In the rapidly emerging field of biomedical applications, multifunctional nanoparticles, especially those containing magnetic and plasmonic components, have gained significant attention due to their combined properties. These hybrid systems, often composed of iron oxide and gold, provide both magnetic and optical functionalities and offer promising avenues for applications in multimodal bioimaging, hyperthermal therapies, and magnetically driven selective delivery. This paper focuses on the implementation of advanced characterization methods, comparing statistical analyses of individual multifunctional particle properties with macroscopic properties as a way of fine-tuning synthetic methodologies for their fabrication methods. Special emphasis is placed on the size-dependent properties, biocompatibility, and challenges that can arise from this versatile nanometric system. In order to ensure the quality and applicability of these particles, various novel methods for characterizing the magnetic gold particles, including the analysis of their morphology, optical response, and magnetic response, are also discussed, with the overall goal of optimizing the fabrication of this complex system and thus enhancing its potential as a preferred diagnostic agent.

Plasmonic nanoparticles have a wide range of applications in science and industry. Despite the numerous synthesis methods reported in the literature over the last decades, achieving precise control over the size and shape of large nanoparticle populations remains a challenge. Since variations in size and shape significantly affect the plasmonic properties of nanoparticles, accurate metrological techniques to characterize their morphological features are essential. Here, we present a novel spectrophotometric method, called Amplitude-Resolved Single Particle Spectrophotometry, that can measure the individual sizes of thousands of particles with nanometric accuracy in just a few minutes. This new method, based on the measurement of the scattering amplitude of each nanoparticle, overcomes some of the limitations observed in previous works and theoretically allows the characterization of nanoparticles of any size with a simple extra calibration step. As proof of concept, we characterized thousands of spherical nanoparticles of different sizes. This new method shows excellent accuracy, with less than a 3% discrepancy in direct comparison with transmission electron microscopy. Although the effectiveness of this method has been demonstrated with spherical nanoparticles, its real strength lies in its adaptability to more complex geometries by using an alternative analytical method to the one described here.

The formation of xerogels implies a sequence of hydrolysis and condensation reactions, which are intricate to analyze in heteromolecular sols. We analyze by probabilistic Montecarlo methods the development of hybrid organosilane–titania xerogels and illustrate how partial charges of the reacting molecules can help estimating relative probabilities for the condensation of the molecules. Since the condensation rate of Ti alkoxides is much higher than the corresponding rate of Si alkoxides (especially if bearing a non-hydrolizable group), by imposing a fast condensation process in agreement with low pH kinetics, the process leads to a surface segregation of the organosilane. The simulation results are compared with results of characterization of thin condensates of two different organosilanes within a titanium–isopropoxide matrix. Non-destructive in-depth profiles were obtained by hard x-ray photoelectron spectroscopy, which can resolve through estimation of Si and specific moieties of the organosilane molecules the progress of the condensation. These results are relevant for the generalization of chemo-functionalization processes by kinetic demixing of organosilanes, which have myriad applications in biomedicine and biotechnology.

In this work, an approach for cooperative and distributed mapping in a self-adaptive formation system based on a modified version of the ant colony optimization algorithm is proposed. The strategy is distributed, decentralized, real time and it is applied to tasks in which formation characteristic is an essential requirement. The coordination system's design is inspired by the biological mechanisms that define a social organization in collective systems, specifically, the ant colony system. Voronoi tessalation and Delaunay triangulation techniques are used to model the formation strategy. The approach is adaptable for scenarios with suffer changes in the structure of the environment. The performance of the system is evaluated using a simulator. Simulation results show that the cooperative mapping is efficient, the trials are performed considering an indoor environment. Besides results show that the proposed formation approach is able to rearrange spatially the robots as they navigate, changing the relative robot distances according to the spatial environment restrictions.