Percolation threshold and conductivity of polymer electrolyte composites: Effect of dispersoid particle size

The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often \textit{ad hoc}, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. Our approach employs training an instance segmentation model, called StarDist [Schmidt et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham], which resolves the main challenge in the pixel-wise localization of nanoparticles in TEM images: the overlapping particles. The segmentation maps outperform models reported in the literature, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.

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Electrical percolation threshold of magnetostrictive inclusions in a piezoelectric matrix composite as a function of relative particle size

Computational Particle Mechanics ◽

10.1007/s40571-017-0165-4 ◽

2017 ◽

Vol 5 (2) ◽

pp. 227-238 ◽

Cited By ~ 1

Author(s):

Ever J. Barbero ◽

Antoine Joseph Bedard

Keyword(s):

Particle Size ◽

Percolation Threshold ◽

Matrix Composite ◽

Electrical Percolation ◽

Electrical Percolation Threshold

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The Effect of Catalyst Particle Size on Durability of a Polymer Electrolyte Membrane (PEM) Fuel Cell

ECS Meeting Abstracts ◽

10.1149/ma2011-01/6/275 ◽

2011 ◽

Keyword(s):

Particle Size ◽

Fuel Cell ◽

Polymer Electrolyte ◽

Polymer Electrolyte Membrane ◽

Catalyst Particle ◽

Pem Fuel Cell ◽

Electrolyte Membrane ◽

Catalyst Particle Size

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Characterizations of PMMA-based polymer electrolyte membranes with Al2O3

Journal of Polymer Engineering ◽

10.1515/polyeng-2019-0088 ◽

2019 ◽

Vol 39 (7) ◽

pp. 612-619 ◽

Cited By ~ 1

Author(s):

Cha Chee Sun ◽

Ah Heng You ◽

Lay Lian Teo

Keyword(s):

Particle Size ◽

Ionic Conductivity ◽

Polymer Electrolyte ◽

Polymer Electrolytes ◽

Polymer Electrolyte Membranes ◽

Average Particle ◽

Cole Plot ◽

Electrolyte Membranes ◽

Nano Size ◽

Host Polymer

Abstract Poly(methyl methacrylate) (PMMA)-based polymer electrolyte membranes are prepared through the solution cast method, with PMMA:ethylene carbonate (EC):LiCF3SO3:Al2O3 weight ratio of 55.13:18.34:24.5:2. The effect of Al2O3 filler grain sizes of 50 nm and 10 μm on the polymer electrolytes was studied in this work. From the Cole-Cole plot obtained through electrochemical impedance spectroscopy, the highest ionic conductivity for 50-nm Al2O3 in the PMMA-LiCF3SO3-EC-Al2O3 sample was measured as 1.52 × 10−4 S/cm at room temperature. The bonding formation among the host polymer and other additives in the polymer electrolytes has been studied using Fourier transform infrared spectroscopy. A strong occurrence of CH3 stretching mode has proven that nano size Al2O3 results in a much stronger bonding effect with the host polymer. The particle sizes were calculated by applying the Debye-Scherrer equation from the X-ray diffraction results. This work considers the effect of instrument broadening to further improve the accuracy of particle broadening for particle size calculation. The average particle size of nano size Al2O3 in the PMMA sample is calculated as 2.9693 nm. Moreover, a higher amorphousity level obtained from nano size filler polymer electrolyte of 98.5% computed from differential scanning calorimetry thermograms had also explained the achievement of high ionic conductivity in this work.

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