“A minimal-data approach for spatially resolved parameter analysis of coupled graphene nanomechanical resonators” appears in ScienceAdvances. The article presents NetMAP, a new mathematical framework for characterizing the fundamental components of resonator networks and compares this new method to the traditional technique of curve fitting.
Horowitz explains that “resonance underlies everything from the synchronized shaking of bridges and orbital patterns of Jupiter’s moons to the precise timekeeping of quartz watches.
“When resonant systems are linked together, we see complex phenomena. For example, the interconnected neurons of the brain are a natural coupled resonant system,” she said.
She described the networks as a layer of graphene (a thin sheet of carbon) that is “stretched over a grid of nanoscale pillars like a tiny trampoline park,” and that “vibrations in the graphene can travel from one square to another, coupling the motion of neighboring regions and potentially providing processing power for future computer chips.”
Noting that while these nanomechanical resonator networks are promising, even invisible variations can completely change their properties. Because the graphene the researchers were using was only a few atoms thick, it was too delicate to study using existing methods. “We had to work with the vibrations we could measure and dig deeper mathematically,” Horowitz said.
According to Horowitz, “unlike conventional methods that require many measurements across a wide range of frequencies, NetMAP extracts key parameters using only two frequencies—a mathematically streamlined alternative to the iterative approach of traditional curve fitting.” She said that though the team has so far characterized only small networks, they anticipate that NetMAP will scale to larger systems given sufficiently precise measurements.
Posted May 5, 2026
