Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
In-Situ Shear Exfoliation Of Graphene From Graphite Polymer Nanocomposites For Lung And Heart Motion, Md Ashiqur Rahman, Md Abdur Rahman Bin Abdus Salam, Ali Ashraf
In-Situ Shear Exfoliation Of Graphene From Graphite Polymer Nanocomposites For Lung And Heart Motion, Md Ashiqur Rahman, Md Abdur Rahman Bin Abdus Salam, Ali Ashraf
Mechanical Engineering Faculty Publications and Presentations
Graphene-based nanocomposites have become attractive for different applications such as energy storage, sensors, biomolecule detection, biomedical, healthcare, and wearable devices due to their unique mechanical, electrical, and thermal properties. However, using commercial graphene for making nanocomposite devices can be expensive, and fabricating nanocomposites can be challenging due to impurities while transferring graphene to elastomer composites. In this study, we used a simple, inexpensive in-situ shear exfoliation method to produce graphene from graphite directly within the elastomer. As the graphene in the elastomer reached beyond its percolation or threshold, electrons hop or tunnel around from one graphene flake to another. So, …
Identifying Sick Cells From High-Resolution Solid-State Micropore Data, Abdul Hafeez, Azhar Ilyas, Ali R. Butt, Samir M. Iqbal
Identifying Sick Cells From High-Resolution Solid-State Micropore Data, Abdul Hafeez, Azhar Ilyas, Ali R. Butt, Samir M. Iqbal
Mechanical Engineering Faculty Publications and Presentations
Early detection of diseases such as cancer can drastically improve prognosis and treatment. To this end, solid-state micropores can measure distinct mechanical properties of diseased cells from their translocation behavior — detected as pulses in the temporal data stream of ionic current — and help diagnose diseases at early stages. However, the obstacle in such approaches is that the accuracy of the sensor is affected by noise, making the pulse detection task too subjective. This is inefficient especially when the disease-relevant data is only a fraction of the total acquired data. Thus, it is important to intelligently automate the detection …