Thermomechanical properties of bio-based epoxy biocomposites reinforced with Dracaena draco fibrils and biochar: performance optimization using artificial neural networks

Abstract

Driven by the urgent need to provide high-performance sustainable, high-performing composites as eco-friendly alternatives to petroleum-based materials in structural applications, this study explores enhancing partially bio-based epoxy (E) biocomposites with sustainable, biosourced Dracaena draco fibers (DdFs) and waste‑derived biochar (BC) to improve their mechanical and thermal properties. Short DdFs were extracted via water retting and added at 30 wt% to an E matrix, while BC from the same plant was incorporated at 0-5 wt%. Composites were produced by hand lay-up and vacuum bagging, then analyzed using thermogravimetric analysis, X-ray diffraction (XRD), tensile and flexural testing, and dynamic mechanical analysis (DMA). Results showed that 30 wt% DdF significantly increased tensile and flexural strengths from 43.49 to 107.95 MPa and from 29.38 to 50.86 MPa, respectively, compared to pure E. Adding BC further boosted thermal stability, raising residual mass from 12% to 18% at 5 wt% and shifting degradation peaks to higher temperatures. The optimal reinforcement was achieved at a 3 wt% BC loading, yielding peak values of 107.95 MPa for tensile strength and 50.86 MPa for flexural strength. DMA confirmed enhancements in stiffness and glass transition temperature with increased BC, while XRD revealed reduced crystallinity due to BC's amorphous structure. Alongside experimental tests, an Artificial Neural Network (ANN) model was developed to predict the mechanical and thermal response between composition and performance. The ANN predictions closely aligned with experimental data (R² > 0.9 for most properties), identified optimal points, and generated response curves for untested compositions. These findings demonstrate the potential of DdF and BC as sustainable, waste‑derived reinforcements for high‑performance bio-based composites, illustrating how ANN modeling can effectively optimize material design.