2, any graph cospectral with the Hamming graph H(D; q) is locally the disjoint union of D copies of
Bang, S., Dam, E.R. van, Koolen, J.H.
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