Quantification of nanoparticle dispersion within polymer matrix using gap statistics

Abstract

This study was prompted by the inadequacy of most dispersion quantification techniques to address issues pertaining to scalability, implementation complexity, accuracy/error, uncertainty factors and versatility. Therefore, a method for quantifying dispersion based on gap statistics was developed. A dispersion quantity (D) was formulated from a Gap factor () G , 0 Particle spacing dispersity (PSD 1) and Particle size dispersity (PSD 2) factors. The summation of the factors resulted in the dispersion parameter (D p) which must be equal to one for an ideal or uniformly distributed condition. The state of dispersion increases as D→100%. The concept was tested with simulated models having uniform dispersion, random dispersion, small aggregate, three large aggregate and one large aggregate were successfully quantified to show 99.34%, 82.42%, 34.17%, 8.95% and 3.65% respectively. For validation of concept, the state of dispersion when samples with (scenario 1) and without (scenario 4) silane treatment were quantified as 32,02% and 7.72% respectively. The concepts were then validated using real microscopy images. This approach is robust, versatile and easy to implement.

Rapid Escherichia coli Trapping and Retrieval from Bodily Fluids via a Three-Dimensional Bead-Stacked Nanodevice

“…An overlay of the 3D mask (in pink) and the raw data are found to be dependent on the flow rate ranging from 20 to 50 μL/min. Inspired by the machine learning-based segmentation task in 2D-cell microscopy image 37 and 2Dmaterial visualization, 38 we generated a 3D mask using a novel segmentation method, which was a combination of an unsupervised machine learning method, K-mean clustering, 39 and morphological operation. 40 A typical example in a crosssectional plane is depicted in Figure 3b.…”

Enhancing Electromagnetic Interference Shielding Effectiveness of Polymer Nanocomposites by Modifying Subsurface Carbon Nanotube Distribution

“…The currently reported descriptors are typically based on the statistical analysis of particle spacing [ 24–27 ] and particle size, [ 25,26,28,29 ] particle concentrations, [ 30,31 ] and clusters estimations. [ 26,32 ] In particular, the statistical distribution of particle spacing and size methodology reported in a previous study [ 25 ] has proposed the dispersion ( D 0.2 ) and agglomeration ( A 0.3 ) descriptors, which have been successfully proved for the case of CNT distributions. Both descriptors D 0.2 and A 0.3 are obtained by first converting an SEM or OM image into a binary CNT image (CNT regions in black and background in white).…”

Reinforced electrospun nanofiber composites for drug delivery applications

“…Uniform distribution of the discontinuous phase is essential for the performance of the composite material. Various techniques like SEM, TEM, Raman spectroscopy, UV–visible or fluorescence spectroscopy, and electrical conductivity measurements are used to analyze the dispersion behavior of the reinforcement material in the composite 37 …”