Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning

2-Pyrone-4,6-dicarboxylic
acid (PDC) can be produced on a large
scale from lignin by transformed bacteria, and its use as a bifunctional
monomer to synthesize biomass-based polymers has been reported. Recently,
excellent adhesive properties of the resulting biomass-based polymers
were also reported, but their performance has not yet been optimized.
In this study, we focus on improving the adhesive properties of PDC-based
polyurethanes (PUs) by combining experiments and machine learning
(ML). We synthesized an initial data set of 25 adhesive samples from
different polyols and isocyanates with different isocyanate-to-hydroxyl
ratios (r). Adhesive strengths were measured after
hot-pressing at varying temperatures (Theat, °C) and durations (theat, h),
following a Taguchi L25 orthogonal design. Gaussian process-based
Bayesian optimization (BO) was employed to identify an optimal PDC-based
PU adhesive as a function of polyol type, isocyanate type, r ratio, heating temperature, and time with an improved
adhesive strength of 10.04 ± 1.26 MPa after only five iterations.
This approach highlights the effectiveness of BO in guiding experimental
conditions for an enhanced performance. Random Forest regression was
also used as an alternative ML approach and supported the conclusions.
Overall, this study demonstrates the potential of the BO in accelerating
the development and optimization of novel adhesive materials.