Programmable Nanoparticle Ensembles via High-Throughput Directed Self-Assembly

Langmuir ◽  
2013 ◽  
Vol 29 (11) ◽  
pp. 3567-3574 ◽  
Author(s):  
Qiu Dai ◽  
Yingyu Chen ◽  
Chi-Chun Liu ◽  
Charles T. Rettner ◽  
Bryan Holmdahl ◽  
...  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  

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.


2019 ◽  
Vol 21 (13) ◽  
pp. 6810-6827 ◽  
Author(s):  
Dilek Yalcin ◽  
Calum J. Drummond ◽  
Tamar L. Greaves

High throughput methods were used to investigate ionic liquid containing solutions to provide systematic data of a broad compositional space. We have principally focused on the surface tension, apparent pH and liquid nanostructure to identify potential self-assembly and protein stabilizing ability of solvent systems.


2012 ◽  
Author(s):  
Oded Rabin ◽  
Robert M. Briber ◽  
Seung Yong Lee ◽  
Wonjoo Lee

Langmuir ◽  
2016 ◽  
Vol 32 (50) ◽  
pp. 13517-13524 ◽  
Author(s):  
Zhen Wang ◽  
Yuanyuan Cao ◽  
Xinyue Zhang ◽  
Dingguan Wang ◽  
Ming Liu ◽  
...  

2020 ◽  
Vol 152 ◽  
pp. 104294 ◽  
Author(s):  
Chenyi Li ◽  
Hongchao Geng ◽  
Xingqi Zhu ◽  
Chan Gao ◽  
Ning Jiang ◽  
...  

Soft Matter ◽  
2011 ◽  
Vol 7 (10) ◽  
pp. 5030 ◽  
Author(s):  
Igor Y. Perevyazko ◽  
Joseph T. Delaney ◽  
Antje Vollrath ◽  
Georges M. Pavlov ◽  
Stephanie Schubert ◽  
...  

2015 ◽  
Vol 3 (1) ◽  
pp. 240-249 ◽  
Author(s):  
Yuanhui Zheng ◽  
Lorenzo Rosa ◽  
Thibaut Thai ◽  
Soon Hock Ng ◽  
Daniel E. Gómez ◽  
...  

A simple, versatile, high-throughput nanofabrication method based on electrostatic self-assembly is developed for the large-scale generation of well-defined asymmetric plasmonic dimers, enabling the study of interparticle plasmon coupling and the "hot-spot" phenomenon in SERS.


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