AI-guided design of compatible composites of birch lignin and polylactic acid for 3D printing

Abstract

In this study, a novel design of composites involving polylactic acid (PLA) and alkaline-extracted birch lignin was explored to produce a sustainable replacement material for additive manufacturing or 3D printing. Due to the large difference in chemical characteristics of two composite components, the challenge focuses on finding an optimal recipe where lignin can partially replace PLA however still being able to maintain the performance as a material in different applications. The optimization of this recipe would have required a large number of experiments, thus in this study, machine learning was implemented in the design of experiments (DOE) to try to optimize data utilization, thereby attempting to reduce the required number of data points and experiment numbers. This implementation introduces a secondary focus of the study, which centers on streamlining the composite creation process and optimizing these composites to the desired characteristics. It is known that when blending PLA with a large quantity of unmodified lignin, the material’s mechanical properties begin to degrade, making the material brittle. To counter this, two different plasticizers, polyethylene glycol (PEG2000) and triethyl citrate (TEC) were introduced in various amounts. To analyze the material characteristics of the composites, the thermal, mechanical, and morphological properties of the composites were analyzed. Results show that when increasing the amount of plasticizer, the glass transition temperature (Tg) decreases rapidly with blends showing a Tg as low as 7 °C. Materials were also observed to retain a much more elastic characteristic in the form of having a torsional strength of 3182.79 Mpa with a lignin to PLA ratio of up to 25% w/w.