Incorporation of inorganic nanoparticles into an organic polymer matrix for data storage application

2017 ◽  
Vol 17 (5) ◽  
pp. 756-762 ◽  
Author(s):  
Ramneek Kaur ◽  
Janpreet Singh ◽  
S.K. Tripathi
Keyword(s):  
Data Storage ◽  
Polymer Matrix ◽  
Inorganic Nanoparticles ◽  
Organic Polymer

scholarly journals Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning

2021 ◽  
Author(s):  
Eric Qu ◽  
Andrew Jimenez ◽  
Sanat Kumar ◽  
Kai Zhang
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Form Factor ◽  
Polymer Matrix ◽  
Structural Information ◽  
Real Space ◽  
Inorganic Nanoparticles ◽  
Organic Polymer ◽  
Main Challenge ◽  
Particle Form

<p>There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub.</p>

scholarly journals Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning

2021 ◽  
Author(s):  
Eric Qu ◽  
Andrew Jimenez ◽  
Sanat Kumar ◽  
Kai Zhang
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Form Factor ◽  
Polymer Matrix ◽  
Structural Information ◽  
Real Space ◽  
Inorganic Nanoparticles ◽  
Organic Polymer ◽  
Main Challenge ◽  
Particle Form

<p>There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub.</p>

Morphological, chemical and structural characterization of silica-containing polyvinylpyrrolidone electrospun nanofibers prepared by sol-gel technique

2016 ◽  
Vol 79 (1) ◽  
pp. 5-12 ◽  
Author(s):  
T. Tański ◽  
W. Matysiak ◽  
M. Zaborowska ◽  
D. Łukowiec ◽  
M. Krzesiński
Keyword(s):  
Silicon Dioxide ◽  
Polymer Matrix ◽  
Sol Gel ◽  
Tetraethyl Orthosilicate ◽  
Organic Polymer ◽  
Metal Alkoxide ◽  
Structural Examination ◽  
Polymer Hydrolysis ◽  
Uniform Dispersion ◽  
Gel Technique

Purpose: The aim of this study was to produce poly(vinylpyrrolidone) (PVP) containingsilica nanofibers using electrospinning method from 10% PVP/EtOH solutions with differentmass concentration 0 and 30% of tetraethoxysilane. Sol-gel technique was used to obtainnanofiber membranes with high amount of inorganic phase. In the case when metal alkoxide,such as tetraethyl orthosilicate (TEOS) is mixed with an organic polymer, hydrolysis andcondensation reaction of TEOS occur in-situ with polymer matrix, which allows to fabricateorganic-inorganic hybrid structures with uniform dispersion.Design/methodology/approach: The examination of the morphology of the obtainedPVP/silicon dioxide nanofibers using scanning electron microscope (SEM) has been made.The chemical structure of produced nanostructures was investigated by Fourier - TransformInfrared spectroscopy (FTIR) and Energy Dispersive Spectrometry (EDX) to analyze theregular dispersion by examining types of bonds occurring between polymer matrix and SiO2phase.Findings: Results obtained in this paper shows that the mass concentration of thereinforcing phase in form of TEOS have an influence on the average diameter of nanofibersand with the increase of tetraethyl orthosilicate in solution nanofibers diameters decrease.Moreover, structural examination shows uniform dispersion of the reinforcing phase in hybridmaterials.Research limitations/implications: Uniform dispersion of the reinforcing phase insilica-containing PVP nanofibers gives the opportunity to make nanowires in calcinationprocess from such obtained fibrous mats and use in novel electrical devices.Originality/value: This paper describes an easy and more effective way of makingpolymer nanofibers with the content of silicon dioxide with the perspective way of makingsilica nanowires in the future from obtained hybrid nanofibers, so that this method canreplace commonly used nanowires growth processes.

Functionalized coatings based on organic polymer matrix against the process of corrosion of mild steel in neutral medium

2018 ◽  
Vol 119 ◽  
pp. 221-229 ◽  
Author(s):  
Victoria Bustos-Terrones ◽  
Iris N. Serratos ◽  
Norma Castañeda-Villa ◽  
Jonathan Osiris Vicente Escobar ◽  
Mario Alberto Romero Romo ◽  
...  
Keyword(s):  
Mild Steel ◽  
Polymer Matrix ◽  
Organic Polymer ◽  
Neutral Medium ◽  
Functionalized Coatings

Facile preparation of ductile, free-standing and multilayer polymeric optical data storage media with macroscopic structural homogeneity

2018 ◽  
Vol 6 (23) ◽  
pp. 6118-6124 ◽  
Author(s):  
Yang Feng ◽  
Jingfa Yang ◽  
Jiang Zhao ◽  
Guangming Chen
Keyword(s):  
Physical Properties ◽  
Data Storage ◽  
Polymer Matrix ◽  
Optical Data Storage ◽  
Optical Data ◽  
Data Recording ◽  
Free Standing ◽  
Structural Homogeneity ◽  
Storage Media ◽  
Facile Preparation

Macroscopic structural homogeneity can help to fully exploit the physical properties of a polymer matrix, making it easier to acquire optical data recording materials featuring specific properties.

Application of Mathematical Principles in Calculating Method of the Conductivity

2013 ◽  
Vol 448-453 ◽  
pp. 2057-2060
Author(s):  
Guan Min Li
Keyword(s):  
Mechanical Properties ◽  
Polymer Electrolyte ◽  
Common Type ◽  
Inorganic Nanoparticles ◽  
Organic Polymer ◽  
Ion Transfer ◽  
Composite Polymer ◽  
Calculating Method ◽  
Different Types ◽  
Strong Ability

Polymer electrolyte organic polymer as the main matrix, poor mechanical properties, in addition, the ion transfer is mainly in the amorphous regions completed and the polymer electrolyte composite polymer generally has a strong ability to crystallize, thus greatly suppressed ion transmission. People try by the incorporation of different types and contents of the filler to improve the mechanical properties of the polymer electrolyte, lower crystallinity and increase the ionic conductivity. Various inorganic nanoparticles are the most common type of doping filler.

Sign in / Sign up

Export Citation Format

Share Document