TY - JOUR
T1 - Quantification of nanoparticle dispersion within polymer matrix using gap statistics
AU - Anane-Fenin, K.
AU - Akinlabi, E. T.
AU - Perry, N.
PY - 2019/4/5
Y1 - 2019/4/5
N2 - 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 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.
AB - 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 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.
KW - agglomeration
KW - dispersion
KW - gap statistics
KW - image segmentation
KW - nanoparticles
UR - http://www.scopus.com/inward/record.url?scp=85065833742&partnerID=8YFLogxK
U2 - 10.1088/2053-1591/ab1106
DO - 10.1088/2053-1591/ab1106
M3 - Article
AN - SCOPUS:85065833742
SN - 2158-5849
VL - 6
JO - Materials Research Express
JF - Materials Research Express
IS - 7
M1 - 075310
ER -