 | The optimization of composition–process–structure–property relationships through the integration of advanced manufacturing, high-performance composites, and data-driven design holds pivotal significance for future sustainable developments. A combination of additive manufacturing (AM) and machine learning (ML) plays a crucial role in the development of transformative materials solutions for construction, aerospace, and energy applications.
Dr. Shajed pioneered the first-ever sustainable 3D printing of wood structures using direct ink writing (DIW), transforming waste wood into a reusable, water-based viscoelastic ink composed of lignin and cellulose. By replicating the fundamental constituents of natural wood, he created printed structures that maintain natural texture while substantially enhancing mechanical properties through the introduction of long natural fibers. To address safety requirements, he introduced a bio-based fire retardant, enabling these 3D-printed wood composites to achieve the highest UL-94 fire safety rating (V-0) with self-extinguishing behavior. |
Dr. Shajed also focuses on the valorization of petroleum by-products, converting asphaltenes into high-value asphaltene-derived flash graphene (AFG) via Flash Joule Heating. These AFG-reinforced epoxy nanocomposites demonstrated a 37% increase in tensile strength and a 155% improvement in thermal conductivity, while also providing corrosion resistance three orders of magnitude greater than neat epoxy. Additionally, his research on carbon nanotube fibers (CNTFs) revealed an exceptional interfacial shear strength of 115.85 MPa, establishing new manufacturing pathways for high-performance structural composites.
He also utilized origami-inspired metamaterial designs to overcome the inherent brittleness of ceramics, thus producing damage-tolerant ceramic metamaterials using stereolithography (SLA). Drawing inspiration from the hierarchical structure of nacre, he coated complex Miura-ori ceramic architectures with a hyperelastic polymer (PDMS). This bio-inspired strategy redirected catastrophic failure into a graceful, compartmental mode, significantly improving the toughness, failure strain, and energy absorption of the composite.
Complementing his experimental work, Dr. Shajed implemented a multi-fidelity physics-informed neural network (MPINN) framework in conjunction with molecular dynamics (MD) simulations to accelerate materials design. This approach dramatically reduced computational costs by 68% while maintaining high predictive accuracy for thermophysical properties such as total energy, pressure, and diffusion coefficients. He further utilizes Bayesian optimization and genetic algorithms to link microstructure to macroscopic performance, allowing for the efficient discovery of optimal material configurations.
Overall, Dr. Shajed’s research contributes significantly to the development of the next generation of functional materials and offers potential solutions to industrial challenges through the synergy of sustainable manufacturing, advanced composite engineering, and intelligent materials design.