Researchers at the University of Texas at Arlington (UT Arlington) are working on a way to fully understand artificial ferroelectric materials that may be engineered to exhibit ‘smart’ mechanical behaviors.
Ferroelectric materials can potentially harbor spontaneous electric polarization, which, in turn, can be exploited to generate mechanical motion with the application of electric fields.
The team, a duo consisting of associate professors Ye Cao and Joseph Ngai, received an award of $597,856 granted by the National Science Foundation to develop their research where they hope to manipulate certain mechanical behaviors of artificial ferroelectric materials.
Ferroelectric materials are materials that demonstrate ferroelectricity which is the ability of a material to possess the capacity to exhibit spontaneous electric polarization. It is possible to reverse this polarization using the influence of an external electric field by applying it in the opposite direction.
This reversal technique is known as ‘switching’, and ferroelectric materials can sustain polarization after the electric field has been removed. The ability to switch polarization and potentially manipulate ferroelectric materials means they lend themself well to various applications, including thermistors, oscillators, capacitors, piezoelectric, displays, etc.
Cao and Ngai are aiming to generate a free energy landscape that is tunable with light, using thin films made of various materials of different thicknesses. Free energy can be arranged in specific ways.
The landscape the researchers hope to create is akin to a topographical map, with peaks and valleys; the material characteristics of the ferroelectric materials can be defined within these valleys.
One of the most unique and most practical functions of ferroelectric materials is that when the electric field is removed, the dipoles remain oriented in the direction they were: meaning ferroelectric materials have a ‘memory’.
To effectively adjust the mechanical behaviors of the ferroelectric materials, Cao and Ngai will use light to establish a reaction. Subsequently, they will apply computational modeli
Thus, by harnessing the power of machine learning in conjunction with phase-field modeling, the researchers can predict the natural properties of the materials. From this, a set of data points can be acquired, which can be ‘stitched together’ using machine learning.
Once connections between all of the data points have been established, Cao and Ngai can then use the computational models to generate 3D and multi-dimensional models that are otherwise unthinkable in a laboratory setting.
ng and machine learning techniques to generate various landscapes that can be used in many different applications.
Artificial ferroelectric materials demonstrate the potential to show high permittivity, which means they could be good for storing electricity. Thus, these materials can be suitable for manufacturing capacitors, and their fundamental reaction to electrical fields makes them particularly well-suited for use as switches or indicators.
Furthermore, ferroelectric materials possess a direct piezoelectric effect, making them suitable for accelerometers, microphones and headphones.
Ferroelectric materials are able to produce a small charge when responding to mechanical stress. This is useful when attempting to convert physical forces like sound or acceleration into electrical signals and vice versa.While the principles of ferroelectricity have been known for some time, the majority of the key research has only been conducted in the last decade. Therefore, artificial ferroelectric materials will only prove to be more beneficial as the research surrounding the underlying physics expands.
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