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Optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin composite using metaheuristic algorithms

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Abstract

Injectable calcium phosphate cements have been introduced as adjuncts to internal fixation for treating selected fractures. These cements harden without producing much heat, develop compressive strength, and are remodeled slowly in vivo. The main purpose of the cement is to fill voids in metaphyseal bone, thereby reducing the need for bone graft. However, such cements may also improve the holding strength around metal devices in osteoporotic bone. This paper presents the optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin (CPC/MWCNT-OH/BSA) composites in terms of compressive strength using well-known metaheuristic optimizers. The process parameters studied were wt% of MWCNT-OH (0.2–0.5 wt%) and wt% of BSA (5–15 wt%). The obtained results from metaheuristic algorithms were compared with the results from the response surface methodology (RSM) in the literature. The results obtained from metaheuristic algorithms outperformed the results given by the RSM in terms of less error percentage and high compressive strength.

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Acknowledgments

The authors would like to acknowledge for the Ministry of Higher Education of Malaysia and the University of Malaya, Kuala Lumpur, Malaysia for the financial support under UM.TNC2/IPPP/UPGP/628/6/ER013/2011A.

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Authors and Affiliations

  1. Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ali Sadollah, Ardeshir Bahreininejad, Mohd Hamdi & Judha Purbolaksono

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  1. Ali Sadollah
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  2. Ardeshir Bahreininejad
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  3. Mohd Hamdi
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  4. Judha Purbolaksono
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Correspondence to Ardeshir Bahreininejad.

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Sadollah, A., Bahreininejad, A., Hamdi, M. et al. Optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin composite using metaheuristic algorithms. Neural Comput & Applic 24, 193–200 (2014). https://doi.org/10.1007/s00521-012-1219-4

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