Pressure sensors are used in electronic devices across all industries and making them as accurate as possible means making them as thin as possible. Researchers from Khalifa University have developed a method to use a novel 2D material for highly-sensitive and tunable flexible pressure sensors. 

Compared with conventional rigid silicon-based electronics, thin, flexible electronics can withstand various deformations such as tension, compression, bending and twisting. Pressure sensors that can transform external pressure into electrical signals are an indispensable application of flexible electronics, particularly for biomedical applications.

A team of researchers from Khalifa University has investigated how to develop a pressure sensor using a novel 2-Dimensional (2D) material, which is a single sheet of material that is just one atom thick, and 3D printing. They published their results in The research team includes Jing Fu, Research Associate, Somayya Taher, PhD candidate, Prof. Rashid Abu Al-Rub, Director of the Advanced Digital and Additive Manufacturing Group and Professor of Mechanical Engineering, Prof. TJ Zhang, Professor of Mechanical Engineering, Prof. Vincent Chan, Professor of Biomedical Engineering, and Prof. Kin Liao, Professor of Aerospace Engineering.

“Pressure sensors can be divided into various categories, including piezoelectric pressure sensors and piezoresistive pressure sensors,” Dr. Kin explained. “The working principle of a piezoresistive pressure sensor capitalizes on the change in the electrical resistance of the sensor against applied pressure. Such sensors have a simple structure, high sensitivity, fast-frequency response and low-energy consumption, making them popular candidates for various applications.”

An effective pressure sensor needs to be sufficiently thin. A sensor that is too thick may give erroneous readings as the sensor would press into a soft material, decreasing the load between the objects and increasing the measured pressure. To be as accurate as possible, researchers have turned to 2D materials to achieve sensors that are thin as possible.

“The engineering performance and robustness of a piezoresistive sensor mainly hinge on the sensor’s embedded active material,” Dr. Kin explained. “So far, different kinds of conductive materials have been used, such as metal nanoparticles, conductive polymers, graphene, and transition metal compounds. More recently, 2D materials have captured researchers’ attention worldwide, particularly transition metal carbides and nitrides or MXenes.”

MXenes are a family of 2D materials comprised of a pretransition metal, such as titanium (Ti), zirconium (Zr) or hafnium (Hf), with carbon and/or nitrogen, and hydroxyl, oxygen or fluorine surface functional group. These combinations give MXenes excellent electrical conductivity and hydrophilicity, making them promising candidates for applications such as piezoresistive sensors. 

As a 2D material, MXenes can be used as sheets and stacked on top of each other via van der Waals forces or hydrogen bonding between the functional groups. This way, MXenes can be formed into flexible and stable films, although the resulting material shows a very weak piezoresistive effect because when compressed, the structure of the sheet doesn’t allow for much deformation. Using MXenes in a 3D structure with similar length scales in all three dimensions would overcome this issue and make best use of the novel MXene material.

The Khalifa University team used additive manufacturing to develop the 3D structures. Traditional methods use templates upon which MXene layers are deposited before the templates are removed. While this does work, it does not allow for precise control of the internal structure of the resulting 3D scaffold. 3D printing overcomes this, with the technology able to fabricate flexible pressure-sensitive sensors with a high dynamic range through an easy to manipulate and large-scale manufacturing method.

“There are enormous possibilities in the design of internal structures that could be produced by 3D printing, but the triply periodic minimal surface (TPMS) structure is one of the more interesting,” Dr. Kin said. “The TPMS structure is known for possessing characteristics of surface area, mechanical robustness and thermal conductivity with an edge-free structure. Fabrication of 2D MXenes into the periodic, porous TPMS structure will lead to the development of novel 3D scaffolds with excellent electrical conductivity and mechanical properties.”

The team developed a simple and efficient method to combine MXene with a uniquely designed TPMS gyroid structure to create a 3D MXene-based gyroidal structure for use as a piezoresistive sensor with extremely high sensitivity, good response time and improvable durability. This method can be used to fabricate 3D MXene structures with any size, shape and internal structure.

More recently, Prof. Liao’s group has been working on constructing 3D structures of heterogenous 2D materials – different types of 2D materials organized in layered manner – for applications such as sensors, electromagnetic interference shielding, as well as energy-related applications.

Jade Sterling
Science Writer
25 May 2022

The post Advances in Flexible Pressure Sensors Using 3D Printing and 2D Materials appeared first on Khalifa University.

Identifying Parkinson’s Disease early is crucial for slowing the disease progression and a new tool developed by Khalifa University can now detect the disease using sensors on the average smartphone.

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Parkinson’s Disease is the second most common neurodegenerative disorder, affecting more than one percent of the population above 60 years old. Often beginning as a barely noticeable hand tremor, over time, the disease interferes with movement, muscle control, and balance. Fine motor impairment (FMI) is progressively expressed in early Parkinson’s Disease patients but clinical techniques for detecting it may not be robust enough.

A team of researchers at KU including Dr. Leontios Hadjileontiadis, Professor of Biomedical Engineering and member of KU’s Healthcare Engineering Innovation Center (HEIC), has developed a tool that can screen for early motor Parkinson’s symptoms and alert individuals accordingly via their smartphones.

In collaboration with researchers from Greece, Germany and the United Kingdom, Dr. Hadjileontiadis introduced a deep learning framework that analyzes data captured passively and discretely during normal smartphone use and .

“Remote unsupervised screening via mobile devices can raise awareness for medical care, with daily data assisting diagnosis,” explained Dr. Hadjileontiadis. “User interaction with smartphones can unveil dense and multi-modal data to reveal patterns that can be connected with both motor and cognitive function. In particular, Hold Time, the time interval between the press and release of a key, offers insights to the probability of a subject suffering from Parkinson’s.”

The rate at which a person presses down and then releases a finger on a key indicates how quickly the brain can control the muscles. When the body needs to start moving, the brain’s motor cortex sends signals to the spinal neurons to activate the muscles. Dopamine is one of the neurotransmitters involved that ignites a chain of events resulting in a movement, a feeling or an action. For Parkinson’s Disease patients, dopamine-producing cells in the brain become inactive and the loss of dopamine leads to issues with movement. Symptoms of the disease become increasingly more apparent and the patient develops tremors, difficulty walking, and other issues with movement.

“Detecting these smaller tremors at the start of the disease can lead to earlier diagnosis and allow us to implement management strategies earlier,” explained Dr. Hadjileontiadis. “The standard medical practice in diagnosing Parkinson’s Disease requires years of expertise. Using a smartphone provides an unobtrusive way of capturing data as we link keystroke typing with an enriched feature vector to describe the keystroke variables.”

Additionally, acceleration values from the smartphone’s Inertial Measurement Unit (IMU) sensor are used to monitor for hand tremors. This also is a source of data captured passively and unobtrusively as users perform common actions with their phone, from placing calls to typing messages.

When combined with deep learning, these data could provide a novel tool for effectively remotely screening the subtle fine motor impairments indicative of early onset of Parkinson’s Disease. Deep learning has been previously shown to be highly effective in extracting useful representations from high dimensional information like images, and the research team showed that deep learning can be leveraged to quantify touchscreen typing based information that is strongly correlated with FMI clinical scores.

In screening for Parkinson’s, deep learning algorithms can detect the disease from MRI scans, tremors recorded on accelerometers and voice degradation from voice signals. Now, typing on a smartphone can monitor keystroke dynamics in everyday activities.

“We tried to detect Parkinson’s Disease using a multi-symptom approach that merges passively-captured data from two different smartphone sensors via a novel deep learning framework,” explained Dr. Hadjileontiadis. “Our method is inspired by the typical workflow of a neurologist, in the sense that it outputs a score for tremor and FMI, two of the most common motor symptoms, as well as a score for Parkinson’s Disease.”

Automated Parkinson’s Disease detection is not a new idea. Many sensors have been tested to capture specific aspects of different symptoms, such as IMU sensors for gait alterations, microphones for speech impairment, keyboards for rigidity, and writing equipment for fine motor impairment. The common denominator in these studies is that they attempt to infer Parkinson’s Disease from single symptom cues. This is inherently problematic as Parkinson’s manifests differently in different subjects, meaning any system that can reliably detect the disease needs to cover multiple symptoms. The research from Dr. Hadjileontiadis is multi-modal in this way, capturing data unobtrusively and ‘in-the-wild.’

Using deep learning techniques, the team achieved 92.8 percent sensitivity and 86.2 percent specificity for Parkinson’s Disease detection. Not only is their proposed framework performing well, but it can also be extended to include additional data in the same architecture, including speech information, for example.

“Performance-wise, our approach produced good classification results and this is the first work to address the problem of detecting Parkinson’s from multi-modal data,” said Dr. Hadjileontiadis. “This is a solid first step towards a high-performing remote Parkinson’s Disease detection system that can be used to discreetly monitor subjects and urge them to visit a doctor signs of the disease are detected.”

Jade Sterling
Science Writer
7 February 2021

The post Detecting Parkinson’s Disease using Deep Learning Techniques from Smart Phone Data appeared first on Khalifa University.