Manufacturing Bits: Jan. 25

Stretchable thermometers
The Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has developed a stretchable and self-powered thermometer that can be integrated into various systems, such as stretchable electronics and soft robots.

Depending on the materials used, the stretchable thermometer can measure temperatures of more than 200 degrees Celsius to -100 degrees Celsius. The stretchable thermometer has fast response times. And it is still accurate even if the unit is bent or undergoes deformation.

The industry is developing next-generation biocompatible medical devices, smart clothing and soft robotics. A sub-field of robotics, soft robotics involves the development of robots that are composed of physically flexible-bodies and electronics. In some cases, soft robots are designed to grab and manipulate delicate shaped objects. At times, they are used for internal deployment inside a human body.

Soft robotics as well as biocompatible medical devices and smart clothing may require temperature sensing units that can stretch. But many of the components used in traditional sensing are rigid.

To solve the problem, Harvard devised a stretchable thermometer, which consists of three parts–an electrolyte, an electrode, and a dielectric material. The dielectric separates the electrolyte and electrode.

Using these components, researchers developed four designs for the thermometer. In one test, they integrated it into a soft gripper and measured the temperature of a hot hard boiled egg. The flexible thermometer is more sensitive than traditional thermoelectric thermometers and can respond to changes in temperature within about 10 milliseconds, according to Harvard.

The thermometer operates like a charged temperature-sensitive capacitor. “The electrolyte/dielectric interface accumulates ions, and the dielectric/electrode interface accumulates electrons (in either excess or deficiency). The ions and electrons at the two interfaces are usually not charge-neutral, and this charge imbalance sets up an ionic cloud in the electrolyte. The design functions as a charged temperature-sensitive capacitor. When temperature changes, the ionic cloud changes thickness, and the electrode changes open-circuit voltage. We demonstrate high sensitivity (∼1 mV/K) and fast response (∼10 ms),” said Yecheng Wang, a postdoctoral fellow at SEAS in a paper published in the Proceedings of the National Academy of Sciences. Others contributed to the works.

Going from 2D to 3D
Separately, using machine learning, Harvard’s SEAS has found a way to transform 2D stretchable objects into specific 3D shapes.

The technology could be used in several science and engineering fields. It could pave the way towards the development of morphable surfaces for architecture, soft sensors, ergonomic garments, and medical devices, according to SEAS.

In the lab, SEAS used the technology in mechanotherapy for wound healing. To develop this technology, SEAS devised a 2D square sheet membrane, which is based on a elastomeric material.

Then, the membrane is mounted on an acrylic chamber. When pressurized, the 2D elastomeric membrane is transformed into a 3D dome-like shape. The height of the membrane depends on the stiffness of the material and the thickness of the sheet.

But identifying the exact shape of the final material is a non-trivial task. To develop the exact 3D shapes of these materials, researchers used a technology called inverse design.

Inverse design is a simple concept. Let’s say you want to develop products with select materials. In a computer, you input the desired materials and the properties that you want in a system. Then, using an algorithm, the system generates a predicted solution.

To enable inverse design, researchers from SEAS used machine learning. A subset of artificial intelligence (AI), machine learning uses advanced algorithms in systems to recognize patterns in data as well as to learn and make predictions about the information.

“Once the machine learning model was trained, we came up with an arbitrary 3D shape and passed it to the model,” said Antonio Elia Forte, a former postdoctoral fellow at SEAS. “The neural network then outputs the membrane design and the pressure at which we should inflate such membrane to obtain the desired 3D shape.

“This platform has potential to quickly and effectively design patient-specific devices for mechanotherapy and beyond. Before this research, we didn’t know how to use machine learning to unravel nonlinear mappings in inflatable systems but it turns out that they are very powerful for these purposes,” said Forte. “Machine learning could push the boundaries of currently known design strategies and allow us to design and build fully reconfigurable shape-morphing material.”

The research is published in Advanced Functional Materials.