Fundamental Biomechanics in Implant Design and Bone Tissue Engineering

A special issue of Journal of Functional Biomaterials (ISSN 2079-4983). This special issue belongs to the section "Bone Biomaterials".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 4314

Special Issue Editors


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Guest Editor
Department of Engineering Management, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia
Interests: biomaterials; composites; additive manufacturing; implants; finite element analysis

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Guest Editor
Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Interests: dental adhesives; resin-based composites; bioceramics; glass fibers; vibrational spectroscopy; electrospinning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Mascara, Algeria
Interests: finite element analysis; computational modeling in biomechanics; appliance designs; biomechanics; biomaterials and biomanufacturing; numerical studies of orthopedic and dental implant systems

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Guest Editor
School of Engineering & Technology, National Center of Composite Materials (NCCM), National Textile University, Faisalabad, Pakistan
Interests: finite element analysis; computational modeling in biomechanics, biomaterials; composites; implants; 3D printing

Special Issue Information

Dear Colleagues,

This Special Issue invites interdisciplinary scientific research papers (articles), reviews, and communications about the biomechanics of biomaterials for implants and tissue engineering, especially the latest manufacturing techniques, such as additive manufacturing. This specific Special Issue focuses on topics in engineering, physics, chemistry, and material science. The objective of this issue is to understand the behavior of different biomaterials when inside the body, whether in vivo, in vitro or simulated using finite element analysis. The mission of this issue is to focus the attention on mechanical, physicochemical, and biological characteristics and their importance in the interactions between biomaterials and living tissues, as well as studies on the preparation, performance, and use of biomaterials in biomedical devices in physiological environments.

The scope of the Special Issue includes but is not limited to:

  • Biomechanics of implants—the design and analysis of implants to repair or replace different organs, for example, for bone, dental, and tissue engineering and so on;
  • Tissue engineering and regenerative medicine—biomaterial sciences, methods/technologies to engineer scaffolds from biomaterials, and scaffold-based tissue regeneration and visualization.

Dr. Hassan Mehboob
Dr. Abdul Samad Khan
Prof. Dr. Ali Merdji
Dr. Ali Mehboob
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Functional Biomaterials is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bone
  • biomechanics
  • implants
  • design and analysis
  • additive manufacturing
  • characterization

Published Papers (3 papers)

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Research

19 pages, 13319 KiB  
Article
The Porosity Design and Deformation Behavior Analysis of Additively Manufactured Bone Scaffolds through Finite Element Modelling and Mechanical Property Investigations
by Shummaila Rasheed, Waqas Akbar Lughmani, Muhammad Mahabat Khan, Dermot Brabazon, Muhannad Ahmed Obeidi and Inam Ul Ahad
J. Funct. Biomater. 2023, 14(10), 496; https://doi.org/10.3390/jfb14100496 - 08 Oct 2023
Cited by 1 | Viewed by 1312
Abstract
Additively manufactured synthetic bone scaffolds have emerged as promising candidates for the replacement and regeneration of damaged and diseased bones. By employing optimal pore architecture, including pore morphology, sizes, and porosities, 3D-printed scaffolds can closely mimic the mechanical properties of natural bone and [...] Read more.
Additively manufactured synthetic bone scaffolds have emerged as promising candidates for the replacement and regeneration of damaged and diseased bones. By employing optimal pore architecture, including pore morphology, sizes, and porosities, 3D-printed scaffolds can closely mimic the mechanical properties of natural bone and withstand external loads. This study aims to investigate the deformation pattern exhibited by polymeric bone scaffolds fabricated using the PolyJet (PJ) 3D printing technique. Cubic and hexagonal closed-packed uniform scaffolds with porosities of 30%, 50%, and 70% are utilized in finite element (FE) models. The crushable foam plasticity model is employed to analyze the scaffolds’ mechanical response under quasi-static compression. Experimental validation of the FE results demonstrates a favorable agreement, with an average percentage error of 12.27% ± 7.1%. Moreover, the yield strength and elastic modulus of the scaffolds are evaluated and compared, revealing notable differences between cubic and hexagonal closed-packed designs. The 30%, 50%, and 70% porous cubic pore-shaped bone scaffolds exhibit significantly higher yield strengths of 46.89%, 58.29%, and 66.09%, respectively, compared to the hexagonal closed-packed bone scaffolds at percentage strains of 5%, 6%, and 7%. Similarly, the elastic modulus of the 30%, 50%, and 70% porous cubic pore-shaped bone scaffolds is 42.68%, 59.70%, and 58.18% higher, respectively, than the hexagonal closed-packed bone scaffolds at the same percentage strain levels. Furthermore, it is observed in comparison with our previous study the μSLA-printed bone scaffolds demonstrate 1.5 times higher elastic moduli and yield strengths compared to the PJ-printed bone scaffolds. Full article
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13 pages, 3933 KiB  
Article
Enhancing Polymethyl Methacrylate Prostheses for Cranioplasty with Ti mesh Inlays
by Gargi Shankar Nayak, Heinz Palkowski and Adele Carradò
J. Funct. Biomater. 2023, 14(8), 420; https://doi.org/10.3390/jfb14080420 - 10 Aug 2023
Viewed by 878
Abstract
Biocompatible polymers such as polymethyl methacrylate (PMMA), despite fulfilling biomedical aspects, lack the mechanical strength needed for hard-tissue implant applications. This gap can be closed by using composites with metallic reinforcements, as their adaptable mechanical properties can overcome this problem. Keeping this in [...] Read more.
Biocompatible polymers such as polymethyl methacrylate (PMMA), despite fulfilling biomedical aspects, lack the mechanical strength needed for hard-tissue implant applications. This gap can be closed by using composites with metallic reinforcements, as their adaptable mechanical properties can overcome this problem. Keeping this in mind, novel Ti-mesh-reinforced PMMA composites were developed. The influence of the orientation and volume fraction of the mesh on the mechanical properties of the composites was investigated. The composites were prepared by adding Ti meshes between PMMA layers, cured by hot-pressing above the glass transition temperature of PMMA, where the interdiffusion of PMMA through the spaces in the Ti mesh provided sufficient mechanical clamping and adhesion between the layers. The increase in the volume fraction of Ti led to a tremendous improvement in the mechanical properties of the composites. A significant anisotropic behaviour was analysed depending on the direction of the mesh. Furthermore, the shaping possibilities of these composites were investigated via four-point bending tests. High shaping possibility was found for these composites when they were shaped at elevated temperature. These promising results show the potential of these materials to be used for patient-specific implant applications. Full article
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17 pages, 3127 KiB  
Article
A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems
by Khaled Akkad, Hassan Mehboob, Rakan Alyamani and Faris Tarlochan
J. Funct. Biomater. 2023, 14(3), 156; https://doi.org/10.3390/jfb14030156 - 15 Mar 2023
Cited by 3 | Viewed by 1635
Abstract
Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. [...] Read more.
Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis. Full article
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