Hidden in plain sight: unlocking the full potential of cyclic voltammetry with the thin-film rotating (ring) disk electrode studies for the investigation of oxygen reduction reaction electrocatalysts

Cyclic voltammetry conducted with a thin-film rotating disk electrode, or ring-disk electrode (CV-TF-R(R)DE) is a very popular ‘ex situ’ tool for the rapid screening of electrocatalysts for their activity in oxygen reduction reaction. Despite its popularity and broad use, in most instances only a small part of the information that could be accessed by CV-TF-R(R)DE is actually used by scientists in their research. This work outlines both innovative and more traditional (but half-forgotten) ways of using CV-TF-R(R)DE to its optimal or full potential in the ongoing quest to study the most relevant features of oxygen reduction reaction electrocatalysts and quickly identify the most promising candidates for their applications in fuel cells or other electrochemical devices.     

The effect of side walls on particles mixing in rotating drums

In this paper, we study the effects of the presence and shape of side walls and of the overall length of rotating cylindrical drums on the mixing of particles with differing sizes by application of the discrete element method (DEM). By varying the semi-axis of the spheroidally shaped side walls and the length of the overall drum, we observe the formation of circulation patterns near the side walls. Although there is a vast amount of literature studying mixing regimes in rotating drums, little is known about the effect of the side walls of the drum on particle mixing. The results of our study demonstrate that introducing curved side walls induces a strong circulation pattern near these side walls, but has, paradoxically, a negative impact on mixing and actually promotes segregation. The cause for this segregation is the difference in velocity of differently sized particles near the curved side walls. Large particles accumulate at the curved side walls, whereas small particles move away from the curved side walls. When the length of the drum is increased, the overall effect of the side walls is decreased, although it does remain observable, even in very large drums.     

A novel radial basis Bayesian regularization deep neural network for the Maxwell nanofluid applied on the Buongiorno model

The aim of this work is to provide the numerical solutions of the fluid model by using the stochastic computing paradigms. The linear/exponential stretching sheets on magneto-rotating flow based on the Maxwell nanofluid have been provided using the Buongiorno model with the impacts of uneven heat source/sink, varying thermal conductivity and reactive species. The solutions of this transformed ordinary differential exponential stretching sheet model have been presented using a novel ‘radial basis’ (RB) activation function together with the Bayesian regularization deep neural network (BRDNN), i.e., RB-BRDNN. The deep neural network is presented into two hidden layers, while thirteen and twenty-five numbers of neurons have been used in the first and second layer. A reference dataset is proposed using the Runge-Kutta scheme for the model. The correctness of the stochastic RB-BRDNN procedure is examined through the comparison of proposed and database results, whereas minimal absolute error values provide the accuracy of the scheme. The reliability and competence of the computing RB-BRDNN solver is authenticated using the state transitions, correlation, regression, and error histograms.