Investigation on Light Extraction Behavior of Surface Plasmon-Coupled Deep-Ultraviolet LED in Different Emission Directions

The light extraction behavior of an AlGaN-based deep-ultraviolet LED covered with Al nanoparticles (NPs) is investigated by three-dimensional finite-difference time-domain simulation. For the transmission spectra of s- and p-polarizations in different emission directions, the position of maximum transmittance can be changed from (θ = 0°, λ = 273 nm) to (θ = 0°, λ = 286 nm) by increasing the diameter of Al NPs from 40 nm to 80 nm. In the direction that is greater than the critical angle, the transmittance of s-polarization is very small due to the strong absorption of Al NPs, while the transmittance spectrum of p-polarization can be observed obviously for the 80 nm Al NPs structure. For a ~284 nm AlGaN-based LED with surface plasmon (SP) coupling, although the luminous efficiency is significantly improved due to the improvement of the radiation recombination rate as compared with the conventional LED, the light extraction efficiency (LEE) is lower than 2.61% of the conventional LED without considering the lateral surface extraction and bottom reflection. The LEE is not greater than ~0.98% (~2.12%) for an SP coupling LED with 40 nm (80 nm) Al NPs. The lower LEE can be attributed to the strong absorption of Al NPs.

Tomato maturity recognition using YOLOV 5 machine learning

The implementation of robotic systems and digitalization in agriculture are important tasks today. In this paper, the possibility of using pattern recognition and machine learning methods as a computer model of an agricultural robot for harvesting is considered. The grading of tomato fruits can be classified based on their ripeness according to their life cycles, which can be identified by their color: green in the growing stage, yellow in the pre-ripening stage, and red when ripe. Conventional skill-based methods cannot meet the exact selection criteria for modern production management in the agricultural sector as they are time-consuming and of low accuracy. Automatic feature extraction behavior using machine learning is most effective in image classification and recognition tasks. Thus, the article presents the results of a study on the recognition of ripe tomato fruits by a robotic system, carried out within the framework of the grant project of the Ministry of Education and Science of the Republic of Kazakhstan AP08857573 and implemented classical algorithms based on the HSV color model and color segmentation using the k-means algorithm as comparative algorithms and based on machine learning, a universal intelligent tomato classification system is proposed for practical use using Yolo 5. This study aims to provide an inexpensive solution with the best performance and accuracy for assessing tomato ripeness. The results are collected in terms of accuracy, loss curves and confusion matrix. The results showed that the proposed model outperforms other machine learning (ML) methods used by researchers for tomato classification problems, providing 99% accuracy.

Ionic Liquid Extraction Behavior of Cr(VI) Absorbed on Humic Acid–Vermiculite

Cr(VI) can be released into soil as a result of mining, electroplating, and smelting operations. Due to the high toxicity of Cr(VI), its removal is necessary in order to protect ecosystems. Vermiculite is applied in situations where there is a high degree of metal pollution, as it is helpful during the remediation process due to its high cation exchange capacity. The Cr(VI) contained in the vermiculite should be extracted in order to recover it and to reduce the impact on the environment. In this work, adsorption equilibrium data for Cr(VI) in a simulated sorbent for soil remediation (a mixture that included both humic acid (HA) and vermiculite) were a good fit with the Langmuir isotherm model. The simulated sorbent for soil remediation was a favorable sorbent for Cr(VI) when it was in the test soil. An ionic liquid, [C4mim]Cl (1-butyl-3-methylimidazolium chloride), was studied to determine its efficiency in extracting Cr(VI) from the Cr- contaminated simulated sorbent in soil remediation. At 298 K and within 30 min, approximately 33.48 ± 0.79% of Cr(VI) in the simulated sorbent in soil remediation was extracted into [C4mim]Cl. Using FTIR spectroscopy, the absorbance intensities of the bands at 1032 and 1010 cm−1, which were attributed to C-O bond stretching in the polysaccharides of HA, were used to detect the changes in HA in the Cr-contaminated simulated sorbent for soil remediation before and after extraction. The results showed that Cr(VI) that has been absorbed on HA can be extracted into [C4mim]Cl. Using 1H NMR, it was observed that the 1-methylimizadole of [C4mim] Cl played an important role in the extraction of Cr(VI), which bonded with HA on vermiculite and was able to be transformed into the [C4mim]Cl phase.

Influence of Dysprosium Compounds on the Extraction Behavior of Dy from Nd-Dy-Fe-B Magnet Using Liquid Magnesium

During the liquid metal extraction reaction between a Nd-Dy-Fe-B magnet and liquid Mg, Nd rapidly diffuses out of the magnet, whereas Dy is not extracted due to the reaction with the matrix and the formation of Dy2Fe17 phase. In addition, theDy2O3 phase exists at the grain boundaries. Until now, only the effect of the Dy2O3 phase on the extraction of Dy has been reported. In this study, the effect of the Dy2Fe17 phase on the extraction of Dy from the Nd-Dy-Fe-B magnet was investigated in liquid Mg. The formation of the Dy2Fe17 phase during the reaction between Mg and matrix (RE2Fe14B) was first examined using a thermodynamical approach and confirmed by microstructural analysis. It was observed that Dy extraction was dominated by Dy2Fe17 phase decomposition from 3 h to 24 h, followed by Dy2O3 phase dominant reaction with Mg. Comparing the activities of the Dy2Fe17 phase and the Dy2O3 phase, the reaction of Dy2Fe17 is dominant, as compared to the Dy2O3 phase. Finally, at 48 h, the high Dy extraction percentage of 93% was achieved. As a result, in was concluded that the Dy2Fe17 phase acts as an obstacle in the extraction of Dy. In the future, if research to control the Dy2Fe17 phase proceeds, it will be of great importance to advance the recycling of Dy.