The effect of microwave radiation on the hydrothermal synthesis of the double perovskite Sr₂FeMoO₆ has been studied based on a comparison of the particle size and structural characteristics of products from both methods. A temperature, pressure, and pH condition screening was performed, and the most representative results of these are herein presented and discussed. Radiation of microwaves in the hydrothermal synthesis method led to a decrease in crystallite size, which is an effect from the reaction temperature. The particle size ranged from 378 to 318 nm when pH was 4.5 and pressure was kept under 40 bars. According to X-ray diffraction (XRD) results coupled with the size-strain plot method, the product obtained by both synthesis methods (with and without microwave radiation) have similar crystal purity. The Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) techniques showed that the morphology and the distribution of metal ions are uniform. The Curie temperature obtained by thermogravimetric analysis indicates that, in the presence of microwaves, the value was higher with respect to traditional synthesis from 335 K to 342.5 K. Consequently, microwave radiation enhances the diffusion and nucleation process of ionic precursors during the synthesis, which promotes a uniform heating in the reaction mixture leading to a reduction in the particle size, but keeping good crystallinity of the double perovskite. Precursor phases and the final purity of the Sr₂FeMoO₆ powder can be controlled via hydrothermal microwave heating on the first stages of the Sol-Gel method.

The size of the nanoparticles (NP) is one of the most important and essential characteristics to know the properties of the synthesized nanostructures. The most common characterization procedures are related to Scanning Electronic Microscopy (SEM), Transmission Electronic Microscopy (TEM), and Atomic Force Microscopy (AFM). Unfortunately, from a practical point of view, they represent a time-consuming procedure and require expensive equipment, which limits its application to specialized research groups. Significant attention has been paid to Dynamic Light Scattering (DLS) as a simple, fast, and reproducible method for sizing nanoparticles. However, inadequate representation of the fundamental principles of DLS and data interpretation represents two of the most important challenges related to this technique. In this work we try to provide the fundamental principles of the DLS technique, the fundamental mathematical treatment of data obtained during the optical scattering studies and provide the MATLAB code to configure non-commercial DLS equipment. Additionally, analyzes of nanoparticles obtained by pulsed laser ablation of Ag, Au, Si and W and commercial Au nanoparticles were carried out. The particle size results are compared with SEM images to calculate the percentage error of the DLS measurements. The results show an error of 5%, 3.8%, 2.1% for the Ag, Au and Si nanoparticles respectively, which proves to be an excellent approximation to the real values of nanoparticle diameter. Meanwhile, the error in size for W nanoparticles by the same technique and commercial Au nanoparticle is 29% and 12%, which shows the effect of the hydrodynamic diameter of the nanoparticles. This work ends with the analysis of the concentration of nanoparticles and its importance in reliable results of DLS measurements.

This study focuses on the use of Au nanoparticles supported on the SrTiO_3−δ perovskite doped with Fe ions as a photocatalyst for CO₂ reduction and methanol production using water as the hydrogen supplier. The photocatalytic evaluation of the synthesized materials shows that Au nanoparticles supported on the SrTi_(1−x)Fe_xO_3−δ perovskites can enhance photocatalytic CO₂ reduction. The sample with the doping level x = 0.125 has the highest methanol production, 9.5 times more than the undoped sample and 3.5 times more than its counterpart without Au nanoparticles. The results of the DFT + U calculations show that the contributions from the iron atoms states reduce the energy gap in the perovskite composite, SrTi_(1−x)Fe_xO_3−δ. Additionally, Fe incorporation endows the system with the capacity to create electron–hole pairs via direct band transitions at the high‐symmetry point R with the doping levels of x = 0.125 and x = 0.250.

In this article, we describe a prototype photoreactor of which the geometrical configuration was obtained by Genetic Algorithms to maximize the residence time of the reactant gases. A gas reaction mixture of CO₂:H₂O (1:2 molar ratio) was studied from the fluid dynamic point of view. The two main features of this prototype reactor are the conical shape, which enhances the residence time as compared to a cylindrical shape reference reactor, and the inlet heights and position around the main chamber that enables turbulence and mass transfer control. Turbulence intensity, mixing capability, and residence time attributes for the optimized prototype reactor were calculated with Computational Fluid Dynamics (CFD) software and compared with those from a reference reactor. Turbulence intensity near the envisioned catalytic bed was one percentage point higher in the reference than in the optimized prototype reactor. Finally, the homogeneity of the mixture was guaranteed since both types of reactors had a turbulent regime, but for the prototype the CO₂ mass fraction was found to be better distributed.

Thermal lens oscillations represent a complex interplay of light–matter interactions, where surface forces, fluid motion, and heat transfer converge to produce self-sustained oscillatory behaviors. Although extensively studied, the underlying mechanisms governing these oscillations remain an open question in optical fluidics. Herein, we investigate the oscillatory dynamics of thermal lenses induced by temperature-driven surface tension gradients at liquid interfaces. Through fluid simulations corroborated by experimental observations, we demonstrate that Marangoni convection, coupled with surface curvature effects, plays a destabilizing role, initiating and sustaining the observed oscillations. Surface curvature modulates the temperature-induced surface tension gradients, enhancing interfacial flow and amplifying the oscillatory response. A simplified theoretical model effectively captures the interplay between thermal, viscous, and surface forces, linking the oscillatory behavior to key physicochemical surface properties. This approach not only advances the fundamental understanding of thermal lens phenomena but also provides a framework for determining critical parameters, such as density, viscosity, and surface tension. The insights gained from this study hold promise for the advancement of precision optical metrology and the development of next-generation fluidic and microfluidic systems.

The measurement of the optical wavefront phase remains a fundamental challenge in classical optics, particularly in systems where light–matter interactions induce complex thermal and refractive effects. Among these, thermal lensing represents a key manifestation of laser-induced heating, in which localized temperature gradients modify the refractive index of the medium, thereby altering light propagation. Notably, thermal lensing has been employed to characterize materials in the liquid state, as it produces a diffraction pattern that can be regarded as an optical fingerprint of the liquid. Precise phase retrieval is, therefore, essential for extracting the physicochemical properties of materials. In this work, we introduce and experimentally validate a robust phase retrieval method specifically designed for the accurate characterization of thermal lenses. The proposed approach is based on a novel intensity-only reconstruction framework using an artificial intelligence algorithm, specifically, a genetic algorithm, that requires only a single measurement of the photothermal-induced diffraction pattern, acquired through a simple, non-interferometric setup. The algorithm is optimized to efficiently handle the smooth phase gradients characteristic of thermal lenses, thereby overcoming convergence issues associated with wide search spaces. We demonstrate the efficacy of the method by quantitatively reconstructing the phase profile of thermal lenses induced in binary mixtures of water and methanol at different power levels, achieving excellent agreement between experimental observations and reconstructed results. These findings demonstrate that incorporating artificial intelligence–based phase reconstruction methods enables high-precision characterization of thermal lenses, revealing subtle features of light–matter interactions. Importantly, this approach opens new avenues for the study and characterization of materials, allowing simultaneous evaluation of their optical, thermal, and dynamic properties.