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The BaSi2O2N2:Eu2+ cyan-emitting phosphor can effectively enhance the color quality of white light-emitting diodes (LEDs) when it combined with red- and yellow-emitting phosphors. However, BaSi2O2N2:Eu2+ phosphors produced via traditional solid-state methods exhibit low quantum efficiency. Considerable efforts have been devoted to optimizing the luminescence characteristics of BaSi2O2N2:Eu2+. In the current work, BaSi2O2N2:Eu2+ phosphor was prepared via heterogeneous precipitation and thoroughly analyzed its thermal–humidity stability in view of practical applications. The main results show that the thermal–humidity stabilities of heterogeneous precipitation prepared BaSi2O2N2:Eu2+ reached the level of commercial BaSi2O2N2:Eu2+ phosphor. The external quantum efficiency of Ba1.96Si2O2N2:0.04Eu2+ phosphor prepared using our protocol exhibited 63%, which is outperformed the current commercial product. This study essentially provides an effective method for the preparation of the BaSi2O2N2:Eu2+ cyan phosphor for application in white LEDs with a high color rendering index. [ABSTRACT FROM AUTHOR]

Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al2O3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified. [ABSTRACT FROM AUTHOR]

Here, we describe a new method to study the biointeraction between Escherichia coli and mannose by using supramolectdar assemblies composed of polydiacetylene supported on the serf-assembled monolayer of octadecanethiol on a gold electrode. These prepared bilayer materials simply are an excellent protosystem to study a range of important sensor-related issues. The experimental results from UV--vis spectroscopy, resonance Raman spectroscopy, and electrochemistry confirm that the specific interactions between E. coli and mannose can cause conformational changes of the polydiacetylene backbone rather than simple nonspecific adsorption. Moreover, the direct electrochemical detection by polydiacetylene supramolecular assemblies not only opens a new path for the use of these membranes in the area of biosensor development but also offers new possibilities for diagnostic applications and screening for binding ligands.

Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al 2 O 3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Baoliang Ma et al.)

Quercetin, a flavonoid found in many fruits and vegetables, belongs to an extensive class of polyphenolic compounds. Previous studies reported that quercetin inhibits the proliferation of various cancer cells and tumor growth in animal models. We investigated the growth inhibition and colony formation of quercetin on three bladder cancer cells (EJ, J82 and T24). The expression of tumor suppressor genes and oncogenes such as P53, Survivin, PTEN, as well as the methylation status of these genes was also evaluated. We observed that quercetin induced apoptosis in bladder cancer cells in a time- and dose-dependent manner. Quercetin (100 micromolars) significantly inhibited EJ, T24 and J82 cell growth accompanied by an increase in the G0/G1 phase. In all cell lines, quercetin decreased the expression of mutant P53 and Survivin proteins. However, there was no change in the level of PTEN protein. Moreover, the DNA methylation levels of the estrogen receptor (Er-beta), P16INK4a and RASSF1A were strongly decreased (from 35 to 70%) in the quercetin-treated group compared to the control. In conclusion, our study suggested that quercetin inhibits growth, colony formation and hypermethylation of bladder cancer cell lines. Quercetin-induced apoptosis might be associated with a decrease in mutant P53 and Survivin proteins.