Electrospun Nanocomposite Fibers of Polycarbonate- and Taurine-Modified Boehmite Nanoparticles: What Can Be Learned from Structural and Thermal Investigations?

2021 ◽  
Author(s):  
Natalia Cano Murillo ◽  
Paulina Szymoniak ◽  
Glen J. Smales ◽  
Heinz Sturm ◽  
Andreas Schönhals
Keyword(s):  
Boehmite Nanoparticles ◽  
Thermal Investigations ◽  
Nanocomposite Fibers

scholarly journals Electrospun nanocomposite fibers from lignin and iron oxide as supercapacitor material

2021 ◽  
Author(s):  
Pichitchai Butnoi ◽  
Autchara Pangon ◽  
Rüdiger Berger ◽  
Hans-Jürgen Butt ◽  
Varol Intasanta
Keyword(s):  
Iron Oxide ◽  
Nanocomposite Fibers

scholarly journals Viscosity Model for Nanoparticulate Suspensions Based on Surface Interactions

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2752
Author(s):  
Benedikt Finke ◽  
Clara Sangrós Sangrós Giménez ◽  
Arno Kwade ◽  
Carsten Schilde
Keyword(s):  
Mechanistic Model ◽  
Model Development ◽  
Rheological Behaviour ◽  
Hybrid Modelling ◽  
Shear Rates ◽  
Drag Forces ◽  
Boehmite Nanoparticles ◽  
Data Points ◽  
Two Parameters ◽  
Physical Phenomena

In this paper, a widely mechanistic model was developed to depict the rheological behaviour of nanoparticulate suspensions with solids contents up to 20 wt.%, based on the increase in shear stress caused by surface interaction forces among particles. The rheological behaviour is connected to drag forces arising from an altered particle movement with respect to the surrounding fluid. In order to represent this relationship and to model the viscosity, a hybrid modelling approach was followed, in which mechanistic relationships were paired with heuristic expressions. A genetic algorithm was utilized during model development, by enabling the algorithm to choose among several hard-to-assess model options. By the combination of the newly developed model with existing models for the various physical phenomena affecting viscosity, it can be applied to model the viscosity over a broad range of solids contents, shear rates, temperatures and particle sizes. Due to its mechanistic nature, the model even allows an extrapolation beyond the limits of the data points used for calibration, allowing a prediction of the viscosity in this area. Only two parameters are required for this purpose. Experimental data of an epoxy resin filled with boehmite nanoparticles were used for calibration and comparison with modelled values.

Evidence of the role of non-stoichiometry in Zn-Cr catalysts by thermal investigations

1988 ◽  
Vol 133 ◽  
pp. 155-161 ◽  
Author(s):  
Bice Fubini ◽  
Elio Giamello ◽  
Ferruccio Trifiro ◽  
Angelo Vaccari
Keyword(s):  
Thermal Investigations

Thermal investigations of transitional metal oxide cathodes in Li-ion cells

2000 ◽  
Vol 2 (2) ◽  
pp. 104-107 ◽  
Author(s):  
Rajeev Venkatachalapathy ◽  
Chang Woo Lee ◽  
Wenquan Lu ◽  
Jai Prakash
Keyword(s):  
Metal Oxide ◽  
Transitional Metal ◽  
Thermal Investigations ◽  
Oxide Cathodes ◽  
Li Ion ◽  
Transitional Metal Oxide

Thermal investigations of microelectronic chip with non‐uniform power distribution: temperature prediction and thermal placement design optimization

2004 ◽  
Vol 21 (3) ◽  
pp. 29-43 ◽  
Author(s):  
Teck Joo Goh ◽  
K.N. Seetharamu ◽  
G.A. Quadir ◽  
Z.A. Zainal ◽  
K. Jeevan Ganeshamoorthy
Keyword(s):  
Power Distribution ◽  
Heat Dissipation ◽  
Thermal Analyses ◽  
Convection Heat Transfer ◽  
Multiple Linear Regression Model ◽  
Least Square Method ◽  
Least Square ◽  
Junction Temperature ◽  
Thermal Investigations ◽  
Placement Distance

This paper presents the thermal analyses carried out to predict the temperature distribution of the silicon chip with non‐uniform power dissipation patterns and to determine the optimal locations of power generating sources in silicon chip design layout that leads to the desired junction temperature, Tj. Key thermal parameters investigated are the heat source placement distance, level of heat dissipation, and magnitude of convection heat transfer coefficient. Finite element method (FEM) is used to investigate the effect of the key parameters. From the FEM results, a multiple linear regression model employing the least‐square method is developed that relates all three parameters into a single correlation which would predict the maximum junction temperature, Tj,max.

Highly Extensible Bio-Nanocomposite Fibers

2010 ◽  
Vol 32 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Akhilesh K. Gaharwar ◽  
Patrick J. Schexnailder ◽  
Avinash Dundigalla ◽  
James D. White ◽  
Cristina R. Matos-Pérez ◽  
...  
Keyword(s):  
Nanocomposite Fibers

Advances of electrospun Mo-based nanocomposite fibers as anode materials for supercapacitors

2021 ◽  
pp. e00302
Author(s):  
Dandan Wang ◽  
Kaikai Tang ◽  
Jun Xiao ◽  
Xiao Li ◽  
Mengqi Long ◽  
...  
Keyword(s):  
Anode Materials ◽  
Nanocomposite Fibers
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