Marko G. Nikolic

Dense Ce,Pr-doped YAG transparent ceramics (YAG:Ce,Pr) with enhanced red emission were fabricated by pressureless sintering in air at 1600 °C. Fine YAG powders containing 0.2 mol% Ce and varying amounts of Pr (0.04–0.32 mol%) were synthesized using a glycine–nitrate combustion method. The emission and excitation spectra were analyzed to investigate the influence of Pr doping on red emission. The results indicate that YAG: Ce,Pr ceramics are a promising candidate for blue-to-yellow light conversion. The strongest red emission component was measured in samples containing 0.16 mol% Pr. It was confirmed that electron transfer from Ce to Pr enhances the red emission component (609 nm), primarily by increasing the population of electrons relaxing from the ¹D₂ excited state to the ³H₄ ground state. Further increasing the Pr concentration beyond 0.16 mol% resulted in a reduction in the red emission intensity due to concentration quenching. Microstructural analysis of YAG:Ce,Pr revealed a dense and homogeneous microstructure composed of equiaxed grains.

In this study, we explore the potential of applying machine learning (ML) to enhance high-pressure luminescence sensing. We investigate the luminescence behavior of Y₂MoO₆:Eu³⁺, synthesized via a self-initiated, self-sustained reaction. Emission spectra were collected under varying pressures using a 405 nm laser diode and an AVANTES AvaSpec 2048TEC USB2 spectrometer. An analysis of the pressure-dependent curve, based on the intensities of two key peaks, indicates a possible crystal phase transition or another underlying physical phenomenon. Moreover, the non-unique relationship between pressure and peak intensity limits its effectiveness for precise sensing. To overcome this challenge, we employ an ML-based approach, combining Uniform Manifold Approximation and Projection (UMAP) for data visualization with a deep neural network to estimate pressure directly from the full luminescence spectrum. This strategy significantly extends the usable pressure range of Y₂MoO₆:Eu³⁺ up to 12 GPa, representing a marked improvement over conventional methods.

This study investigates the high-pressure luminescence characteristics of Y2MoO6:Eu3+, a material prepared by a self-initiated and self-sustained reaction. Using a 405 nm laser diode and an Ocean optics spectrometer, we acquired emission spectra under varying pressures. While the pressure-dependent curve, based on the intensities of two key peaks, clearly showed a crystal phase transition, the resulting non-unique relationship between pressure and peak intensity hindered its use for precise sensing. We address this limitation by introducing a machine learning approach. Our method uses Uniform Manifold Approximation and Projection (UMAP) for initial data visualization and a deep neural network to accurately estimate pressure from the full luminescence spectrum.

The gadolinium vanadate doped with samarium (GdVO₄:Sm³⁺) nanopowder was prepared by the solution combustion synthesis (SCS) method. After synthesis, in order to achieve full crystallinity, the material was annealed in air atmosphere at 900 °C. Phase identification in the post-annealed powder samples was performed by X-ray diffraction, and morphology was investigated by high-resolution scanning electron microscope (SEM) and transmission electron microscope (TEM). Photoluminescence characterization of emission spectrum and time resolved analysis was performed using tunable laser optical parametric oscillator excitation and streak camera. In addition to samarium emission bands, a weak broad luminescence emission band of host VO₄³⁻ was also observed by the detection system. In our earlier work, we analyzed the possibility of using the host luminescence for two-color temperature sensing, improving the method by introducing the temporal dependence in line intensity ratio measurements. Here, we showed that further improvements are possible by using the machine learning approach. To facilitate the initial data assessment, we incorporated Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) clustering of GdVO4:Sm³⁺ spectra at various temperatures. Good predictions of temperature were obtained using deep neural networks. Performance of the deep learning network was enhanced by data augmentation technique.

An upgrade of the electronic system for frequency stabilization of the HeNe laser, primary length standard of Republic of Serbia, based on digital electronics, is described. Arduino microcontrollers have been used for stabilization, and laptop computer has been used only to communicate with the user. In addition, an analog electronics has been developed in order to boost the performance of the setup. The setup is simple and inexpensive, made of off-the-shelf electronics components. Despite this, good performances have been achieved.

This paper examines the potential applications of machine learning algorithms in the analysis of optical spectra from Gd₂O₃:Er,Yb thermophosphor. The material was synthesized using the solution combustion method. For data acquisition, we employed pulsed laser diode excitation at 980 nm and utilized a streak camera with a spectrograph to obtain time-resolved spectral data of the optical emission from Gd₂O₃:Er,Yb. To ensure data consistency and facilitate visualization, we employed principal component analysis and Uniform Manifold Approximation and Projection clustering. Our findings demonstrate that, instead of the conventional approach of identifying spectral peaks and calculating intensity ratios, it is feasible to train computer software to recognize time-resolved spectra associated with different temperatures of the thermophosphor. Through our analysis, we have successfully devised a technique for remote temperature estimation by leveraging deep learning artificial neural networks.

The aim of this paper is to discuss some theoretical possibilities for engineering multifunctional nanomaterials. The discussions are founded on calculations previously performed for 1D and 2D nanomaterials within the Hubbard model (HM). The main results of the HM are briefly reviewed. The conclusion is that the approach taken in this paper is a distinct improvement over those in the literature.In the present paper results of applications of the (HM) are directly used in examples of engineering nanomaterials. On the other hand, in the literature calculations were performed by ab initio methods and then fitted to the form of the Hamiltonian of the (HM). It was shown on several examples that it is possible to engineer multifunctional nano-materials by calculating their conductivity, reflectivity and sensitivity for piezoresistivity, making calculations within the Hubbard model a viable way in theoretically modelling nanomaterials.

In this manuscript, down-conversion nanopowders of SrGd2O4 doped with different concentrations of either Dy3+ or Sm3+ ions were examined in detail. All samples were prepared via glycine-assisted combustion method, primarily burned at 500?C for 1.5 h and additionally calcined at 1000?C for 2 h, at ambient room temperature. The XRD analysis showed that all samples crystallize as single phase and the orthorhombic lattice SrGd2O4. TEM analysis determined high degree of crystallinity of samples with grain size of approximately 200 nm for Dy3+ doped and 150 nm for Sm3+ doped SrGd2O4. For both samples SAED confirmed that diffraction rings correspond to the hkl plane indices of SrGd2O4, while EDS confirmed presence of Dy in crystal structure. Results of luminescent characterization demonstrated all appropriate emission peaks related to either Dy3+ or Sm3+ dopant ions. Investigation of dopant concentration revealed that the lowest values of both dopants have the most prominent emission peaks, while coordinates obtained from the CIE diagram showed emission shifting with the change of concentration.

Magnesium aluminate and other alumina-based spinels attract attention due to their high hardness, high mechanical strength, and low dielectric constant. MgAl₂O₄ was produced by a solid-state reaction between MgO and α-Al₂O₃ powders. Mechanical activation for 30 min in a planetary ball mill was used to increase the reactivity of powders. Yttrium oxide and graphene were added to prevent abnormal grain growth during sintering. Samples were sintered by hot pressing under vacuum at 1450 °C. Phase composition and microstructure of sintered specimens were characterized by X-ray powder diffraction and scanning electron microscopy. Rietveld analysis revealed 100% pure spinel phase in all sintered specimens, and a decrease in crystallite size with the addition of yttria or graphene. Density measurements indicated that the mechanically activated specimen reached 99.6% relative density. Furthermore, the highest solar absorbance and highest spectral selectivity as a function of temperature were detected for the mechanically activated specimen with graphene addition. Mechanical activation is an efficient method to improve densification of MgAl₂O₄ prepared from mixed oxide powders, while additives improve microstructure and optical properties.