Show Notes
Season 2, Episode #4.
In this mini episode, Naomi Oppenheimer and Matan Yah Ben Zion talk about their path to becoming physicists and their latest work — a look at the diffusion of a substance made of repulsive particles.
Most often when high school students learn about diffusion, the assumption is that the particles feel no attractions to each other. When you place a drop of dye into a flask of water, the dye spreads. Microscopically, the particles of dye and water bounce off each other due to thermal motion but we assume they experience no significant attraction or repulsion to each other.
But what happens if the particles DO repel each other? It turns out that this often the case, in scenarios ranging from collections of proteins to groups of people. Naomi and Matan studied diffusion in this scenario, and they share their findings here and the implications for larger questions.
If you like this episode, stay turned for the full length interview in a few days!
Scientist bios:
Naomi is an assistant professor of Exact Sciences in the school for Physics and Astronomy at Tel Aviv University. She is interested in complex fluids, statistical mechanics, soft matter, and biology-inspired physical systems. She uses theoretical analytical tools, numerical simulations, and a dash of experiments. Some of her research achievements include predicting the effect of protein concentration on membrane viscosity and understanding why crumpled paper is shapeable, and her future directions include studying heterogeneous materials in biology and for next-generation functional materials.
Matan is an assistant professor of artificial intelligence at the Donders Center for Cognition at Radboud University in Nijmegen in the Netherlands. Matan’s research focuses on natural computation and collective behavior. He uses a combination of applied physics, statistical mechanics, artificial intelligence, and materials science to explain collective behavior in nature and to realize it in robotic swarms. Some of his research achievements include programmable self-assembly on the sub-cellular scale, developing a synthetic swarm or micro-swimmers on the cellular scale, and designing decentralized learning in robotic swarms.
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