scholarly journals Optimization of the high-throughput synthesis of multiblock copolymer nanoparticles in aqueous media via polymerization-induced self-assembly

2018 ◽  
Vol 3 (5) ◽  
pp. 645-657 ◽  
Author(s):  
Amy A. Cockram ◽  
Robert D. Bradley ◽  
Sylvie A. Lynch ◽  
Patricia C. D. Fleming ◽  
Neal S. J. Williams ◽  
...  
Keyword(s):  
High Throughput ◽  
Self Assembly ◽  
Aqueous Media ◽  
Multiblock Copolymer

High-throughput synthesis of multiblock copolymer nanoparticles via PISA.

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 Luminescent Supramolecular Nano- or Microstructures Formed in Aqueous Media by Amphiphile-Noble Metal Complexes

2020 ◽  
Vol 2020 ◽  
pp. 1-24 ◽  
Author(s):  
Carmen Cretu ◽  
Loredana Maiuolo ◽  
Domenico Lombardo ◽  
Elisabeta I. Szerb ◽  
Pietro Calandra
Keyword(s):  
Noble Metal ◽  
Smart Materials ◽  
Self Assembly ◽  
Noble Metals ◽  
Photophysical Properties ◽  
Aqueous Media ◽  
Molecular Architecture ◽  
Stimuli Responsive ◽  
Panoramic View ◽  
Amphiphilic Molecules

The involvement of metal ions within the self-assembly spontaneously occurring in surfactant-based systems gives additional and interesting features. The electronic states of the metal, together with the bonds that can be established with the organic amphiphilic counterpart, are the factors triggering new photophysical properties. Moreover, the availability of stimuli-responsive supramolecular amphiphile assemblies, able to disassemble in a back-process, provides reversible switching particularly useful in novel approaches and applications giving rise to truly smart materials. In particular, small amphiphiles with an inner distribution, within their molecular architecture, of various polar and apolar functional groups, can give a wide variety of interactions and therefore enriched self-assemblies. If it is joined with the opportune presence and localization of noble metals, whose chemical and photophysical properties are undiscussed, then very interesting materials can be obtained. In this minireview, the basic concepts on self-assembly of small amphiphilic molecules with noble metals are shown with particular reference to the photophysical properties aiming at furnishing to the reader a panoramic view of these exciting problematics. In this respect, the following will be shown: (i) the principles of self-assembly of amphiphiles that involve noble metals, (ii) examples of amphiphiles and amphiphile-noble metal systems as representatives of systems with enhanced photophysical properties, and (iii) final comments and perspectives with some examples of modern applications.

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.

scholarly journals Light-driven self-assembly of cyanostilbene derivative with reversible chirality in aqueous media

2022 ◽  
Author(s):  
Xiaoxuan Zeng ◽  
Yue Wu ◽  
Lin Zou ◽  
Xingwang Liu ◽  
Xin Qi ◽  
...  
Keyword(s):  
Self Assembly ◽  
Aqueous Media

Self-assembly of new surface active ionic liquids based on Aerosol-OT in aqueous media

2014 ◽  
Vol 428 ◽  
pp. 267-275 ◽  
Author(s):  
K. Srinivasa Rao ◽  
Praveen Singh Gehlot ◽  
Tushar J. Trivedi ◽  
Arvind Kumar
Keyword(s):  
Ionic Liquids ◽  
Self Assembly ◽  
Aqueous Media ◽  
Aerosol Ot ◽  
Surface Active

Hexaphenylbenzene-based fluorescent aggregates for detection of zinc and pyrophosphate ions in aqueous media: tunable self-assembly behaviour and construction of a logic device

2017 ◽  
Vol 41 (12) ◽  
pp. 4806-4813 ◽  
Author(s):  
Subhamay Pramanik ◽  
Vandana Bhalla ◽  
Manoj Kumar
Keyword(s):  
Self Assembly ◽  
Aqueous Media ◽  
Logic Device

The aggregates of HPB derivative 7 exhibited “on–on” response towards Zn2+ ions and this in situ prepared 7-Zn2+ ensemble was utilized as a “not quenched” probe for detection of PPi ions in aqueous media.

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