Experimental Dataset of Electrochemical Efficiency of a Direct Borohydride Fuel Cell (DBFC) with Pd/C, Pt/C and Pd Decorated Ni–Co/rGO Anode Catalysts

Abstract

<p>Dataset includes Direct Borohydride Fuel Cell (DBFC) impedance and polarization test in anode with Pd/C, Pt/C and Pd decorated Ni–Co/rGO catalysts. In fact, different concentration of Sodium Borohydride (SBH), applied voltages and various anode catalysts loading with explanation of experimental details of electrochemical analysis are considered in data. Voltage, power density and resistance of DBFC change as a function of weight percent of SBH (%), applied voltage and amount of anode catalyst loading that are evaluated by polarization and impedance curves with using appropriate equivalent circuit of fuel cell. Can be stated that interpretation of electrochemical behavior changes by the data of related cell is inevitable, which can be useful in simulation, power source investigation and depth analysis in DB fuel cell researches. </p> <p> </p>

Machine Learning-Based Current Density Simulation for Direct Borohydride Fuel Cell

“…We compare the performance of two popular machine learning algorithms, which are the artificial neural network (ANN) and the decision trees regression (DTR), to simulate the performance of DBFC. We employ a public dataset of single DBFC experiments [10] for training and test of the proposed models. This dataset contains results from tests on the Direct Borohydride Fuel Cell (DBFC) anode's impedance and polarization using catalysts with Pd/C, Pt/C, and Pd decorations.…”

“…They demonstrated that SVM was better than ANN in small datasets, whereas ANN performs better on bigger datasets. In this research, we used a public dataset on DBFC [10] that investigates the relationship between the various fuel cell characteristics to simulate the current density depending on four different parameters.…”