Acoustic levitation · phased ultrasound array
Control matter
with sound.
An acoustic levitation platform with an AI-based controller. It positions and shapes fields of particles, cells, and droplets using phased transducer arrays. Contactless. No moving parts.
What we're building
MageHand is a general-purpose acoustic levitation platform: phased transducer arrays driven by a controller trained with reinforcement learning.
Acoustic manipulation has existed in research labs for decades. What has kept it from becoming a platform technology is control: every new task, material, or geometry requires long expert hand-tuning. There is no general-purpose controller.
Reinforcement learning changes that. A single system can adapt to new particle types, geometries, and tasks. It abstracts away the expert tuning, so lab workflows can be reconfigured overnight, not redesigned from scratch.
- Array
- phased ultrasonic transducers
- Control
- learned policy, field-level
- Status
- verified in simulation
Applications
Space & microgravity
In orbit, surface tension rules liquids and loose particles drift freely. Acoustic fields offer programmable control. No contact, no contamination.
Programmable chemical laboratory
Build small molecules in situ from basic components. Software-defined synthesis pipelines replace bespoke fluidic hardware per experiment.
Life sciences & lab automation
Label-free manipulation of living cells, microparticles, and liquid droplets for lab-on-chip, diagnostics, and drug delivery.
Acoustic manufacturing
Position dust and powder fields in free space. Additive manufacturing without nozzles, substrates, or binders.
Aerosol engineering
Concentrate dilute aerosol clouds into dense pockets. Increase detector signals to reveal otherwise unreadable measurements.
Filterless filtration
Acoustic fields concentrate and remove coarse dust and pollens from gas streams with no replaceable filter element.
Why MageHand
Field-level control
We don't move one particle at a time. We shape the trajectory of everything in the field, simultaneously.
AI-native
AI can adapt to new materials and geometries. Every conventional approach needs trial and error re-engineering.
Simulation-first
Trained on GPU clusters across billions of physics timesteps. Proven before heavy investment in hardware.
Current status
The AI controller works in simulation. Hardware is next.
Interested in collaborating or investing? Reach out.