We report on the state-of-the art synthesis and improved luminescence properties of thiol-capped CdTe nanocrystals (NCs) synthesized in water. The optimized pH (12) and molar ratio of thiol to Cd ions (1.3:1) increases the room-temperature photoluminescence quantum efficiency of as-synthesized CdTe NCs capped by thioglycolic acid (TGA) to values of 40−60%. By employing mercaptopropionic acid (MPA) as a stabilizer, we have synthesized large (up to 6.0 nm in diameter) NCs so that the spectral range of the NCs' emission currently available within this synthetic route extends from 500 to 800 nm. Sizing curve for thiol-capped CdTe NCs is provided. In contrast to CdTe NCs capped by TGA, MPA-capped CdTe NCs show up to 1 order of magnitude longer (up to 145 ns) emission decay times, which become monoexponential for larger particles. This phenomenon is explained by considering the energetics of the Te-related traps in respect to the valence-band position of CdTe NCs. The correlation between luminescence quantum ef...

Aqueous synthesis of thiol-capped CdTe nanocrystals : State-of-the-art

Social network analysis

Summary


Ecological networks are often composed of different subcommunities (often referred to as modules). Identifying such modules has the potential to develop a better understanding of the assembly of ecological communities and to investigate functional overlap or specialization.
The most informative form of networks are quantitative or weighted networks. Here, we introduce an algorithm to identify modules in quantitative bipartite (or two-mode) networks. It is based on the hierarchical random graphs concept of Clauset et al. (2008 Nature 453: 98–101) and is extended to include quantitative information and adapted to work with bipartite graphs. We define the algorithm, which we call QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study.
Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within- or between-modules. Recursively applying the algorithm results in additional information of within-module organization of the network.
The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12139

A method for detecting modules in quantitative bipartite networks

Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

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Moser G

Papers

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