Developing 3D printing high‐performance biodegradable materials is important to protect the environment and deal with emergencies such as COVID‐19. Fused deposition modeling (FDM), one of the 3D printing methods, has many advantages, such as low cost and wide range of materials. However, the weak interlayer adhesion is an important factor restricting the development of FDM. In addition to the influence of material properties, the optimization of 3D printing parameters is also an important means to give full play to the inherent properties of materials. The optimal 3D printing parameters are conducive to the diffusion and entanglement of molecular chains between adjacent layers. PLA/PBAT/PLA‐g‐GMA (70/30/10 wt%, PLA‐g‐GMA was a compatibilizer synthesized in our lab) was used as the research object. This work aims to analyze the mechanical properties response of biodegradable polymers products manufactured through FDM. Herein, the effect of 3D printing parameters including layer thickness, nozzle temperature, printing speed and platform temperature have been systematically investigated by orthogonal experimental design. The result showed that the excellent performance of 3D printing specimen was obtained when the layer thickness was 0.15 mm, the printing speed was 50 mm·s−1, the nozzle temperature was 200°C and the platform temperature was 50°C. The SEM images showed that the optimal 3D printing products had the best interlayer adhesion and the lowest porosity. Undergoing optimization of 3D printing processing, the yield strength and elongation at break of specimen increased by 115% and 229%, respectively. In this paper, the interlayer adhesion and mechanical properties of 3D printing products can be significantly improved by simply optimizing the 3D printing parameters without complex material modification. This work provided a new method for improving the interlayer adhesion of FDM and the mechanical properties of FDM products.
Aim and Objective:
Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the
feature metric hidden in the sequence. Otherwise, the graphical representation of biologic sequence is an important tool for
sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences.
Materials and Methods:
The transition probability is used to describe amino acid combinations of protein sequences. The
combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition
probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from transition probability graph to transition probability vector by k-order transition probability graph. Transition
entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is
applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.
Results:
By constructing a phylogenetic tree, we find that the results of each application are consistent with other studies.
Conclusion:
The graphical representation proposed in this article is a practical and correct method.
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