Biodegradable Interactive Materials

TL;DR

The paper addresses the environmental and scalability challenges of tactile interfaces by introducing Biodegradable Interactive Materials (IM) that encode information directly into material properties and are decoded by wearable devices. The approach fuses natural biomatter with conductive and magnetic fillers to realize multimodal information channels—electrical, magnetic, and surface textures—achieving a total capacity of $12$ bits ($4096$ classes) across chip-less interfaces, with end-to-end prototypes spanning millimeter to decimeter scales. The materials are backyard-compostable and demonstrate natural degradation within $21$ days, and a cradle-to-grave LCA suggests environmental advantages over conventional embedded-sensor systems. Collectively, the work advances a sustainable path toward the Internet of Materials by enabling pervasive, sensor-translation of tactile data via wearables, without embedded electronics in the interfaces themselves.

Abstract

The sense of touch is fundamental to how we interact with the physical and digital world. Conventional interactive surfaces and tactile interfaces use electronic sensors embedded into objects, however this approach poses serious challenges both for environmental sustainability and a future of truly ubiquitous interaction systems where information is encoded into everyday objects. In this work, we present Biodegradable Interactive Materials: backyard-compostable interactive interfaces that leverage information encoded in material properties. Inspired by natural systems, we propose an architecture that programmatically encodes multidimensional information into materials themselves and combines them with wearable devices that extend human senses to perceive the embedded data. We combine unrefined biological matter from plants and algae like chlorella with natural minerals like graphite and magnetite to produce materials with varying electrical, magnetic, and surface properties. We perform in-depth analysis using physics models, computational simulations, and real-world experiments to characterize their information density and develop decoding methods. Our passive, chip-less materials can robustly encode 12 bits of information, equivalent to 4096 unique classes. We further develop wearable device prototypes that can decode this information during touch interactions using off-the-shelf sensors. We demonstrate sample applications such as customized buttons, tactile maps, and interactive surfaces. We further demonstrate the natural degradation of these interactive materials in degrade outdoors within 21 days and perform a comparative environmental analysis of the benefits of this approach.

Figures (13)

• Figure 1: Bio-inspired information encoding through natural material properties. 1) An off-of-shelf bio-impedance sensor can measure impedance changes when touching a conductive surface, akin to bees sensing a flower's electric field. 2) Magnetometers can measure magnetic field strength, akin to turtles sensing intensity and inclination of Earth's magnetic field for navigation. 3) Microphones can detect the vibrations of human motion on a tangible surface, akin to spiders can sense vibrations when prey touch their webs.
• Figure 2: Interactive material fabrication. We mix unrefined biomatter powder with varying weight percentages of minerals like graphite and magnetite, then add water for 3D printing or direct compression molding and laser cutting to create interactive everyday objects with surface features.
• Figure 3: Biodegradation in soil. Photographs showing the biodegradation process of Biodegradable Interactive Materials disposed directly on backyard soil, the specimen is observably decomposed on Day 21. Note that on Day 7, a (white) centipede was eating the specimen (bottom-left).
• Figure 4: Electrical simulation. a, Simulation setup of conductive interactive material and bio hand model in CST Studio Suite, as well as pitch angle offset setup and the cross-section of bio hand model showing various body tissues. b, Simulated S11 results, showing the amount of signal reflected caused by impedance mismatch resulting from contact with external surfaces with corresponding conductive levels. c, Simulated S11 results respond to varying pitch angle offsets.
• Figure 5: Electrical implementation. a,b, Characterized conductivity (a) and flexural strength (b) of Interactive Materials with different proportions of graphite and magnetite, data is presented as mean (SD) of 3 (a) and 9 (b) specimens in 1000 (a) and 1 (b) measurements of each proportion. c, Measurements from a wearable bio-impedance sensor when user's finger is in contact with external surfaces of corresponding conductive states ($N=2\times5\times2\times30=600$ frequency sweeps per state, shaded region indicates SD).