scholarly journals Asymmetric flow networks

2014 ◽  
Vol 237 (2) ◽  
pp. 566-579 ◽  
Author(s):  
Norma Olaizola ◽  
Federico Valenciano
Keyword(s):  
Asymmetric Flow ◽  
Flow Networks

scholarly journals Limitations of Nanoparticles Size Characterization by Asymmetric Flow Field‑Fractionation Coupled with Online Dynamic Light Scattering

2021 ◽  
Author(s):  
Francesco Giorgi ◽  
Judith M. Curran ◽  
Douglas Gilliland ◽  
Rita La Spina ◽  
Maurice Whelan ◽  
...  
Keyword(s):  
Light Scattering ◽  
Dynamic Light Scattering ◽  
Flow Field ◽  
Field Flow Fractionation ◽  
Asymmetric Flow ◽  
Uv Detector ◽  
Elution Time ◽  
On Line ◽  
Real Size ◽  
Flow Fractionation

AbstractThe development of reliable protocols suitable for the characterisation of the physical properties of nanoparticles in suspension is becoming crucial to assess the potential biological as well as toxicological impact of nanoparticles. Amongst sizing techniques, asymmetric flow field flow fractionation (AF4) coupled to online size detectors represents one of the most robust and flexible options to quantify the particle size distribution in suspension. However, size measurement uncertainties have been reported for on-line dynamic light scattering (DLS) detectors when coupled to AF4 systems. In this work we investigated the influence of the initial concentration of nanoparticles in suspension on the sizing capability of the asymmetric flow field-flow fractionation technique coupled with an on-line dynamic light scattering detector and a UV–Visible spectrophotometer (UV) detector. Experiments were performed with suspensions of gold nanoparticles with a nominal diameter of 40 nm and 60 nm at a range of particle concentrations. The results obtained demonstrate that at low concentration of nanoparticles, the AF4-DLS combined technique fails to evaluate the real size of nanoparticles in suspension, detecting an apparent and progressive size increase as a function of the elution time and of the concentration of nanoparticles in suspension.

Enhanced desalination performance in asymmetric flow electrode capacitive deionization with nickel hexacyanoferrate and activated carbon electrodes

Desalination ◽  
2021 ◽  
Vol 514 ◽  
pp. 115172
Author(s):  
Yang Xu ◽  
Feng Duan ◽  
Yuping Li ◽  
Hongbin Cao ◽  
Junjun Chang ◽  
...  
Keyword(s):  
Activated Carbon ◽  
Carbon Electrodes ◽  
Capacitive Deionization ◽  
Asymmetric Flow ◽  
Nickel Hexacyanoferrate

scholarly journals Effects of thermo-erosion gullying on hydrologic flow networks, discharge and soil loss

2014 ◽  
Vol 9 (10) ◽  
pp. 105010 ◽  
Author(s):  
Etienne Godin ◽  
Daniel Fortier ◽  
Stéphanie Coulombe
Keyword(s):  
Soil Loss ◽  
Flow Networks

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Information Flow Networks of Chinese Stock Market Sectors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 13066-13077 ◽  
Author(s):  
Peng Yue ◽  
Qing Cai ◽  
Wanfeng Yan ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Chinese Stock Market ◽  
Flow Networks
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