A review of phosphorescent and fluorescent phosphors for fingerprint detection

Currently, the efficient detection of fingerprints is essential for the crime investigations. Revealing fingerprints is commonly achieved with fluorescent organic compounds but they are not efficient for fingerprint detection on porous or reflective surfaces. In order to solve the problem of collecting fingerprints on porous/reflective surfaces, inorganic phosphors have been employed, since they have characteristics of variable color emission, afterglow, high chemical stability and nano-size, which allow the fingerprint detection on any porous or non-porous surfaces. Due to these last properties, this review presents a summary about the use of phosphorescent and fluorescent phosphors for the detection of latent fingerprints. First, we discussed the main physical and chemical characteristics of the fingerprints which permit their detection and collection from any surface. After this, we presented the main morphological, structural and luminescent properties of the phosphorescent and fluorescent phosphors that allow their use for fingerprint detection. Later, we demonstrated with pictures of fingerprints (with and without light emission from the phosphors deposited on them) that both, phosphorescent and fluorescent phosphors can be used to visualize fingerprints with high resolution and high contrast without interference of the background surface, which is ideal for its collection and registration in the Automated Fingerprint Identification System (AFIS). We believe that this review could be useful to understand how to select an appropriate phosphorescent or fluorescent material for fingerprint detection depending on the type of surface (porous or non-porous, reflective or not reflective) where the fingerprint is deposited.     

Salt-assisted solution combustion synthesis of NiFe2O4: Effect of salt type

The effects of three types of salt including NaF, KCl, and NaCl on the properties of NiFe₂O₄ nanoparticles using salt-assisted solution combustion synthesis (SSCS) have been investigated. The synthesized powders were evaluated by SEM, TEM, FTIR, XRD, and VSM analysis. Also, the specific surface area (SSA), as well as size distribution and volume of the porosities of NiFe₂O₄ powders were determined by the BET apparatus. The visual observations showed that the intensity and time of combustion synthesis of nanoparticles have been severely influenced by the type of salt. The highest crystallinity was observed in the synthesized powder using NaCl. The SSA has also been correlated completely to the type of salt. The quantities of SSA was achieved about 91.62, 64.88, and 47.22 m²g^-1 for the powders synthesized by KCl, NaCl, and NaF respectively. Although the magnetic hysteresis loops showed the soft ferromagnetic behavior of the NiFe₂O₄ nanoparticles in all conditions, KCl salt could produce the particles with the least coercivity and remanent magnetization. Based on the present study, the salt type is a key parameter in the SSCS process for the preparation of spinel ferrites. Thermodynamic evaluation also showed that the melting point and heat capacity are important parameters for the proper selection of the salt.     

Structure–phase state of the surface layers in rails subjected to differential hardening

A layer-by-layer analysis of the structures, the phase compositions, and the defect substructures of differentially hardened rails has been carried by optical and transmission electron microscopy. It is found that the material volume of the rail head fillet is cooled faster than the volume located along the central axis.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

An automated system based on cryogenic probe station for integrated studies of semiconductor light-emitting structures and wafers in the range of 15 to 475 K

An automated system for integrated electrophysical and optical studies of semiconductor nanoheterostructures, which operates in a wide temperature range from 15 to 475 K, is designed. The setup is intended to measure the temperature and frequency admittance and electroluminescence spectra of light-emitting diode and laser chips formed on substrates of diameter up to 50.2 mm, and the distribution of parameters over the wafer. The setup includes the closed-cycle helium cryogenic station, LCR meter, and temperature controller. The characterization results of nanoheterostructures with InGaN/GaN multiple quantum wells, which are used for creating highly efficient white and blue light-emitting diodes, are presented.