Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Mechanical Properties And Characterization Of Epoxy Composites Containing Highly Entangled As-Received And Acid Treated Carbon Nanotubes, Aaron Krieg, Julia A. King, Gregory M. Odegard, Timothy Leftwich, Leif K. Odegard, Paul D. Fraley, Ibrahim Miskioglu, Claire Jolowsky, Matthew Lundblad, Jin Gyu Park, Richard Liang
Mechanical Properties And Characterization Of Epoxy Composites Containing Highly Entangled As-Received And Acid Treated Carbon Nanotubes, Aaron Krieg, Julia A. King, Gregory M. Odegard, Timothy Leftwich, Leif K. Odegard, Paul D. Fraley, Ibrahim Miskioglu, Claire Jolowsky, Matthew Lundblad, Jin Gyu Park, Richard Liang
Michigan Tech Publications
Huntsman–Merrimack MIRALON® carbon nanotubes (CNTs) are a novel, highly entan-gled, commercially available, and scalable format of nanotubes. As-received and acid-treated CNTs were added to aerospace grade epoxy (CYCOM® 977-3), and the composites were characterized. The epoxy resin is expected to infiltrate the network of the CNTs and could improve mechanical properties. Epoxy composites were tested for flexural and viscoelastic properties and the as-re-ceived and acid treated CNTs were characterized using Field-Emission Scanning and Transmission Electron Microscopy, X-Ray Photoelectron Spectroscopy, and Thermogravimetric Analysis. Composites containing 0.4 wt% as-received CNTs showed an increase in flexural strength, from 136.9 MPa for neat epoxy …
Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network With Sensor Array Time Series Data, Kai Zhou, Yixin Liu
Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network With Sensor Array Time Series Data, Kai Zhou, Yixin Liu
Michigan Tech Publications
Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence-based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network …