Carbon footprint of 3d-printed bone tissue engineering scaffolds: an life cycle assessment study

• Main Authors: Tajurahim, Nurul Ainina Nadhirah, Mahmood, Salwa, Mat Saman, Muhamad Zameri, Ngadiman, Nor Hasrul Akhmal
• Format: Article
• Published: International Journal of Environmental Science and Development 2022
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/id/eprint/101120/; http://dx.doi.org/10.18178/ijesd.2022.13.3.1373
• Institution: Universiti Teknologi Malaysia

The bone tissue engineering scaffolds is one of the methods for repairing bone defects caused by various factors. According to modern tissue engineering technology, three-dimensional (3D) printing technology for bone tissue engineering provides a temporary basis for the creation of biological replacements. Through the generated 3D bone tissue engineering scaffolds from previous studies, the assessment to evaluate the environmental impact has shown less attention in research. Therefore, this paper is aimed to propose the Model of life cycle assessment (LCA) for 3D bone tissue engineering scaffolds of 3D gel-printing technology and presented the analysis technique of LCA from cradle-to-gate for assessing the environmental impacts of carbon footprint. Acrylamide (C3H5NO), citric acid (C6H8O7), N,N-Dimethylaminopropyl acrylamide (C8H16N2O), deionized water (H2O), and 2-Hydroxyethyl acrylate (C5H8O3) was selected as the material resources. Meanwhile, the 3D gel-printing technology was used as the manufacturing processes in the system boundary. The analysis is based on the LCA Model through the application of GaBi software. The environmental impact was assessed in the 3D gel-printing technology and it was obtained that the system shows the environmental impact of global warming potential (GWP). All of the emissions contributed to GWP have been identified such as emissions to air, freshwater, seawater, and industrial soil. The aggregation of GWP result in the stage of manufacturing process for input and output data contributed 47.6% and 32.5% respectively. Hence, the data analysis of the results is expected to use for improving the performance at the material and manufacturing process of the product life cycle.