In this work, we present a simple and efficient method for pure phase magnetite (Fe₃O₄) nanoparticle synthesis. The phase structure, particle shape, and size of the samples were characterized by Raman spectroscopy (Rm), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray (EDS), and transmission electron microscopy (TEM). The morphology tuning was controlled by the temperature of the reaction; the nanoparticles were synthesized via the hydrothermal method at 120°C, 140°C, and 160°C, respectively. The Rm and XRD spectra showed that all the nanoparticles were Fe₃O₄in a pure magnetite phase. The obtained nanoparticles exhibited a high level of crystallinity with uniform morphology at each temperature, as can be observed through TEM and SEM. These magnetic nanoparticles exhibited good saturation magnetization and the resulting shapes were quasi-spheres, octahedrons, and cubes. The samples showed striking magnetic properties, which were examined by a vibrating sample magnetometer (VSM). It has been possible to obtain a good morphological control of nanostructured magnetite in a simple, economical, and scalable method by adjusting the temperature, without the modification of any other synthesis parameter.

Background. The incorporation of an antibacterial agent into an adhesive could improve its clinical performance. Some nanoparticles (NPs), including copper nanoparticles (Cu NPs), display an antibacterial effect. Therefore, Cu NPs could act as a nanofiller when added to an adhesive. Objectives. The aim of this study was to evaluate the antibacterial activity, cytotoxicity and shear bond strength (SBS) of an experimental dental adhesive with Cu NPs. Material and methods. Different concentrations (0.0050 wt%, 0.0075 wt% and 0.0100 wt%) of Cu NPs were added to the adhesive. The distribution of Cu NPs in the polymer matrix was observed based on transmission electron microscope (TEM) images. The antimicrobial activity of the adhesive + Cu NPs was evaluated with the agar disk diffusion test against Staphylococcus aureus (S. aureus), Escherichia coli (E. coli) and Streptococcus mutans (S. mutans). The cytotoxicity assay was performed by means of the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) method with human pulp cells (HPC). Additionally, the SBS tests were carried out (n = 31) and the modes of fracture were registered. The vestibular and lingual surfaces of each tooth were randomly assigned to the study groups (group I - control adhesive; group II - adhesive + 0.0100 wt% Cu NPs). The samples were statistically analyzed (p <= 0.05). Results. The adhesive + 0.0100 wt% Cu NPs showed inhibition zones against the strains under study that were similar to, or slightly smaller than, the halos produced by chlorhexidine (CHX) and specific drugs for each strain (30 mu g of cefotaxime against S. mutans and S. aureus, and 1.25/3.75 mu g of sulfamethoxazole/trimethoprim against E. coli). The control adhesive was moderately cytotoxic (relative cell viability of 36.7 +/- 0.8%), being more cytotoxic than Cu NPs themselves (58.3 +/- 0.1%). A significantly higher SBS was obtained for the adhesive + 0.0100 wt% Cu NPs (6.038 +/- 2.95 MPa) than for the control group (3.278 +/- 1.75 MPa). The modes of fracture in group I were almost equally distributed between adhesive and cohesive failures whereas in group II, the failure was mainly cohesive. Conclusions. The results of this study suggest that incorporating Cu NPs into an adhesive improves its SBS and provides it with antibacterial properties, without increasing its inherent cytotoxicity - 2 desirable characteristics for the dental adhesives of composites.

Processing images represents a necessary step in the process of analysing the information gathered about nanoparticles after characteristic material samples have been scanned with electron microscopy, which often requires the use of image processing techniques or general purpose image manipulation software to carry out tasks such as nanoparticle detection and measurement. In recent years, the use of networks has been successfully implemented to detect and classify electron microscopy images as well as the objects within them. In this work, we present four detection models using two versions of the YOLO neural network architectures trained to detect cubical and quasi-spherical particles in SEM images; the training datasets are a mixture of real images and synthetic ones generated by a semi-arbitrary method. The resulting models were capable of detecting nanoparticles in images different than the ones used for training and identifying them in some cases as the close proximity between nanoparticles proved a challenge for the neural networks in most situations.

An ether gas-sensor was fabricated based on gold nanoparticles (Au-NPs) decorated zinc oxide microstructures (ZnO-MS). Scanning electron microscope (SEM) and high-resolution transmission electron microscope (HRTEM) measurements were performed to study morphological and structural properties, respectively, of the ZnO-MS. The gas sensing response was evaluated in a relatively low temperature regime, which ranged between 150 and 250 °C. Compared with a sensor fabricated from pure ZnO-MS, the sensor based on Au-NPs decorated ZnO-MS showed much better ether gas response at the highest working temperature. In fact, pure ZnO-MS based sensor only showed a weak sensitivity of about 25%. The improvement of the ether gas response for sensor fabricated with Au-NPs decorated ZnO-MS was attributed to the catalytic activity of the Au-NPs. Keywords: ZnO microstructures, Au nanoparticles, Ether, Gas senso