PbS thin films with thickness between 100 and 150 nm were grown for the first time by microwave-assisted chemical bath deposition in a commercial automated system with deposition times not exceeding 5 min. X-ray diffraction analysis shows that the thin films have cubic rock salt type structure with good crystallinity. The grain size increased from 18 to 20 nm, as the deposition time increased. Energy dispersive X-ray results confirm that the films are stoichiometric. Optical measurements show that thin films have relatively high absorption coefficients between 10⁴ and 10⁵ cm-1 in the visible range. In addition, the films exhibit a direct gap, within the energy range from 1.0 to 1.35 eV. The electrical properties, such as conductivity, the Seebeck coefficient, carrier concentration, and carrier mobility, are discussed.

Fabrication of photoluminescent n-type porous silicon (nPS), using electrode-assisted lateral electric field accompanied with a perpendicular magnetic field, is reported. The results have been compared with the porous structures fabricated by means of conventional anodization and electrode-assisted lateral electric field without magnetic field. The lateral electric field (LEF) applied across the silicon substrate leads to the formation of structural gradient in terms of density, dimension, and depth of the etched pores. Apart from the pore shape tunability, the simultaneous application of LEF and magnetic field (MF) contributes to a reduction of the dimension of the pores and promotes relatively more defined pore tips as well as a decreased side-branching in the pore walls of the macroporous structure. Additionally, when using magnetic field-assisted etching, within a certain range of LEF, an enhancement of the photoluminescence (PL) response was obtained.

Sediment microbial fuel cells (SMFC) are bioelectrochemical systems that can use different wastes for energy production. This work studied the implementation of nanoparticles (NPs) of α-FeOOH (goethite, which is well-known as a photoactive catalyst) in the electrodes of an SMFC for its potential use for dye removal. The results obtained demonstrate the feasibility of the NPs activation with the electrical potential generated in the electrodes in the SMFC instead of the activation with light. The NPs of α-FeOOH were synthesized using a hydrothermal process, and the feasibility of a conductive bio-composite (biofilm and NPs) formation was proven by X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and electrochemical techniques. The improvement of the power density in the cell was more than twelve times higher with the application of the bio-composite, and it is attributed mostly to the presence of NPs. The results also demonstrate the NPs effect on the increase of the electron transfer, which resulted in 99% of the COD removal. The total electrical energy produced in 30 days in the SMFC was 1.2 kWh based on 1 m² of the geometric area of the anode. The results confirm that NPs of α-FeOOH can be used to improve organic matter removal. Moreover, the energy produced due to its activation through the potential generated between the electrodes suggests the feasibility of its implementation for dye removal.

In the present work, we developed hybrid nanostructures based on ZnO films deposited on macroporous silicon substrates using the sol–gel spin coating and ultrasonic spray pyrolysis (USP) techniques. The changes in the growth of ZnO films on macroporous silicon were studied using a UV-visible spectrometer, an X-ray diffractometer (XRD), scanning electron microscopy (SEM) and atomic force microscopy (AFM). XRD analysis revealed the beneficial influence of macroporous silicon on the structural properties of ZnO films. SEM micrographs showed the growth and coverage of ZnO granular and flake-like crystals inside the pores of the substrate. The root mean square roughness (RMS) measured by AFM in the ZnO grown on the macroporous silicon substrate was up to one order of magnitude higher than reference samples. These results prove that the methods used in this work are effective to cover porous and obtain nano-morphologies of ZnO. These morphologies could be useful for making highly sensitive gas sensors.

The thermal behavior of the single-axis tracked bifacial photovoltaic (PV) module is important for efficient energy extraction in large-scale power plants, especially in tropical regions under high irradiation and high ambient temperature. However, it is difficult to accurately predict their operating temperature due to the complex interaction between environmental variables and the characteristics of solar tracking. The available models, ranging from empirical correlations and computational fluid dynamics (CFD) simulations to machine learning methods, face challenges in terms of accuracy, interpretability, and computational load. This gap is addressed in this study, with the development of a modeling methodology based on symbolic regression (SR) utilizing genetic algorithms (GA) towards obtaining an explicit, interpretable Equation for the prediction of the PV module temperature in single-axis tracking systems. One year of data was collected at 5-minute intervals from a 19.9 MW PV plant located in San Marcos, Colombia, consisting of measurements for solar radiation, ambient temperature, wind speed, and module temperature. The constructed SR GA model achieved satisfactory prediction accuracy compared to classic models with the best root mean square error (RMSE = 4.14 °C) and R² (0.91) on the test data set. These results compare favorably with results from MLR (RMSE = 4.31 °C, R² = 0.90), the standard industry NOCT model (RMSE = 8.59 °C, R² = 0.60), and the empirical Skoplaki I model (RMSE = 5.92 °C, R² = 0.81). The resulting symbolic equation directly characterizes the effects of nonlinear solar radiation, ambient temperature, and wind speed, providing greater physical insight into the thermal dynamics of the system. An important finding is that the maximum temperature of the bifacial module is reached around 14:00h, probably due to the accumulation of temperature caused by solar tracking, which contrasts with what occurs in fixed-tilt monofacial technology. This study demonstrates that the symbolic regression technique with a genetic algorithm kernel can produce accurate, interpretable, and computationally economical models for advanced photovoltaic systems.