Project: DigiLignin

Lignin, the second most abundant biopolymer on Earth, is considered a promising renewable aromatic macro-polyol to replace the conventional fossil-derived polyols used in polyurethanes (PU). Despite the long history of lignin-PU synthesis, several issues prevent the widespread integration of lignin in PU products: lack of low molecular weight lignin fractions suitable for PU products formulation, and the intrinsic complexity of designing lignin-based biopolymer systems. As a result, lignin integration in PU remains a time-consuming task based on lengthy trial-and-error approaches. Digitalization tools are expected to speed-up PU systems design with respect to properties and processes. To do so, structure-property relations and process models must be developed from theory rather than extensive experimentation. The DigiLignin project addresses these challenges of lignin-based PU design by creating a model that could predict the structure-property relationships via a combined Machine Learning (ML) and quantum mechanical (QM) modelling approach. This ML-QM model will be considered as a robust digital building block to facilitate and speed-up the design of lignin-PU. Overall, DigiLignin promises to rationalize the design of lignin-PU and to accelerate the commercialization of more sustainable materialswith lower carbon footprints. In the future, the predictive model could be extended and shared via user-friendly apps with PU users for a wide range of applications.

Acknowledgement financial/compute support

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