Graphene "Tattoos" for Plants Could Form Neural Networks
Researchers at the University of Texas at Austin developed a graphene 'tattoo' that sticks directly onto a plant leaf to provide real-time moisture readings. Beyond sensing hydration, the team believes the patches could one day act as artificial synapses and be linked into a neural network that computes on the plants themselves. The work, led by associate professor Jean Anne Incorvia and colleagues, was published in Nano Letters in February.
Key Takeaways
- A hydrated leaf is a healthy leaf.
That's true for the leaves of crop plants in a farmer's field and for the leaves of trees in an area vulnerable to forest fires.
- A forest of the future, Incorvia and colleagues think, might hold a whole grove of sensors networked to gauge the risk of fire or drought in real time.
A Graphene Leaf "Tattoo" as a Moisture Sensor The sensor is a graphene patch that can be pasted onto the leaf of a plant (the researchers used Monstera ) like a stick-on tattoo.
- It can stretch and squeeze as the leaf grows, shrinks, or twists.
This isn't the first graphene sensor of its kind, but real-time hydration sensors aren't common in the field.
- Researchers study a Monstera plant in the lab, with sensors pasted on each leaf.
They've typically crafted transistors with graphene and Nafion , a polymer that's a good proton conductor.
- At first, they weren't sure how to use it.
A hydrated leaf is a healthy leaf. That's true for the leaves of crop plants in a farmer's field and for the leaves of trees in an area vulnerable to forest fires. But the traditional techniques to monitor leaf hydration require cutting them from their plants, which is time-consuming and cannot give live measurements.
That's why many researchers are building sensors that measure a plant's health in real time. Now, researchers in Texas have developed a graphene "tattoo" that can be stuck directly onto a leaf to provide real-time moisture readings. The researchers also believe it could one day be the building block for a new kind of plant monitoring, by turning the patches into a neural network that computes on the plants themselves.
"Not only are we just sensing the moisture level, but we can have that sensor act as this artificial synapse, which then we can put into a neural network," says Jean Anne Incorvia , an associate professor of electrical and computer engineering at the University of Texas at Austin. Incorvia and colleagues (including her graduate student Utkarsh Misra) published their work in Nano Letters in February. A forest of the future, Incorvia and colleagues think, might hold a whole grove of sensors networked to gauge the risk of fire or drought in real time.
For more details please read the original article at IEEE Spectrum AI.
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