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MRS Bulletin Materials News Podcast

A Science, Medicine and News podcast
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Materials News podcast by MRS Bulletin provides breakthrough news & interviews with researchers on the hot topics of 3D bioprinting, artificial intelligence and machine learning, bioelectronics, perovskites, quantum materials, robotics, and synthetic biology. Produced by the Materials Research Society.


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Recent Episodes

Episode 16: Thermoelectrics enables clothes with adjustable temperature
Sophia Chen of MRS Bulletin interviews Renkun Chen of the University of California, San Diego about his flexible thermoelectric devices that can provide personalized cooling and heating effects in clothing. Read the article in Science Advances.TranscriptTranscriptSOPHIA CHEN: If you’ve ever had to pay an air conditioning bill during the summer, you know how expensive it gets. Renkun Chen is a mechanical engineer at UCSD with an energy-saving idea: clothes with adjustable temperature. He and his team have designed and fabricated a material you can wear that directly cools the skin. RENKUN CHEN: Instead of having a centralized air conditioning system in a building, where you need to cool down a large volume of space for building occupants, we use our system to cool down a much smaller volume at a personal level. By doing so, we can save energy by at least an order of magnitude. SC: The power consumption per person of a conventional AC system is a few kilowatts, he says. Whereas personalized cooling, like a temperature-regulating outfit, uses tens of watts. Chen isn’t the first to invent clothes that directly cool your skin. For example, you can buy shirts right now that circulate icy water to cool you off. But his team’s design uses a thermoelectric material, which cools via a distinctive mechanism known as the Peltier effect, which creates cooling by passing an electric current between the junction of a semiconductor and metal. When you reverse the current, you create a heating effect. This can achieve much subtler temperature control than the wearables that are commercially available. Chen’s device can cool and heat. RC: It’s really like the thermostat in the air conditioning system. You can really set the skin temperature. SC: The highest performing thermoelectric materials are rigid, so Chen’s team needed to configure these materials to make a flexible, wearable device. They used two different commercially available thermoelectric materials. These materials consist of two bismuth telluride alloys: a p-type semiconductor alloyed with antimony, and an n-type semiconductor alloyed with selenium. Both alloys are connected to metal electrodes, and they create a cooling effect by making an electric current flow from the metal to the p-type material, or from the n-type material to the metal. Reversing the direction of the current causes heating. To make their system flexible, Chen and his team made these alloys into pillars and sandwiched them between two sheets of Ecoflex, a flexible silicone rubber. RC: Even though the pillars by themselves are rigid, the entire device is flexible because of the overall architecture. SC: They wanted the entire layer of each sheet of Ecoflex to keep at a uniform temperature. So to achieve this, they embedded aluminum nitride particles to increase its thermal conductivity. They also kept a 4 mm air gap between the two sheets for insulation. When the ambient temperature was between 22°C and 36°C, they could maintain the wearer’s skin temperature at 32°C, which they defined as a condition of thermal comfort. Chen wants to develop this into a therapeutic device for people who have medical conditions that make it difficult for them to regulate their skin temperature. RC: There are patients who are very sensitive or prone to overheating with certain health conditions like multiple sclerosis, or people who are genetically not able to sweat, they are prone to overheating. There are certain occupations, outdoor construction workers or fire fighters, and people who are doing outdoor activities, like athletes for example. For this kind of application, I think our device will also provide good thermal comfort solution.
Episode 15: 3D stretchable electronics achieves ultrahigh conductivity
Prachi Patel of MRS Bulletin interviews Benjamin C.-K. Tee of the National University of Singapore about an interfacial design for stretchable electronics that uses three-dimensional helical copper micro-interconnects embedded in an elastic rubber substrate. Read the article in APL Materials.TranscriptPRACHI PATEL: Metals are excellent at conducting electricity but not the best at being stretched or bent. For electronics that can be worn or wrapped around curved surfaces, stretchable conductors are key.  BENJAMIN TEE: One good example is a smart patch that you can wear to record your heartbeat, or your ECG and so on. PATEL: That’s Benjamin Tee at the National University of Singapore. He and his colleagues have come up with a new way to make stretchable conductors that stay strong and remain highly conductive when stretched to almost twice their length. Their strategy overcomes two main challenges of previous stretchable conductors. TEE: So one approach to make stretchable conductors is to use nanomaterials like carbon nanotubes, graphene. These are one way where people use these particles and coat it onto a stretchable substrate like silicone rubber or polyurethanes.PATEL: Another approach is to use metal thin films. Basically, researchers create wavy serpentine patterns of these films so they can stretch with the substrate. But in both approaches, stretching the materials tends to reduce their conductivity. Plus, the thin materials have lower electrical conductivities than bulk metal. So Tee and his colleagues took a different approach. TEE: We drew inspiration from actually spring-like structures. Spring-like structures are able to withstand strain. If you either stretch on a spring or compress a spring, they return, right? PATEL: They first made a spring using some off-the-shelf copper wire. Then they embedded it in silicone rubber to make it elastic. But that still wasn’t good enough. The spring started changing shape within the rubber after being stretched a few times.  TEE: And we found out that the reason was that the interface between metal and rubber needs to be well-matched. If you’re talking about metals you have modulus is extremely high in the gigapascals range whereas rubbers typically have a modulus of megapascal range. There’s a three orders of magnitude difference. So we need a way to make sure that these two interface do not slip. PATEL: And they did that by adding an epoxy to the rubber, which helps bond the metal to the rubber. This did the trick. TEE: We can stretch it over a 1000 times and these springs stay in the same shape as they were after stretching. What’s interesting is that this electrical conductivity does not change because we’re not changing the crystalline structure of the metal. Our approach basically extends the dimension into 3D as opposed to a planar patterned film. We’re exploiting the bulk property of the metal. The other advantage is it can actually stretch more because we’re going into three dimensions. So I think there is certainly a limit to how much we can scale this down if we want to keep the same good electrical properties that we’re talking about. But that being said when you scale them down to about a micron, they actually become softer and so you can have even greater stretchability. So I think a micron or so is sort of where we want to be if you want to capture bulk properties and still retain the stretchability.PATEL: The team’s findings are published in APL Materials. My name is Prachi Patel from the Materials Research Society.       
Episode 14: Thin film patterns classified by machine learning
Sophia Chen of MRS Bulletin interviews Alex Hexemer of Lawrence Berkeley National Laboratory in California, and Daniela Ushizima and Shuai Liu of the University of California, Berkeley about their design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. Read the article in MRS Communications. Transcript SOPHIA CHEN: Materials researchers come from around the world to study their samples in the beamline at the Advanced Light Source facility, located at Lawrence Berkeley National Laboratory in California. Alex Hexemer, a senior scientist at the facility, tells me that they’re currently upgrading the machine, so that it can take much more data, much more quickly. HEXEMER: The amount of data you’re going to create is so large that A, you can’t take it home on a hard drive anymore, nor can you start looking at the data anymore. It’s just too big. Some of the detectors here are going to run at thousands of frames a second. It becomes unmanageable from a human point, so we have to transition to more automated approaches.CHEN: So Hexemer and his collaborators decided to try a machine learning approach to quickly classify and process x-ray images. To develop their image classification algorithm, they worked with frequency-space pictures of thin films made of polymers, about 100 nm thick. Scientists image these thin films at the facility. They consist of intricate geometrical patterns on the nanometer scale, which researchers try to engineer to create specific materials properties. For example, Hexemer explains that one future application is a printable solar panel. In the future, people might be able to print photovoltaics made of thin film polymers. But first, they need to figure out what nanometer structures work the best.HEXEMER: To try to understand the efficiency of the material, we have to understand the morphology.CHEN: They came up with seven different categories of thin film patterns. One of Hexemer’s computer science collaborators, Dani Ushizima, explains that they had to show the computer millions of examples.DANIELA USHIZIMA: This neural network base will build a mathematical model that will represent the different patterns.CHEN: They found they could classify images successfully into the seven categories 94% of the time. USHIZIMA: The training process might take a long time—hours. But the feedback, to classify a scattering pattern, this is coming on the millisecond. CHEN: The images they classified were simulations of thin films rather than real data. HEXEMER: We want to have better and better simulations close to real and partially disordered systems. And that is very difficult. CHEN: The team brought together experts from materials science and computer science. Shuai Liu, a member of the team, says to expect more collaborations between the disciplines.SHUAI LIU: We point out a very important direction in future research is to combine machine learning, which has been well developed in recent years, with a lot of characterization techniques.CHEN: My name is Sophia Chen from the Materials Research Society. For more news, log onto the MRS Bulletin website at and follow us on twitter, @MRSBulletin. Thank you for listening.      
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Podcast Details
Jan 15th, 2019
Latest Episode
Aug 16th, 2019
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4 minutes

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