High-throughput nanostructured SERS substrates by self-assembly

2012 ◽  
Author(s):  
Oded Rabin ◽  
Robert M. Briber ◽  
Seung Yong Lee ◽  
Wonjoo Lee
Keyword(s):  
High Throughput ◽  
Self Assembly ◽  
Sers Substrates

Plasmonics tunable Ag-coated gold nanorods arrays as reusable SERS substrates for multiplexed antibiotics detection

2021 ◽  
Author(s):  
Xiaoya Peng ◽  
Dan Li ◽  
Yuanting Li ◽  
Haibo Xing ◽  
Wei Deng
Keyword(s):  
Gold Nanorods ◽  
Self Assembly ◽  
Aqueous Media ◽  
Antibiotic Residues ◽  
Sers Substrates ◽  
Antibiotics Detection ◽  
Robust Strategies

Antibiotic contaminants in aqueous media pose serious threat to human and ecological environments. Therefore, it is necessary to develop robust strategies to detect antibiotic residues. For this purpose, a self-assembly...

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.

High throughput approach to investigating ternary solvents of aqueous non-stoichiometric protic ionic liquids

2019 ◽  
Vol 21 (13) ◽  
pp. 6810-6827 ◽  
Author(s):  
Dilek Yalcin ◽  
Calum J. Drummond ◽  
Tamar L. Greaves
Keyword(s):  
Surface Tension ◽  
Ionic Liquid ◽  
Ionic Liquids ◽  
High Throughput ◽  
Self Assembly ◽  
Protic Ionic Liquids ◽  
Systematic Data ◽  
Solvent Systems ◽  
Compositional Space

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.

Self-assembly of various silver nanocrystals on PmPD/PAN nanofibers as a high-performance 3D SERS substrate

The Analyst ◽  
2015 ◽  
Vol 140 (16) ◽  
pp. 5707-5715 ◽  
Author(s):  
Peng Jia ◽  
Bing Cao ◽  
Jianqiang Wang ◽  
Jin Qu ◽  
Yuxuan Liu ◽  
...  
Keyword(s):  
Self Assembly ◽  
High Performance ◽  
Sers Substrate ◽  
Sers Substrates ◽  
Highly Sensitive ◽  
Nanofiber Mats ◽  
Pan Nanofiber

The AgNCs (AgNPs, AgNTs and AgNDs) decorated-PmPD/PAN nanofiber mats were obtained as highly sensitive 3D SERS substrates.

Polymer induced one-step interfacial self-assembly method for the fabrication of flexible, robust and free-standing SERS substrates for rapid on-site detection of pesticide residues

Nanoscale ◽  
2019 ◽  
Vol 11 (27) ◽  
pp. 12829-12836 ◽  
Author(s):  
Peng Wu ◽  
Lu-Bin Zhong ◽  
Qing Liu ◽  
Xi Zhou ◽  
Yu-Ming Zheng
Keyword(s):  
Pesticide Residue ◽  
Self Assembly ◽  
Pesticide Residues ◽  
Free Standing ◽  
Sers Substrates ◽  
Residue Detection ◽  
Assembly Method ◽  
One Step

A polymer induced one-step interfacial self-assembly method was developed to fabricate flexible, robust and free-standing SERS substrates for rapid pesticide residue detection.

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 ◽  
...  
Keyword(s):  
High Throughput ◽  
Self Assembly

Rapid Self-Assembly of Block Copolymers for Flower-Like Particles with High Throughput

Langmuir ◽  
2016 ◽  
Vol 32 (50) ◽  
pp. 13517-13524 ◽  
Author(s):  
Zhen Wang ◽  
Yuanyuan Cao ◽  
Xinyue Zhang ◽  
Dingguan Wang ◽  
Ming Liu ◽  
...  
Keyword(s):  
Block Copolymers ◽  
High Throughput ◽  
Self Assembly
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