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Robotic additive manufacturing of lichen composites for air quality monitoring

Nikolaidou, Anna; Jorgensen, Tavs

Authors

Anna Nikolaidou Anna.Nikolaidou@uwe.ac.uk
Senior Lecturer in Architecture and Design



Abstract

In the light of an unprecedented climate emergency, there is an urgent need to reconsider the materials we use for the construction of our buildings and to explore alternative material systems that are abundant, easy to source, and exhibit increased climate resilience and adaptability. Materials with these attributes have been developed by living systems. We propose the development of hybrid materials from living lichens and abiotic components for air pollutant sensing, using a 3D digitally driven paste delivery system. Lichens are excellent indicators of air pollutants, they present resilience to extreme environmental conditions and are abundant, can self-grow and self-regulate. The lichen species Flovoparmelia caperata, Parmotrema perlatum and Xanthoria parietina were used for the production of the samples. We employed a robust photographic recording system that allowed the morphological monitoring of the samples. We assessed colonisation tendencies and performed statistical analysis of changes in pigmentation distribution to understand substrate-lichen correlations and the metabolic activity change over time. Our findings demonstrated that three combinations of lichen species-substrates presented optimal integration and were selected as most suitable for 3D printing. We also report that one sample demonstrated excellent colonisation behaviour and less changes in its metabolic activity over time.

Citation

Nikolaidou, A., & Jorgensen, T. (in press). Robotic additive manufacturing of lichen composites for air quality monitoring.

Conference Name PLEA Rethinking resillience
Conference Location Poland
Start Date Jun 25, 2024
End Date Jun 28, 2024
Acceptance Date Jan 24, 2024
Deposit Date Feb 4, 2024
Keywords hybrid living materials; lichens; robotic 3D printing; biosensing; sustainability
Public URL https://uwe-repository.worktribe.com/output/11658116