In this work, iron oxide nanoparticles produced using the laser ablation technique were studied in order to determine the characteristics of these nanoparticles as a function of the laser energy for the possible application in magnetic hyperthermia. Nanoparticles were obtained by varying the power of the laser considering values of 90, 173, 279 and 370 mJ. The morphology of these nanoparticles was determined using the dynamic light scattering (DLS) and scattering transmission electron microscopy (STEM) techniques, confirming that the size of the particles was in the order of nanometers. A great influence of the laser power on the particle size was also observed, caused by the competition between the energy and the temperature. The composition was determined by X-ray diffraction and Raman spectroscopy, showing the presence of magnetite, maghemite and hematite. The hyperthermia measurements showed that the temperature rise of the iron oxide nanoparticles was not greatly influenced by the energy change, the heating capacity of magnetic NPs is quantified by the specific absorption rate (SAR), that tends to decrease with increasing energy, which indicates a dependence of these values on the nanoparticles concentration.

Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra- and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms $$\mu $$μ and $$\beta $$β .

Thermal waters are natural resources of great value to geothermal sciences, the tourism industry, and health. In this work, geochemical classification of physicochemical results of 17 sources at the Puracé-La Mina (Cauca, Colombia) sector was implemented in order to strengthen and determine their potential applications and enhance the continental tourism in Colombia. The analyzed parameters were developed following the Standard Methods 22nd edition, at Universidad Nacional de Colombia-Manizales. According to the results obtained by means of a geochemical classification, it was found that most of the sources have a sulfated-acid nature which makes them heated vapor waters and volcanic waters. Likewise, it was observed that all the sources are immature waters and still do not reach chemical equilibrium. On the other hand, mineralogical and chemical characterization by means of XRD and XRF showed a high content of silica isomorphous minerals with a low concentration. In addition, the presence of Fe₂O₃ was observed, which is insoluble at pH > 5 and remains in the rock. Nevertheless, considering that mine sources possess pH ± 2 and temperatures of 40°C, leaching is possible for iron justifying its presence in the water. Instead, elements like Na⁺, K⁺, Mg²⁺, and Ca²⁺ have high mobility at the conditions of mine sources (low pH) as a consequence of hydrolysis processes, which produce variations in water composition.