High-Throughput Directed Self-Assembly of Core–Shell Ferrimagnetic Nanoparticle Arrays

Langmuir ◽  
2013 ◽  
Vol 29 (24) ◽  
pp. 7472-7477 ◽  
Author(s):  
Qiu Dai ◽  
Jane Frommer ◽  
David Berman ◽  
Kumar Virwani ◽  
Blake Davis ◽  
...  
Keyword(s):  
High Throughput ◽  
Self Assembly ◽  
Core Shell ◽  
Nanoparticle Arrays

Rapid and Efficient Self-Assembly of Au@ZnO Core–Shell Nanoparticle Arrays with an Enhanced and Tunable Plasmonic Absorption for Photoelectrochemical Hydrogen Generation

2017 ◽  
Vol 9 (37) ◽  
pp. 31897-31906 ◽  
Author(s):  
Yiqiang Sun ◽  
Bo Xu ◽  
Qi Shen ◽  
Lifeng Hang ◽  
Dandan Men ◽  
...  
Keyword(s):  
Self Assembly ◽  
Hydrogen Generation ◽  
Core Shell ◽  
Nanoparticle Arrays

Self-assembly of Pd@Au core/shell nanosheets used as a highly sensitive SERS substrate based on the determination of trace fluorescent dye

2019 ◽  
Vol 110 (6) ◽  
pp. 563-569
Author(s):  
Tran Thi Bich Quyen ◽  
Huynh Thanh Hien ◽  
Truong Khai Hoan ◽  
Bui Le Anh Tuan ◽  
Nguyen Thi Tho
Keyword(s):  
Self Assembly ◽  
Fluorescent Dye ◽  
Core Shell ◽  
Sers Substrate ◽  
Highly Sensitive

Core/Shell Dual‐Responsive Nanocarriers via Iron‐Mineralized Electrostatic Self‐Assembly for Precise Pesticide Delivery

2021 ◽  
pp. 2102027
Author(s):  
Da‐xia Zhang ◽  
Jiang Du ◽  
Rui Wang ◽  
Jian Luo ◽  
Tong‐fang Jing ◽  
...  
Keyword(s):  
Self Assembly ◽  
Core Shell ◽  
Dual Responsive

scholarly journals Correction to Self-Assembly Growth of In-Rich InGaAs Core–Shell Structured Nanowires with Remarkable Near-Infrared Photoresponsivity

Nano Letters ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 2188-2188
Author(s):  
Chen Zhou ◽  
Xu-Tao Zhang ◽  
Kun Zheng ◽  
Ping-Ping Chen ◽  
Wei Lu ◽  
...  
Keyword(s):  
Self Assembly ◽  
Near Infrared ◽  
Core Shell

Magnetic-field-induced self-assembly of FeCo/CoFe2O4 core/shell nanoparticles with tunable collective magnetic properties

Nanoscale ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 4519-4529
Author(s):  
J. Mohapatra ◽  
J. Elkins ◽  
M. Xing ◽  
D. Guragain ◽  
Sanjay R. Mishra ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Magnetic Properties ◽  
Physical Properties ◽  
Self Assembly ◽  
Core Shell ◽  
Shell Nanoparticles ◽  
Novel Approach ◽  
Core Shell Nanoparticles

Self-assembly of nanoparticles into ordered patterns is a novel approach to build up new consolidated materials with desired collective physical properties.

Self-assembly of a surface bisphenol A-imprinted core–shell nanoring amino-functionalized superparamagnetic polymer

2013 ◽  
Vol 1 (38) ◽  
pp. 11648 ◽  
Author(s):  
Yong-Gang Zhao ◽  
Xiao-Hong Chen ◽  
Sheng-Dong Pan ◽  
Hao Zhu ◽  
Hao-Yu Shen ◽  
...  
Keyword(s):  
Bisphenol A ◽  
Self Assembly ◽  
Core Shell

Self-Assembly of Star Micelle into Vesicle in Solvents of Variable Quality: The Star Micelle Retains Its Core–Shell Nanostructure in the Vesicle

Langmuir ◽  
2015 ◽  
Vol 31 (8) ◽  
pp. 2262-2268 ◽  
Author(s):  
Nijuan Liu ◽  
Qun He ◽  
Weifeng Bu
Keyword(s):  
Self Assembly ◽  
Core Shell ◽  
Variable Quality

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.

Hybrid Core-Shell Nanoparticles by “Plug and Play” Self-Assembly

2018 ◽  
Vol 24 (67) ◽  
pp. 17672-17676 ◽  
Author(s):  
Benjamin Pacaud ◽  
Loïc Leclercq ◽  
Jean-François Dechézelles ◽  
Véronique Nardello-Rataj
Keyword(s):  
Self Assembly ◽  
Core Shell ◽  
Plug And Play ◽  
Shell Nanoparticles ◽  
Core Shell Nanoparticles
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