Cornell research team has created a novel approach to constructing intricate microscale devices that is motivated by the functioning of proteins and hummingbird beaks.
The paper was published in Proceedings of the National Academy of Sciences on August 14th, 2023. Itay Griniasty, a Schmidt AI postdoctoral researcher in Itai Cohen’s group in the College of Arts and Sciences, is the lead author.
Building smaller and smaller computers is more than just reducing the components. While macroscopic machines are often compartmentalized, splitting a task into little bits and allocating each to a distinct portion of the machine, proteins, the prototypical microscopic machines responsible for most of life, are structured differently.
Tasks are frequently accomplished by coordinated movement of all of the protein’s components, making them more resistant to the chaos of the microscopic environment.
Previously, Cohen’s group used origami principles to create a variety of microscale devices, ranging from self-folding structures to walking microrobots, that are novel for their size yet possess remarkably simple functions. Adding functionality to origami sheets proves to be a difficult undertaking.
The machines that we have made so far are very, very simple. But when we start thinking about how to increase the functionality in systems that are highly coupled, we started realizing that every time you move one part of the machine, all the other parts move. It’s maddening, because you can’t isolate anything, it is all connected in these sheets. Then we started asking how does this get done in the microscopic world.
Itai Cohen, Professor, Physics, College of Arts and Sciences, Cornell University
They described a protein as a machine that switches between states in response to tiny changes in a few parameters. The researchers were motivated by a macroscale example of this sort of functionality: the hummingbird.
Andy Ruina, the John F. Carr Professor of Mechanical Engineering, demonstrated in a 2010 study how a hummingbird’s beak can be “smoothly opened and then snapped shut through an appropriate sequence of bending and twisting actions by the muscles of the lower jaw.”
This mechanism is described by a mathematical concept known as a cusp bifurcation, in which the beak can have a single stable state, i.e., closed, or two stable states, both open and closed, depending on the pressures exerted by the jaw muscles. The cusp bifurcation occurs when a single stable state separates into two stable ones.
The benefit of working around a cusp bifurcation is that it gives two crucial design aspects. The first is topological protection, which assures a device’s function is consistent so that even if the jaw muscles pull slightly differently, the beak can still open and close.
The second benefit is a lever advantage, which guarantees that the muscles only need to move little to produce a significant shift in the beak. These are the exact components required to achieve function at the microscale.
Cohen, Griniasty, and their colleagues wondered if they could increase the number of states structured around a bifurcation from two to dozens, if not hundreds. This expansion would enable the creation of machines capable of performing complicated functions.
Instead of compounding compartmentalized function, these capabilities would emerge from the entire object. It is dancing together.
Itay Griniasty, Schmidt AI Postdoctoral Researcher, Cornell University
Teaya Yang ‘22 and Yuchao Chen ‘19, both co-authors, were recruited to construct a proof-of-concept macroscale magneto-elastic model featuring a butterfly bifurcation that allowed the system to snap or seamlessly transition between three stable states.
The model was made up of two panels, one of which moved in a plane while the other rotated freely around a fixed hinge. Each panel was adorned with nine magnets that interacted with one another to form complicated interactions similar to those observed in proteins.
The main problem was figuring out how to create magnetic patterns that would cause the required bifurcation. Griniasty and David Hathcock resolved the challenge by devising an algorithm based on the work of A.R. Bullis Professor Emeritus of Mathematics (A&S), John Guckenheimer.
Cohen added, “If we tried to just guess these magnetic patterns, to generate multiple equilibria, we would run out of computing power. So Itay designed a very nice algorithm that simplifies the search.”
The concept will then be demonstrated at the microscale.
Cohen further stated, “For a 100-micron machine, like the typical robots that we make, Itay calculated that we could achieve 20 separate states. That is kind of what we envision could be made at the microscale, a machine where I use an actuator to move one of the panels, and the configuration of the entire machine could switch between 20 different configurations. You could have a machine that could, let’s say, locomote through fluid, or maybe do a complicated grasping action.”
Paul McEuen, the John A. Newman Professor of Physical Science (A&S), and James Sethna, the James Gilbert White Professor of Physical Sciences (A&S), are co-authors.
The National Science Foundation DMREF program, the Sloan Foundation, the Kavli Institute at Cornell, the Air Force Office of Scientific Research, the Cornell Laboratory of Atomic and Solid State Physics, an NSF Graduate Research Fellowship, and the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship all contributed to the research.
Yang, T., et al. (2023) Bifurcation instructed design of multistate machines. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2300081120