Zihan Oliver Zeng

Zihan Oliver Zeng, David Marshall Porterfield, Upinder Kaur

Multimodal environmental monitoring conventionally requires a suite of purpose-built transducers, each constrained to a predefined target. Here, we present MIND (Mycoponically Integrated Network Device), a platform that sustains living fungal mycelial networks on porous bioceramic substrates and reads their passive extracellular voltages. Without hardware modification, a single device produces distinguishable bioelectrical responses to 14 stimuli spanning chemical, optical, mechanical, thermal, and biological domains. We show that steady-state intensity responses follow Hill-type calibration functions conserved across five phylogenetically diverse fungal species, and that multichannel decoding recovers stimulus duration, spatial origin, and continuous position from the bioelectrical output. Strain selection tunes sensitivity without hardware redesign. The platform restores full electrophysiological function within 72 h of mechanical damage and has maintained calibration-quality readout for more than 11 months of continuous operation. These results position fungal electrophysiology as a measurement platform for sensing applications in which the full stimulus set, the electrode geometry, and the recovery requirements cannot be fully specified in advance.

Zihan Oliver Zeng, Jiajun An, Preston Luk et al.

Soft robots are compliant but often cannot support loads or hold their shape, while rigid robots provide structural strength but are less adaptable. Existing variable-stiffness systems usually operate at the scale of whole segments or patches, which limits precise control over stiffness distribution and virtual joint placement. This paper presents the Variable Stiffness Lattice Skin (VSL-Skin), the first system to enable individually addressable voxel-level morphological control with centimeter-scale precision. The system provides three main capabilities: nearly two orders of magnitude stiffness modulation across axial (15-1200 N/mm), shear (45-850 N/mm), bending (8*10^2 - 3*10^4 N/deg), and torsional modes with centimeter-scale spatial control; the first demonstrated 30% axial compression in phase-change systems while maintaining structural integrity; and autonomous component-level self-repair through thermal cycling, which eliminates fatigue accumulation and enables programmable sacrificial joints for predictable failure management. Selective voxel activation creates six canonical virtual joint types with programmable compliance while preserving structural integrity in non-activated regions. The platform incorporates closed-form design models and finite element analysis for predictive synthesis of stiffness patterns and joint placement. Experimental validation demonstrates 30% axial contraction, thermal switching in 75-second cycles, and cut-to-fit integration that preserves addressability after trimming. The row-column architecture enables platform-agnostic deployment across diverse robotic systems without specialized infrastructure. This framework establishes morphological intelligence as an engineerable system property and advances autonomous reconfigurable robotics.