Journal of Materials Science

, Volume 46, Issue 2, pp 490–499 | Cite as

Morphological and biological characterization of density engineered foams fabricated by ultrasonic sonication

  • C. Torres-SánchezEmail author
  • J. R. Corney


The successful manufacture of functionally tailored materials (e.g., density engineered foams) for advanced applications (e.g., structures or in bioengineering) requires an effective control over the process variables. In order to achieve this, density gradation needs to be represented and quantified. Current density measurement techniques offer information on bulk values, but neglect local position as valuable information (i.e., do not associate density scalar values with specific location, which is frequently critical when mechanical properties or functionalities have to be engineered). In this article, we present a method that characterizes the density gradation of engineered foams manufactured by the sonication technique, which allows the generation of sophisticated porous architectures beyond a simple linear gradient. A 3D data capture (μCT) and a flexible analysis software program (ImageJ) are used to obtain “global” density gradation values that can, ultimately, inform, control, and optimize the manufacture process. Polymeric foams, i.e., polyurethane (PU) foams, were used in this study as proof of concept. The measurements performed on the PU foams were validated by checking consistency in the results for both horizontal and vertical image slices. Biological characterization was done to assess the samples’ tailored structure viability as scaffolds for tissue engineering. The comparison between untreated and sonicated samples yielded a 12.7% of increment in living cell count adhered to the walls after treatment. The conclusions drawn from this study may inform the design and manufacture of density-engineered materials used in other fields (e.g., structural materials, optoelectronics, food technology, etc.)


Foam Metal Foam Biological Characterization Polymeric Foam Density Gradation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Dr Paul Tatum, at AWE plc, for allowing the acquisition of the μCT images for the samples in this study; Dr Ruggero Gabbrielli for the photograph in Fig. 1a and Dr Krassimir Dotchev for assistance in the fabrication of the sample at the Manufacturing Engineering Centre in Cardiff University; Dr Donald Fawcett, from Visuals Unlimited Inc. for Fig. 1b. Dr Sarah Cartmell and Dr Lilia Araida Hidalgo-Bastida from Keele University kindly provided the images from the viability study in Fig. 10.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, School of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghUK
  2. 2.Department of Design, Manufacture and Engineering ManagementUniversity of StrathclydeGlasgowUK

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