Mechanical properties and wear behavior of Tin+1(Al,A)Cn (A = Ga,In, Sn,n = 1, 2) via quasi-high-entropy of single atomic thick A layer

The strengthening method of multi-element M-site solid solution is a common approach to improve mechanical properties of MAX phase ceramic. However, the research on capability of multi-element A-site solid solution to improve mechanical properties has rarely been reported. Thereupon, quasi-high-entropy MAX phase ceramic bulks of Ti₂(Al_1?xA_x)C and Ti₃(Al_1?xA_x)C₂ (A = Ga, In, Sn, x = 0.2, 0.3, 0.4) were successfully synthesized by in situ vacuum hot pressing via multi-elements solid solution. The multi-elements solid solution in single-atom thick A layer was confirmed by X-ray diffraction and X-ray photoelectron spectroscopy as well as by energy dispersive X-ray spectroscopy mappings. Effects of doped multi-elements contents on the phase, microstructure, mechanical properties, and high temperature tribological behaviors were studied. Results demonstrated that the Vickers hardness, anisotropic flexural strength, fracture toughness, and tribological properties of Ti–Al–C based MAX ceramics could be remarkably improved by constitution of quasi-high-entropy MAX phase in A layers. Moreover, the strengthening and wear mechanisms were also discussed in detail. This method of multi-element solid solution at A-site provides new way to enhance mechanical properties of other MAX phase ceramics.     

Microstructural and mechanical properties assessment of transient liquid phase bonding of CoCuFeMnNi high entropy alloy

The transient liquid phase (TLP) bonding of CoCuFeMnNi high entropy alloy (HEA) was studied. The TLP bonding was performed using AWS BNi-2 interlayer at 1050 °C with the TLP bonding time of 20, 60, 180 and 240 min. The effect of bonding time on the joint microstructure was characterized by SEM and EDS. Microstructural results confirmed that complete isothermal solidification occurred approximately at 240 min of bonding time. For samples bonded at 20, 60 and 180 min, athermal solidification zone was formed in the bonding area which included Cr-rich boride and Mn₃Si intermetallic compound. For all samples, the γ solid solution was formed in the isothermal solidification zone of the bonding zone. To evaluate the effect of TLP bonding time on mechanical properties of joints, the shear strength and micro-hardness of joints were measured. The results indicated a decrement of micro-hardness in the bonding zone and an increment of micro-hardness in the adjacent zone of joints. The minimum and maximum values of shear strength were 100 and 180 MPa for joints with the bonding time of 20 and 240 min, respectively.     

A novel approach for bulk micromachining of 4H-SiC by tool-based electrolytic plasma etching in HF-free aqueous solution

Bulk micromachining of single-crystal SiC has been challenging due to its extreme stability both mechanically and chemically. To address this issue, a novel tool-based electrolytic plasma etching method is proposed, with which micropatterns and micro-holes are fabricated in SiC in a hydrofluoric acid-free aqueous solution with no need for masks. The material removal is the result of the combined effects of electrolytic plasma chemistry and physics. The chemistry refers to the reaction of Si with hydroxyl radical to form various SiO_x and with H to form silanes, and the reactions of C to form volatile carbon oxides or hydrocarbons, all of which are accomplished and enhanced under the electrolytic plasma atmosphere. Besides, the local high temperature of plasma thermally promotes the evaporation or dissolution of SiO₂ in NaOH solution. The tool-based electrolytic plasma etching method provides alternative approaches for the fabrication of SiC-based MEMS and devices.     

高熵形状记忆合金的研究进展

高熵形状记忆合金是在等原子比NiTi合金的基础上,结合高熵合金的概念,逐渐发展起来的一种新型高温形状记忆合金。近年来,已开发出了综合性能优异的(TiZrHf)50(NiCoCu)50系和(TiZrHf)50(NiCuPd)50系高熵形状记忆合金,引起了广泛的关注和研究兴趣。本文从物相组成、微观组织、马氏体相变行为、形状记忆效应和超弹性等角度出发,综述了高熵形状记忆合金的研究进展,并对高熵形状记忆合金未来的研究重点进行了展望。     

氮含量及固溶温度对21-6-9奥氏体不锈钢组织和硬度的影响

为了探讨氮含量及固溶温度对21-6-9不锈钢组织和硬度的影响,分别在950、1000、1050和1100 ℃对3种不同氮含量的热轧态21-6-9不锈钢进行1 h固溶处理,通过光学显微镜观察其组织结构,结合Thermo-Calc热力学计算对试验钢的微观组织进行分析,并对其进行硬度测试。结果表明,0.20%~0.28%N的21-6-9不锈钢热轧后沿轧制方向析出铁素体,且钢中铁素体经950~1100 ℃固溶处理可消除,当N含量达到0.34%时,试验钢中不再出现铁素体。随着固溶处理温度的升高,21-6-9不锈钢的晶粒组织长大,硬度降低。N含量的增加可显著提高固溶态21-6-9不锈钢的硬度,其增加程度随固溶处理温度的升高而减弱。     

A Novel Formulation of Glucose-Sparing Peritoneal Dialysis Solutions with l-Carnitine Improves Biocompatibility on Human Mesothelial Cells

The main reason why peritoneal dialysis (PD) still has limited use in the management of patients with end-stage renal disease (ESRD) lies in the fact that the currently used glucose-based PD solutions are not completely biocompatible and determine, over time, the degeneration of the peritoneal membrane (PM) and consequent loss of ultrafiltration (UF). Here we evaluated the biocompatibility of a novel formulation of dialytic solutions, in which a substantial amount of glucose is replaced by two osmometabolic agents, xylitol and l-carnitine. The effect of this novel formulation on cell viability, the integrity of the mesothelial barrier and secretion of pro-inflammatory cytokines was evaluated on human mesothelial cells grown on cell culture inserts and exposed to the PD solution only at the apical side, mimicking the condition of a PD dwell. The results were compared to those obtained after exposure to a panel of dialytic solutions commonly used in clinical practice. We report here compelling evidence that this novel formulation shows better performance in terms of higher cell viability, better preservation of the integrity of the mesothelial layer and reduced release of pro-inflammatory cytokines. This new formulation could represent a step forward towards obtaining PD solutions with high biocompatibility.     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Investigation the effect of FeNiCoCrMo HEA addition on properties of B4C ceramic prepared by spark plasma sintering

In this study, monolithic B₄C and B₄C-based ceramics incorporating FeNiCoCrMo dual-phase (FCC and BCC) high entropy alloys (HEAs) were produced by spark plasma sintering (SPS). The effect of additives on the densification behavior, mechanical properties, microstructures, and phase evaluation of the samples were investigated. X-ray analysis confirmed the existence of FCC structured HEA and depletion of BCC structured HEA, after high-temperature reaction between B₄C-HEAs. The addition of HEAs enhanced the densification behavior by liquid phase sintering. Furthermore, hardness and fracture toughness values of the samples increased with increasing HEAs content. Fracture toughness and hardness values for all composites were higher than the monolithic B₄C. A combination of the highest density (∼99.22 %) and the best mechanical properties (32.3 GPa hardness and 4.53 MPa m^1/2 fracture toughness) was achieved with 2.00 vol.% HEA addition.     

Entropy analysis of unsteady MHD three-dimensional flow of Williamson nanofluid over a convectively heated stretching sheet

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well-known scheme, namely the shooting method along with the Runge-Kutta-Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non-Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.