Tag Archives: Purdue University
A rectangular robot as tiny as a few human hairs can travel throughout a colon by doing back flips, Purdue University engineers have demonstrated in live animal models. Why the back flips? Because the goal is to use these robots to transport drugs in humans, whose colons and other organs have rough terrain. Side flips work, too. Why a back-flipping robot to transport drugs? Getting a drug directly to its target site could remove side effects, such as hair loss or stomach bleeding, that the drug may otherwise cause by interacting with other organs along the way.
The study, published in the journal Micromachines, is the first demonstration of a microrobot tumbling through a biological system in vivo. Since it is too small to carry a battery, the microrobot is powered and wirelessly controlled from the outside by a magnetic field.
“When we apply a rotating external magnetic field to these robots, they rotate just like a car tire would to go over rough terrain,” said David Cappelleri, a Purdue associate professor of mechanical engineering. “The magnetic field also safely penetrates different types of mediums, which is important for using these robots in the human body.”
The researchers chose the colon for in vivo experiments because it has an easy point of entry – and it’s very messy. “Moving a robot around the colon is like using the people-walker at an airport to get to a terminal faster. Not only is the floor moving, but also the people around you,” said Luis Solorio, an assistant professor in Purdue’s Weldon School of Biomedical Engineering. “In the colon, you have all these fluids and materials that are following along the path, but the robot is moving in the opposite direction. It’s just not an easy voyage.”
But this magnetic microrobot can successfully tumble throughout the colon despite these rough conditions, the researchers’ experiments showed. The team conducted the in vivo experiments in the colons of live mice under anesthesia, inserting the microrobot in a saline solution through the rectum. They used ultrasound equipment to observe in real time how well the microrobot moved around.
The United States is seeing an increase in the number of neurological diseases. Stroke is ranked as the fifth leading cause of death, with Alzheimer’s being ranked sixth. Another neurological disease – Parkinson’s – affects nearly 1 million people in the U.S. each year. Implantable neurostimulation devices are a common way to treat some of these diseases. One of the most commonly used elements in these devices is platinum microelectrodes – but it is prone to corrosion, which can reduce the functional lifetime of the devices. Purdue University researchers have come up with a solution to help – they are adding a graphene monolayer to the devices to protect the microelectrodes.
“I know from my industry experience that the reliability of implantable devices is a critical issue for translating technology into clinics,” said Hyowon “Hugh” Lee, an assistant professor in Purdue’s College of Engineering and a researcher at the Birck Nanotechnology Center, who led the research team. “This is part of our research focusing on augmenting and improving implantable devices using nano and microscale technologies for more reliable and advanced treatments. We are the first ones that I know of to address the platinum corrosion issue in neurostimulation microelectrodes.”
Lee said he learned about the advantage of using graphene from his colleague at Birck Nanotechnology Center, Zhihong Chen, who is an expert in graphene technology. The team has shown the graphene monolayer to be an effective diffusion barrier and electrical conductor.
“If you attempt to deliver more charge than the electrode can handle, it can corrode the electrode and damage the surrounding tissues,” Lee said. He also thinks that microscale electrodes are going to play a key role in the future with more demand for precise and targeted neurostimulation therapy. “We think neurosurgeons, neurologists, and other scientists in neuroengineering field will be able to use this electrode technology to better help patients with implantable devices for restoring eyesight, movement, and other lost functionalities.”
Lee and his team are working with the Purdue Research Foundation Office of Technology Commercialization on patenting and licensing the technology. They are looking for partners interested in licensing it.
The research has been published in the journal 2D Materials.
Computers and artificial intelligence continue to usher in major changes in the way people shop. It is relatively easy to train a robot’s brain to create a shopping list, but what about ensuring that the robotic shopper can easily tell the difference between the thousands of products in the store?
Purdue University researchers and experts in brain-inspired computing think part of the answer may be found in magnets. The researchers have developed a process to use magnetics with brain-like networks to program and teach devices such as personal robots, self-driving cars and drones to better generalize about different objects.
“Our stochastic neural networks try to mimic certain activities of the human brain and compute through a connection of neurons and synapses,” said Kaushik Roy, Purdue’s Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering. “This allows the computer brain to not only store information but also to generalize well about objects and then make inferences to perform better at distinguishing between objects.”
The stochastic switching behavior is representative of a sigmoid switching behavior of a neuron. Such magnetic tunnel junctions can be also used to store synaptic weights. Roy presented the technology during the annual German Physical Sciences Conference earlier this month in Germany. The work also appeared in the Frontiers in Neuroscience.
The switching dynamics of a nano-magnet are similar to the electrical dynamics of neurons. Magnetic tunnel junction devices show switching behavior, which is stochastic in nature. The Purdue group proposed a new stochastic training algorithm for synapses using spike timing dependent plasticity (STDP), termed Stochastic-STDP, which has been experimentally observed in the rat’s hippocampus. The inherent stochastic behavior of the magnet was used to switch the magnetization states stochastically based on the proposed algorithm for learning different object representations. “The big advantage with the magnet technology we have developed is that it is very energy-efficient,” said Roy, who leads Purdue’s Center for Brain-inspired Computing Enabling Autonomous Intelligence. “We have created a simpler network that represents the neurons and synapses while compressing the amount of memory and energy needed to perform functions similar to brain computations.”