High-throughput ionic liquid electrospray sources based on dense monolithic arrays of emitters with integrated extractor grid and carbon nanotube flow control structures

2013 ◽  
Author(s):  
F. A. Hill ◽  
P. J. Ponce de Leon ◽  
L. F. Velasquez-Garcia
Keyword(s):  
Ionic Liquid ◽  
Carbon Nanotube ◽  
Flow Control ◽  
High Throughput ◽  
Control Structures

Functionalized carbon nanotube/ionic liquid-coated wire as a new fiber assembly for determination of methamphetamine and ephedrine by gas chromatography-mass spectrometry

2014 ◽  
Vol 6 (21) ◽  
pp. 8645-8653 ◽  
Author(s):  
Omid Narimani ◽  
Naser Dalali ◽  
Kobra Rostamizadeh
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Ionic Liquid ◽  
Carbon Nanotube ◽  
Steel Wire ◽  
Gas Chromatography Mass Spectrometry ◽  
Fiber Assembly ◽  
Coated Wire ◽  
Headspace Spme

Carbon nanotube–ionic liquid gel immobilized on a stainless steel wire as a novel fiber was successfully applied for headspace SPME of methamphetamine and ephedrine.

The electrolyte switchable solubility of multi-walled carbon nanotube/ionic liquid (MWCNT/IL) hybrids

2006 ◽  
pp. 2356 ◽  
Author(s):  
Bo Yu ◽  
Feng Zhou ◽  
Gang Liu ◽  
Yongmin Liang ◽  
Wilhelm T. S. Huck ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Carbon Nanotube ◽  
Multi Walled Carbon Nanotube

scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

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