A machine learning-based surrogate model for efficient wood microstructure generation compatible with a physics-based model is developed. The model is based on the U-Net neural network, a variant of convolutional neural network which, due to its architecture, is suitable for image-to-image…

This research presents an analysis of Transparent Wood (TW), a material currently under development, focusing on its perceptual and sensory aspects, specifically through sight, hearing, and touch. The objective of this study is to examine TW’s current perception to propose clear and effective…

Realistic 3D microstructure models of wood fiber networks (WFNs, e.g., paper, molded fibers, hot-pressed fibers, etc.) are of interest for numerical modeling of mechanical, optical, and other physical properties. One challenge is to numerically describe 3D high-density WFN (HD-WFN) models with…

Accurate characterization of transmittance and haze of transparent composites, such as transparent wood (TW), is challenging due to strong light-scattering effects. Reported TW data in the literature, particularly haze values, show substantial variability, which can be largely attributed to…

Microdroplet tests are widely used for interface characterization in fiber-reinforced composites. However, their interpretation remains challenging due to complex, non-uniform stress states and the interplay of multiple geometric and mechanical factors. These challenges are further amplified in…

Bringing a radical innovation from the laboratory to society is not always a straightforward process. Beyond technical performance, innovative materials must be understood, accepted, and valued by the people who will potentially design, use, and experience them. This challenge is particularly relevant for such emerging sustainable materials as Transparent Wood (TW), a new bio-based material that combines the natural structure of wood with the optical properties of transparent materials.

Artificial intelligence (AI) is a broad term referring to the capability of certain computational systems to perform tasks typically associated with human intelligence, such as reasoning, learning, creativity or pattern recognition. Recent progress in development of AI systems, particularly in their subset known as machine learning (ML), has been helpful in many fields of science and technology. The idea behind ML is that the computational system instead of being explicitly told what to do at each step, is trained with the available data to perform certain tasks it has been designed to.

Transparent Wood (TW) represents a fascinating development in materials science, transforming the ancient, ubiquitous natural composite that is wood into a material possessing optical clarity while retaining many of its inherent advantageous properties. Historically utilized extensively across numerous sectors from construction to art and furniture, wood is renewable and recyclable, aligning perfectly with contemporary circular economy principles. However, its natural opacity, primarily due to the presence of lignin and light scattering within its complex cellular structure, limits its application where transparency is required. The concept of transparent wood was initially explored by Fink in 1992, but it was rediscovered and rigorously investigated by independent research groups starting about a decade ago.

This Guide aims to provide people who are not familiar with Transparent Wood (TW) with an overview on this material, which was discovered in 1992 and thoroughly investigated from 2015 onwards.

Modeling of Transparent Wood - Introduction on modeling pristine selected woods at the micro-, macro-, and meso-scales