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Half-sandwiched structure iridium­(III) complexes appear to be an attractive organometallic antitumor agents in recent years. Here, four triphenylamine-modified fluorescent half-sandwich iridium­(III) thiosemicarbazone (TSC) antitumor complexes were developed. Because of the “enol” configuration of the TSC ligands, these complexes formed a unique dimeric configuration. Aided by the appropriate fluorescence properties, studies found that complexes could enter tumor cells in an energy-dependent mode, accumulate in lysosomes, and result in the damage of lysosome integrity. Complexes could block the cell cycle, improve the levels of intrastitial reactive oxygen species, and lead to apoptosis, which followed an antitumor mechanism of oxidation. Compared with cisplatin, the antitumor potential in vivo and vitro confirmed that Ir4 could effectively inhibit tumor growth. Meanwhile, Ir4 could avoid detectable side effects in the experiments of safety evaluation. Above all, half-sandwich iridium­(III) TSC complexes are expected to be an encouraging candidate for the treatment of malignant tumors.

A series of half‐sandwich structural iridium(III) phenanthroline (Phen) complexes with halide ions (Cl−, Br−, I−) and pyridine leaving groups ([(η5‐CpX)Ir(Phen)Z](PF6)n, Cpx: electron‐rich cyclopentadienyl group, Z: leaving group) have been prepared. Target complexes, especially the Cpxbiph (biphenyl‐substituted cyclopentadienyl)‐based one, showed favourable anticancer activity against human lung cancer (A549) cells; the best one (Ir8) was almost five times that of cisplatin under the same conditions. Compared with complexes involving halide ion leaving groups, the pyridine‐based one did not display hydrolysis but effectively caused lysosomal damage, leading to accumulation in the cytosol, inducing an increase in the level of intracellular reactive oxygen species and apoptosis; this indicated an anticancer mechanism of oxidation. Additionally, these complexes could bind to serum albumin through a static quenching mechanism. The data highlight the potential value of half‐sandwich iridium(III) phenanthroline complexes as anticancer drugs.

In the study of polymer flooding, researchers usually ignore the genetic stress properties of viscoelastic fluids. In this paper, we investigate the process of viscoelastic fluid flooding the remaining oil in the dead end. This work uses the fractional-order Maxwell in the traditional momentum equation. Furthermore, a semi-analytic solution of the flow control equation for fractional-order viscoelastic fluids is derived, and the oil-repelling process of viscoelastic fluids is simulated by a secondary development of OpenFOAM. The results show that velocity fractional-order derivative α significantly affects polymer solution characteristics, and increasing the elasticity of the fluid can significantly improve the oil repelling efficiency. Compared to the Newtonian fluid flow model, the fractional order derivative a and relaxation time b in the two-parameter instanton equation can accurately characterize the degree of elasticity of the fluid. The smaller the a, the more elastic the fluid is and the higher the oil-repelling efficiency. The larger the b, the less elastic the fluid is and the lower the cancellation efficiency. Moreover, the disturbance of the polymer solution to the dead end is divided into two elastic perturbation areas. The stronger the elasticity of the polymer solution, the higher the peak value of the area in the dead end and the higher the final oil displacement efficiency.

Forecasting models have a high value in the field of finance, we can better gain in investment and it provide a better basis for the national macroeconomic control strategy. In this paper we build three forecasting models based on a combination of linear ARIMA time series and nonlinear Back-Propagation neural networks to improve the accuracy of forecasting. The first model uses direct summation; the second uses ARIMA and Back-Propagation neural network forecasting results as independent variables and actual prices as dependent variables to build a multiple linear regression model; the third uses bp neural network to indirectly compensate for the residuals of ARIMA results. The more representative gold in the international market was selected as the forecasting object, and the final errors were respectively, proving that the new combination method can improve the accuracy.