Accelerated engineering and genetic programming of wood-based living composite materials.
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| Title: | Accelerated engineering and genetic programming of wood-based living composite materials. |
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| Authors: | Schmachtenberg, Rosanne1,2 (AUTHOR), Mayer, Hanna1,3 (AUTHOR), Litwin, Tim4,5 (AUTHOR), Conrad, Stefan2,6 (AUTHOR), Auth, Philipp2,6 (AUTHOR), Teutloff, Nele1,7 (AUTHOR), Falkenstein, Johannes1 (AUTHOR), Elberskirch, Linda1 (AUTHOR), Goodarzi, Payman1 (AUTHOR), Rauer, Karolin8 (AUTHOR), Wrublewsky, Selina9 (AUTHOR), Laschke, Matthias W.9 (AUTHOR), Weber, Achim8,10 (AUTHOR), Masselter, Tom2,6 (AUTHOR), Finkbeiner, Matthias1,7 (AUTHOR), Speck, Thomas2,6,11 (AUTHOR), Kreutz, Clemens4,5 (AUTHOR), Weber, Wilfried1,3,4 (AUTHOR) Wilfried.weber@leibniz-inm.de |
| Source: | Materials Today. Sep2026, Vol. 98, pN.PAG-N.PAG. 1p. |
| Subjects: | Engineered wood, Bacterial genetic engineering, Biodegradable materials, Mechanical behavior of materials, Optimization algorithms, Manufacturing industries, Biomimetic materials, Genetic software |
| Abstract: | [Display omitted] • Design of wood composites with genetically programmable structure and function. • AI-driven optimisation towards desired mechanical material properties. • Scalable manufacturing of free-form demonstrator objects. Engineering cells to fabricate and program bio-based materials presents a sustainable alternative to petroleum-derived composites while simultaneously enabling the integration of advanced functionality. This is particularly relevant for engineered wood composites, which are widely used in construction, yet rely on petrol-based, non-biodegradable binders. Here, we develop bio-programmed wood composites using engineered bacteria, addressing both functional enhancement and sustainability. To navigate the large design space linking genetic programs, material composition, and processing conditions to mechanical performance, we combine lab automation with a pretrained transformer model for in-context prediction. This approach enables rapid identification of formulations yielding desired mechanical properties. Beyond mechanical properties, we introduce programmable features such as optogenetically patterned in situ pigmentation, local porosity control, and autonomous damage reporting. We demonstrate the applicability of our approach through manufacturing macro-scale furniture prototypes. Our platform establishes a blueprint for the accelerated, AI-driven development of sustainable, living multifunctional materials with applications in construction and beyond. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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