Reza Barbaz Isfahani holds an M.Sc. in Applied Design Mechanical Engineering from Iran University of Science and Technology (2013), and a Ph.D. in Applied Design Mechanical Engineering from Amirkabir University of Technology (2021), all with distinction. His research focuses on composites/nanocomposites, smart/self-healing materials, mechanical property prediction, finite element modeling, and non-destructive testing. Key achievements include pioneering electrosprayed multi-core microcapsules for self-healing polymers, hybrid MWCNT/nano-SiO₂ reinforcement enhancing elastic modulus, and patenting multi-scale composites. He has authored >30 ISI papers on nanocomposite mechanics and multi-scale simulations validated experimentally.
Articles
Publication Date: 2026
Nanochemistry Research (25384279)11(1)pp. 133-151
This study evaluates the properties of various ceramic nanoparticles (NPs) utilized in dental biomaterials, focusing on their applicability in clinical settings. An 8 × 8 comparative analysis was conducted on key properties, including particle size, surface area, porosity, density, mechanical strength, biocompatibility, thermal stability, and cost. The materials examined include zirconia, alumina, hydroxyapatite, bioglass, silica, titania, calcium phosphate, and glass ceramics. Zirconia showed superior mechanical strength (1200 MPa) and thermal stability (1500°C), making it a leading candidate for load-bearing applications. Hydroxyapatite (HA) showed optimal biocompatibility (rating of 10), supporting its suitability for applications requiring integration with biological tissues. Alumina and bioglass showed promising attributes in terms of surface area and porosity, essential for enhancing the interaction with biological environments. The results highlight the importance of selecting the appropriate ceramic nanoparticle based on specific application requirements in dental biomaterials. This analysis provides valuable insights for researchers and clinicians in the field of dental materials, facilitating informed decisions in the development and application of advanced biomaterials tailored to enhance patient outcomes. Future studies should aim to explore the long-term performance and clinical efficacy of these materials in real-world scenarios. Statistical analysis identified strong correlations (r=0.85-0.89) between porosity, surface area, and mechanical strength, with PCA explaining 89% variance in material properties. © 2026 The Author(s).
Publication Date: 2026
Journal of Reinforced Plastics and Composites (07316844)
Achieving high dimensional accuracy in CNC machining of polymers is challenging due to their low stiffness and viscoelasticity. This study introduces an artificial neural network (ANN) framework to predict multi-axial dimensional deviations in the milling of polyamide (PA12) and low-density polyethylene (LDPE). The model innovatively integrates a fundamental mechanical property, ultimate tensile strength (UTS), with a key geometric parameter, tool diameter. A comprehensive experimental campaign was conducted under dry machining conditions with constant cutting parameters, systematically varying the tool diameter (3, 6, and 8 mm). The results demonstrated that larger tool diameters and higher material UTS values consistently led to a significant reduction in dimensional errors across the length, width, and thickness of the machined components. The developed feedforward ANN successfully captured the complex, non-linear relationship between these inputs and the outputs, demonstrating exceptional predictive performance with coefficients of determination (R2) exceeding 0.997 for all deviations. This work conclusively shows that a simplified model based on intrinsic material and tool properties can effectively forecast machining outcomes, providing a powerful proof-of-concept tool for virtual process optimization to enhance precision and promote resource-efficient manufacturing of high-tolerance polymer components. © The Author(s) 2026
Publication Date: 2025
Nanomedicine Research Journal (24767123)10(1)pp. 95-113
3D printing, also known as additive manufacturing, is an emerging technology with significant applications across various industries, including biomedical engineering. This study shows the diverse methods of 3D bioprinting and their capabilities. The fundamental components of 3D printing, including printers, inks, and software, are discussed, highlighting the importance of geometric infill. The study then delves into the three main bioprinting technologies: laser-based, extrusion, and inkjet printing, each with its unique strengths and weaknesses. The article emphasizes the crucial role of biological inks, or bioinks, in achieving the desired mechanical, chemical, and morphological properties of printed tissues and organs. Hydrogels, in particular, are highlighted as promising bioinks due to their biocompatibility, swelling properties, and ability to be modified for specific applications. The study examines both physical and chemical gelation mechanisms, discussing the advantages and limitations of each approach. The significance of crosslinking, whether achieved via photo crosslinking or chemical crosslinkers, is highlighted due to its vital role in preserving the structural integrity and mechanical properties of printed constructs. Furthermore, hybrid hydrogel development, comprising synthetic and natural polymers, is investigated as a strategy to synergistically combine the advantageous properties of both material classes. This study concludes by showing the significant progress made in the field of 3D bioprinting, while acknowledging the ongoing challenges in fully replicating the complexity of natural tissues and organs. The study shows the need for continued research and development to advance this technology and its applications in the biomedical field. © 2025 Tehran University of Medical Sciences. All rights reserved.
