Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2021Zeta potentials (ζ) of metal oxide nanoparticles: a meta-analysis of experimental data and a predictive neural networks modeling55citations
  • 2019Predicting Thermal Conductivity Enhancement of Al2O3/Water and CuO/Water Nanofluids Using Quantitative Structure-Property Relationship Approach7citations

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Mikołajczyk, Alicja
1 / 4 shared
Syzochenko, Michael
2 / 2 shared
Leszczynski, Jerzy
2 / 4 shared
Puzyn, Tomasz
1 / 8 shared
Chart of publication period
2021
2019

Co-Authors (by relevance)

  • Mikołajczyk, Alicja
  • Syzochenko, Michael
  • Leszczynski, Jerzy
  • Puzyn, Tomasz
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article

Predicting Thermal Conductivity Enhancement of Al2O3/Water and CuO/Water Nanofluids Using Quantitative Structure-Property Relationship Approach

  • Syzochenko, Michael
  • Leszczynski, Jerzy
  • Sizochenko, Natalia
Abstract

In the current contribution, the authors have applied a quantitative structure-property relationship (QSPR) approach to develop theoretical models of thermal conductivity enhancement in water-based nanofluids (Al2O3 and CuO). The developed models represent physical properties of nanofluids as functions of experimentally measured and calculated descriptors. The developed model for Al2O3 is characterized by determination coefficient R2= 0.876 (training) and R2= 0.826 (test); the model for CuO is characterized by R2 = 0.984 (training) and R2= 0.912 (test). The developed models are in good agreement with modern theories of nanofluids behavior. Size-dependent and concentration-dependent behavior of thermal conductivity of Al2O3 and CuO nanoparticles properties were discussed. The authors found that thermal conductivity increases with increase of weighted fraction-dependent parameters. The developed models have been compared with the existing models for thermal conductivity of Al2O3 and CuO water-based nanofluids.

Topics
  • nanoparticle
  • impedance spectroscopy
  • thermal conductivity