Direct formic acid fuel cells (DFAFCs) have gained immense importance as a source of clean energy for portable electronic devices. It outperforms other fuel cells in several key operational and safety parameters. However, slow kinetics of the formic acid oxidation at the anode remains the main obstacle in achieving a high power output in DFAFCs. Noble metal‐based electrocatalysts are effective, but are expensive and prone to CO poisoning. Recently, a substantial volume of research work have been dedicated to develop inexpensive, high activity and long lasting electrocatalysts. Herein, recent advances in the development of anode electrocatalysts for DFAFCs are presented focusing on understanding the relationship between activity and structure. This review covers the literature related to the electrocatalysts based on noble metals, non‐noble metals, metal‐oxides, synthesis route, support material, and fuel cell performance. The future prospects and bottlenecks in the field are also discussed at the end.
Platinum-based catalysts have a long history of application in formic acid oxidation (FAO). The single metal Pt is active in FAO but expensive, scarce, and rapidly deactivates. Understanding the mechanism of FAO over Pt important for the rational design of catalysts. Pt nanomaterials rapidly deactivate because of the CO poisoning of Pt active sites via the dehydration pathway. Alloying with another transition metal improves the performance of Ptbased catalysts through bifunctional, ensemble, and steric effects. Supporting Pt catalysts on a high-surface-area support material is another technique to improve their overall catalytic activity. This review summarizes recent findings on the mechanism of FAO over Pt and Pt-based alloy catalysts. It also summarizes and analyzes binary and ternary Pt-based catalysts to understand their catalytic activity and structure relationship.
Polypyrrole (PPy) nanoparticles are reliable conducting polymers with many industrial applications. Nevertheless, owing to disadvantages in structure and morphology, producing PPy with high electrical conductivity is challenging. In this study, a chemical oxidative polymerization-assisted ultra-sonication method was used to synthesize PPy with high conductivity. The influence of critical sonication parameters such as time and power on the structure, morphology, and electrical properties was examined using response surface methodology. Various analyses such as SEM, FTIR, DSC, and TGA were performed on the PPy. An R2 value of 0.8699 from the regression analysis suggested a fine correlation between the observed and predicted values of PPy conductivity. Using response surface plots and contour line diagrams, the optimum sonication time and sonication power were found to be 17 min and 24 W, respectively, generating a maximum conductivity of 2.334 S/cm. Meanwhile, the model predicted 2.249 S/cm conductivity, indicating successful alignment with the experimental data and incurring marginal error. SEM results demonstrated that the morphology of the particles was almost spherical, whereas the FTIR spectra indicated the presence of certain functional groups in the PPy. The obtained PPy with high conductivity can be a promising conducting material with various applications, such as in supercapacitors, sensors, and other smart electronic devices.
Biodiesel production often results in the production of a significant amount of waste glycerol. Through various technological processes, waste glycerol can be sustainably utilized for the production of value-added products such as hydrogen. One such process used for waste glycerol conversion is the bioprocess, whereby thermophilic microorganisms are utilized. However, due to the complex mechanism of the bioprocess, it is uncertain how various input parameters are interrelated with biohydrogen production. In this study, a data-driven machine-learning approach is employed to model the prediction of biohydrogen from waste glycerol. Twelve configurations consisting of the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN) were investigated. The effect of using different combinations of activation functions such as hyperbolic tangent, identity, and sigmoid on the model’s performance was investigated. Moreover, the effect of two optimization algorithms, scaled conjugate gradient and gradient descent, on the model performance was also investigated. The performance analysis of the models revealed that the manner in which the activation functions are combined in the hidden and outer layers significantly influences the performance of various models. Similarly, the model performance was also influenced by the nature of the optimization algorithms. The MLPNN models displayed better predictive performance compared to the RBFNN models. The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. While the MLPNN configuration with the hyperbolic tangent as the hidden layer activation function and the sigmoid as the outer layer activation function yielded the best performance as indicated by an R2 of 0.978 and a RMSE of 9.91. The gradient descent optimization algorithm was observed to help improve the model’s performance. All the input variables significantly influence the predicted biohydrogen. However, waste glycerol has the most significant effects.
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