This paper presents a comparative study between two maximum power point tracking (MPPT) algorithms, the incremental conductance algorithm (InC) and the fuzzy logic controller (FLC). The two algorithms were applied to a low photovoltaic power conversion system, and they both use different PI controllers and grid synchronization techniques. Moreover, both InC and FLC methods have Clarke and Park Transformation. To some extent, the incremental conductance and fuzzy logic controller approaches are similar, but their control loops are different. Therefore, the InC has classic Proportional Integrative (PI) controllers with simple phase-locked loops (PLL). At the same time, the FLC works with fuzzy logic PI controllers linked with the Second Order Generalized Integrator (SOGI). The proposed techniques examine the solar energy conversion performance of the photovoltaic (PV) system under possible irradiance changes and constant temperature conditions. Finally, a performance comparison has been made between InC and FLC, which demonstrates the effectiveness of the fuzzy controller over the incremental conductance algorithm. FLC turns to convert photovoltaic power easily, decreasing fluctuations, and it offers a quick response to the variation of solar irradiance (shading effect). The simulation results show a superior performance of the controller with fuzzy logic, which helps the inverter convert over 99% of the power generated by the photovoltaic panels. In comparison, the incremental conductance algorithm converts around 80%.

Small donor–acceptor molecules combining arylamine donor blocks with various acceptors have been synthesized and evaluated as active materials in homojunction single material organic solar cells.

Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%.

Six new bio-inspired flavylium salts were synthesized and investigated by a combined computational and experimental study for dye-sensitized solar cell applications. The compounds were characterized by FT–IR, UV–Vis, NMR spectroscopy, and LC–MS spectrometry techniques. The pH-dependent photochromic properties of the flavylium dyes were investigated through a UV–Vis spectroscopy study and revealed that they follow the same network of chemical reactions as anthocyanins upon pH changes. The structural and electronic properties of the dyes were investigated using density functional theory (DFT) and time-dependent density functional theory (TD–DFT). Geometry optimization calculation revealed that all dyes, regardless of the specie, flavylium cations or quinoidal bases, present a planar geometry. The photovoltaic performances of the dyes, in both flavylium and quinoidal base forms, were evaluated by the HOMO and LUMO energies and by calculating the light-harvesting efficiencies, the free energy change of electron injection, and the free energy change regeneration. The MO analysis showed that all dyes can inject electrons into the conduction band of the TiO₂ upon excitation and that the redox couple can regenerate the oxidized dyes. The results obtained for the free energy change of electron injection suggest that the quinoidal bases should inject electrons into the semiconductor more efficiently than the flavylium cations. The values for the free energy change regeneration showed that the redox electrolyte can easily regenerate all dyes. Dipole moment analysis was also performed. DSSCs based on the dyes, in both flavylium and quinoidal base forms, were assembled, and their photovoltaic performances were evaluated by measuring the open-circuit voltage, the short circuit current density, the fill factor, and the energy conversion efficiency. Results obtained by both experimental and computational studies showed that the overall performances of the DSSCs with the quinoidal forms were better than those obtained with the flavylium cations dyes.