The effect of solvent and ionomer on agglomeration in fuel cell catalyst inks: Simulation by the Discrete Element Method

Influence of Cathode Catalyst Layer with SiO2-Coated Pt/Ketjen Black Catalysts on Performance for Polymer Electrolyte Fuel Cells

“…Based on these results and considerations, the next step will be to perform numerical analysis on the mass transfer phenomenon inside the cathode CLs using the 3D catalyst layer simulation developed in our research group in order to improve the cell performance. In order to understand the effect of nano and mesoscale structure of Pt/Carbon catalyst layer on cell performance and internal phenomena, various simulation models which included the effect of the structure of carbon aggregate, ionomer coverage, and formation of agglomerate have already been developed with some experimental knowledge, such as FIB-SEM observation, the actual pore size distribution of CLs, relative oxygen diffusion coefficient, agglomerate size distribution measurement in CL ink [20][21][22][23][24]. These simulation technologies are useful to the analysis of CLs with SiO 2 -coated Pt catalysts.…”

A Critical Assessment on Functional Attributes and Degradation Mechanism of Membrane Electrode Assembly Components in Direct Methanol Fuel Cells

“…A study reported that using ethanol solvent as catalyst ink for anode catalyst increased the interaction between Pt particles and ionomer, resulting in reduced agglomeration [180]. A similar study reported that using N-methyl pyrrolidone and dimethyl sulfoxide as catalyst ink enables the reduction of catalysts agglomeration [181]. Polyaniline-Silica (PANI-SiO 2 ) nanocomposite was created as a support for improving the performance of Pt/Ni electrocatalysts, to improve catalyst stability.…”

Longitudinal strand ordering leads to shear thinning in Nafion

“…23,24 Enormous progress has been made in understanding molecular and material properties of these inks and the Nafion membranes using atomistic and coarse-grained simulations. [25][26][27][28][29] Proton transfer across membranes, [30][31][32][33][34][35][36] intra-membrane water dynamics, 31,32,[36][37][38][39][40] ionomer assembly and aggregation, 36,[39][40][41][42][43][44][45][46][47] confinement effects, [48][49][50] polymer chain length effects, 51 solvent sorption and swelling 52,53 and tensile strength and nucleation 54 are just some of the important aspects of these simulations. More recently, efforts have been made to utilise the power of CG MD simulations and machine learning approaches in predicting the nano-structure of Nafion films at different hydration levels.…”