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Simulation Studies of Interactions Between Models of Graphene Oxide and Amyloid-Beta Fibrils
No full textAccording to the amyloid cascade hypothesis, neurodegeneration in Alzheimer���s Disease (AD) is an outcome of amyloid-�� (����) plaque and neurofibrillary tangle formations. A�� overproduction, aggregation, clearance failure and fibrillation lead to amyloid plaque formation, contributing to inflammation and cell-death. There has been a significant search for anti-amyloid agents and methods of preventing A�� aggregation. Nanomaterials have been investigated for their capacity to modulate the aggregation of A�� peptides. Specifically, experiments depicted that graphene oxide (GO) can inhibit A�� monomer fibrillation, while a particular study suggested GO can lead to dissociation of fibrils. Here, in this study, we aimed to use computational methods including extensive docking and molecular dynamics (MD) simulations to provide insights into the interactions between GO and A�� fibrils. GO was represented by a model of approx. 20.2*14.8 �� dimensions, while both A��40 fibril and A��42 fibril were represented by three ��-sheet strands from corresponding experimentally resolved structures of ordered fibrils of A��40 and A��42, respectively. The lowest binding energy docked poses were used as starting conformations in MD simulations to elucidate how GO interacts with fibrils and uncover how GO could inhibit further fibril elongation by binding to preformed fibrils, or potentially partly dissociate formed fibrils. We observed the capacity of GO to form different types of interactions with A��40 fibrils and A��42 fibrils, termed by us as ���partial N-terminal-wrapping���, ���partial N-terminal dissection���, or bind at different positions of the A�� fibril and potentially lead to ���perturbation��� of the fibrils��� structures, in comparison with additional simulations performed in the absence of GO. Our simulations provided an in-depth investigation on the structural and energetic features associated with different types of interactions between different parts of GO and different domains of A��, and interestingly suggest that GO may be energetically favored to form different interactions with A��40 fibril and A��42. We additionally provide a comparison between interactions formed by ���� with Aducanumab and Gantenerumab compared to GO. This study provides a paradigm of how computational methods can be used to uncover interactions between a material with preformed fibrils, providing important insights into inhibition, which can be also potentially used for the design of novel anti-amyloid agents
Optimization of Process Parameters for Additive Manufacturing of Polymers
No full textPolyethylene (PE), a material usually found in plastic waste and known for its desirable mechanical properties, faces challenges in additive manufacturing (AM) due to issues like excessive shrinkage and poor adhesion. This research presents a framework to upcycle PE waste into valuable feedstock for fused deposition modeling (FDM), addressing sustainability in materials and manufacturing processes. Low-density PE (LDPE) and high-density PE (HDPE) pellets, categorized as industrial waste, were sourced from the Qatar Petrochemical Company (QAPCO). To improve PE's printability, Pre-puff Polystyrene (PS) beads were blended with varying ratios of LDPE and HDPE with a melt flow index (MFI) of 8 and 44, respectively. A 5 wt% addition of styrenic block copolymer (SEBS) was used to ensure blend compatibility and homogeneity. After blending the constituents in a twin-screw extruder, the blends were granulated, dried, and extruded into standard 1.75 mm filaments using a 3DEVO Composer 350. Filament quality was evaluated using tensile strength tests, filament roundness, and printing trials, which involved printing a single perimeter cube with a 0.6 mm nozzle at various temperatures. This helped identify the optimal blend for FDM and examine the effect of FDM process parameters on the printable blend, including dimensional accuracy, shrinkage, and warpage, in addition to generating a printability index for the blends. The optimal blend considered included 40 wt% of LDPE within its composition, which was the main material to be additively manufactured due to complexities with its properties and the required properties for printing, proving the achievement of the goals. Our findings illuminate the significant role of AM in promoting sustainable manufacturing practices and enhancing material properties, marking a step forward in the utilization of recycled materials in advanced manufacturing processes
