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…

Continuum micromechanics homogenization provides an efficient framework to relate the microstructural features of heterogeneous materials to their macroscopic mechanical response. The microstructure is idealized as an assembly of interacting matrix–inclusion problems, each governed by Eshelby’s…

New publication! LFNMR-Informed Multi-Phase Moisture Modelling of Wood Biodegradation by Coniophora puteana

Transparent wood has potential not only as a sustainable substitute for glass, but also as a multifunctional energy material whose value lies in the integration of diffuse light management, thermal insulation, mechanical load bearing and sustainability. Its widespread adoption will require…

Wood is an anisotropic material, which affects its performance under different loading conditions. To understand the origin of surface failures occurring in wood under mechanical disintegration loads, an accurate investigation of its elastic and plastic behaviour is required. This study introduces a…

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