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.

In this work, bimetallic Pd_xCo_y nanoparticles supported on nitrogen-doped reduced graphene oxide catalysts were synthesized and tested for formic acid oxidation as potentially efficient and durable electrocatalysts. Graphene oxide was nitrogen doped through hydrothermal chemical reduction with urea as a nitrogen source. The Pd_xCo_y nanoparticles were deposited on the nitrogen-doped graphene oxide support using the impregnation-reduction method with sodium borohydride as a reducing agent and sodium citrate dihydrate as a stabilizing agent. The structural features, such as phases, composition, oxidation states, and particle sizes, of the nanoparticles were characterized using X-ray diffraction, transmission electron microscopy, scanning electron microscopy–energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. The Pd nanoparticle sizes in Pd₁Co₁/N-rGO, Pd/N-rGO, and Pd₁Co₁/CNT were 3.5, 12.51, and 4.62 nm, respectively. The electrochemical performance of the catalysts was determined by CO stripping, cyclic voltammetry, and chronoamperometry. Pd₁Co₁/N-rGO showed the highest mass activity of 4833.12 mA^–1 mg Pd, which was twice that of Pd₁Co₁/CNT. Moreover, Pd₁Co₁/N-rGO showed a steady-state current density of 700 mA^–1 mg Pd after 5000 s in chronoamperometry carried out at +0.35 V. Apart from the well-known bifunctional effect of Co, nitrogen-doped graphene contributed to the performance enhancement of the Pd₁Co₁/N-rGO catalyst.

Sugar industries generate substantial quantities of waste biomass after the extraction of sugar water from sugarcane stems, while biomass-derived porous carbon has currently received huge research attention for its sustainable application in energy storage systems. Hence, we have investigated waste sugarcane bagasse (WSB) as a cheap and potential source of porous carbon for supercapacitors. The electrochemical capacitive performance of WSB-derived carbon was further enhanced through hybridization with silicon dioxide (SiO₂) as a cost-effective pseudocapacitance material. Porous WSB-C/SiO₂ nanocomposites were prepared via the in situ pyrolysis of tetraethyl orthosilicate (TEOS)-modified WSB biomass. The morphological analysis confirms the pyrolytic growth of SiO₂ nanospheres on WSB-C. The electrochemical performance of WSB-C/SiO₂ nanocomposites was optimized by varying the SiO₂ content, using two different electrolytes. The capacitance of activated WSB-C was remarkably enhanced upon hybridization with SiO₂, while the nanocomposite electrode demonstrated superior specific capacitance in 6 M KOH electrolyte compared to neutral Na₂SO₄ electrolyte. A maximum specific capacitance of 362.3 F/g at 0.25 A/g was achieved for the WSB-C/SiO₂ 105 nanocomposite. The capacitance retention was slightly lower in nanocomposite electrodes (91.7–86.9%) than in pure WSB-C (97.4%) but still satisfactory. A symmetric WSB-C/SiO₂ 105//WSB-C/SiO₂ 105 supercapacitor was fabricated and achieved an energy density of 50.3 Wh kg⁻¹ at a power density of 250 W kg⁻¹, which is substantially higher than the WSB-C//WSB-C supercapacitor (22.1 Wh kg⁻¹).

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.

Aluminum–copper alloys are commonly used in the aerospace industry due to their low density and high strength. Pitting corrosion is the major problem of Al-Cu alloys due to the presence of largely separated electrochemical potential difference phases. Microstructure refinement and phase homogenization of the alloys are believed to be the factors that contribute to decreasing the galvanic coupling between phases, hence decreasing the pitting tendency. In this work, we investigate whether microstructure refinement is the only factor that contributes to pitting or whether some other factors are involved in the pitting tendency. The investigation was conducted on two frequently used aerospace aluminum–copper alloys, Al-2024 T3 and Al-2014 T6. The surface refinement was conducted by laser surface melting, and microstructure characterization was conducted by scanning electron microscopy with an energy-dispersive X-ray analysis. Phase identification before and after the laser surface melting was conducted by X-ray diffraction, while pitting tendency was measured by a polarization test in 1 molar sodium chloride solution. These experimental results revealed that the enrichment of copper in the α-matrix phase was the major contributing factor in pitting as compared to the largely believed microstructural phase refinement.