Journal of Materials Science最新文献

To explore the application of high-entropy alloys in the field of additive manufacturing, a non-equimolar FeCrNiMnCuSi HEAs was prepared using wire arc additive manufacturing technology. The effects of different cladding currents on the microstructure, microhardness, tensile properties, tribological properties, and corrosion resistance of the HEAs bulk were investigated. The experimental results showed that the prepared HEAs bulk was composed of FCC phases with the columnar crystals. As the cladding current increased, the average grain size of the HEA increased from 41.9 μm at 160 A to 68.5 μm at 220 A, the average hardness decreased, and the tensile strength exhibited a trend of first increasing and then decreasing. At a cladding current of 180 A, the tensile properties were optimal, with a tensile strength of 746.88 MPa and an elongation of 46.85% in the build direction, and a tensile strength of 768.53 MPa with an elongation of 38.12% in the transverse direction. As the cladding current increased, the wear rate of the HEAs initially decreased and then increased. The lowest wear rate reaching 4.36 × 10⁻⁵ mm³·N⁻¹·m⁻¹ was observed at a cladding current of 180 A. The wear mechanism of the 180 A HEAs was mainly adhesive wear and oxidative wear. Additionally, as the cladding current increased, the corrosion resistance of the HEAs improved. This improvement was primarily due to the higher cladding current inhibiting the segregation of Cu at the grain boundaries, thereby preventing uneven corrosion behavior.

Atomistic modeling using first-principles density functional theory was employed to investigate the early-stage oxidation and passivity of CoCrFeNi high-entropy alloy (HEA). Our findings reveal increased adsorption energy and charge transfer for sites with higher Cr density, indicating preferential Cr oxidation. The work function of the HEA increases with adsorbate coverage due to changes in the electrostatic dipole moment between HEA and adsorbates. Diffusion activation energies showed no correlation with local atomic configurations but were accurately predicted using an effective spatial-energy parameter. Density of states and d-band center calculations indicated a shift toward the Fermi level, suggesting enhanced surface reactivity, particularly for Cr. The HEA surface demonstrated high oxygen reactivity at high temperatures and low pressures, making recovery of the clean surface impossible. These insights facilitated the calculation of the oxygen diffusion coefficient, advancing the understanding of oxide formation and stability in CoCrFeNi HEA.

In this study, the weights of SiC (silicon carbide) nanoparticle-filled and unfilled glass fiber reinforced polymer matrix composites (PMC) after artificial aging were estimated using an artificial neural network (ANN) model. Composite samples with different SiC nanoparticle weight fractions (0%, 0.5%, 1%, 1.5%, 2%) were produced by vacuum infusion method and subjected to artificial aging at 70 ºC and 85% relative humidity for 0, 250, 500, 750, 1000, 1250, and 1500 h. The weights of the samples were measured and recorded periodically during the aging process. The developed ANN model was trained to estimate the sample weight using SiC nanoparticle weight fraction and aging time as input parameters. The network with four neurons in a single hidden layer was trained with the Levenberg–Marquardt feedforward backpropagation algorithm, and a total of 35 datasets were used for training, testing, and validation. The weights predicted by the model overlapped with the experimentally obtained data with high accuracy. The mean square error (MSE) value calculated to evaluate the accuracy and adequacy of the model was determined as 0.001225 in the 256th iteration. It was concluded that the trained artificial neural network model was able to predict the weights of SiC nanoparticle-filled and unfilled glass fiber reinforced PMCs with high accuracy and efficiency.

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Conducting polymers (CPs) combine the electric charge conduction properties of metals with polymers’ advantages. Among CPs, polyaniline (PANI) is unique for its characteristics and physico-chemical properties. PANI traditional synthesis, based on the oxidative polymerization of aniline by strong inorganic oxidant, is still the most employed, albeit it leads to a large amount of toxic and carcinogenic waste. This approach has become less practicable in the last years due to stricter rules on environmental protection and pollution limits. Therefore, the possibility of using more environmentally friendly oxidants and alternative reaction mechanisms, which avoid the production of toxic by-products, represents an attractive goal. Based on these aspects, a new synthetic method has been developed in the last years, starting from more sustainable reagents (N-phenyl-p-phenylenediamine and molecular oxygen or hydrogen peroxide), demonstrating improved biocompatibility of the obtained polymer. However, PANI from aniline (PANI1) and that from N-phenyl-p-phenylenediamine (PANI2) differ, particularly in terms of morphology, porosity (porous PANI1 and compact PANI2), and conductivity (higher for PANI1). Since it is not clear which parameters are mainly affecting the final properties of PANI2, the goal of the present work is investigating the mechanisms involved in the synthesis of the two materials to modulate and enhance the final properties of PANI2, making it a sustainable alternative to traditional PANI1. Finally, for the first time, a comparative life cycle assessment (LCA) study was conducted on PANI synthesis to compare the traditional method (PANI1) and the “green” one (PANI2) to determine whether the latter truly reduces the environmental impact.

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Tumoral cell apoptosis and necroptosis could be accelerated by further amplified intracellular oxidative stress, which could be enhanced through the ferrous-based intracellular Fenton reaction. Shikonin, a necroptosis inducer and anticancer chemotherapeutic drug, has been reported to be able to produce the intracellular reactive species (ROS) in several types of tumor cells, facilitating oxidative stress increase by coordination with ferrous. A functional complex (Shik-Fe(II)) served not only as the catalyst of Fenton reaction but also as the ROS producer was prepared in this work. However, the formulated Shik-Fe(II) exhibited the poor water solubility alongside with the fast clearance rate, resulting in the limited application. Herein, Shik-Fe(II) was encapsulated in the stealth liposomal carrier to overcome the poor water solubility of Shik-Fe(II) and form the functional lipid nanomedicine Shik-Fe(II)@Lip. After the internalization of Shik-Fe(II)@Lip by neuroblastoma cells, the intracellular oxidative stress could be dramatically enhanced by the increased level of ROS as well as the cytotoxic hydroxyl radicals (·OH) generating from Fe(II)-mediated Fenton reaction, ultimately resulting in the advanced therapeutic effect. Thus, this work presents the preparation of functional Shik-Fe(II)@Lip as an effective anticancer formulation with both chemotherapeutic effect and the remarkable intracellular oxidative stress amplification capability for clinical application.

From fighting corrosion to illuminating cancer cells, can one material truly do it all? Carbon dots (CDs), with their unique properties, offer a promising answer to the escalating global challenges of environmental pollution and infrastructure degradation. Surface passivation, achieved through doping, is crucial in enhancing CD stability and photoluminescence. Doping with heteroatoms and metal ions modifies CD surface chemistry and electronic properties, resulting in improved fluorescence, stability, and anti-corrosion performance. This comprehensive review explores various doping techniques and their impact on CD properties, highlighting their diverse applications in anti-corrosion coatings, solar cells, bioimaging, sensing, and antibacterial agents. The review also addresses future perspectives and challenges in doped CD research, emphasizing the need for innovative doping strategies, scalable production methods, standardization, and successful device integration. The strategic manipulation of CD properties through doping will undoubtedly play a pivotal role in shaping the future of nanomaterial science and engineering, paving the way for innovative solutions to global challenges.

In the conventional quenching and partitioning (Q&P) process, the so-called film-like austenite, which is generally fabricated by decreasing the quenching temperatures, oftentimes contains a high C content of up to 1.2 wt.%, which is unfavorable for improving the ductility. Here, we combine the quenching with isochronal partitioning (Q&IP) to obtain the newly Q&IP steel, featuring the film-like austenite with a slightly reduced C content of 0.98 wt.%. As compared with the traditional Q&P steel fabricated by quenching and isothermal partitioning, the Q&IP steel exhibits higher yield and ultimate tensile strengths (1168 ± 8.7 MPa and 1722 ± 10.2 MPa, respectively) and good ductility (with a uniform elongation of 13.2 ± 0.2%), due to the combination of the enhanced dislocation plasticity, the higher back stress hardening, and the durable transformation-induced plasticity effect. Applying a thermo-kinetic theory of generalized stability, we further demonstrate that the increased strength and good plasticity in the Q&IP steel come from the phase transformations with high thermodynamic driving forces and high generalized stability.

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The improvement of nickel-based superalloys' mechanical properties can be achieved by controlling the grain boundary structure, particularly the twin boundary. Understanding the role of the twin boundary in deformation and mechanical properties can develop grain boundary engineering strategies to increase the mechanical properties of nickel-based superalloys. This paper examines the effects of three different orientations of the twin boundary to the loading direction, i.e., parallel, inclined, and perpendicular under uniaxial tensile loading, on the mechanical properties and mechanisms of slip and dislocation generation. A molecular dynamics model is used to simulate the behavior of the nickel-based superalloy. The results indicate that the yield stress of the nickel-based superalloy is improved by the twin boundary perpendicular to the loading direction. In contrast, parallel and inclined twin boundaries weaken the material's yield stress. The yield stresses are 12.97, 11.45, 10.33, and 13.65 GPa, respectively, for the sample without a twin boundary, containing a twin boundary parallel, inclined, and perpendicular to the loading direction. The results demonstrate that when the twin boundary is parallel or perpendicular to the loading direction, the slip planes are inclined to the twin boundary. However, when the twin boundary is inclined to the loading direction, the dislocations mainly slip gradually parallel to the twin boundary. Also, the twin boundary changes the material's toughness. The toughness values for samples without the twin boundary and with the twin boundary parallel, inclined, and perpendicular to the loading direction are 0.134, 0.139, 0.292, and 0.06 GPa, respectively. Also, the creation and growth of the crack due to the ultimate stress occurs at points in the phase interface for the parallel twin boundary and in the twin boundary for the inclined and perpendicular twin boundaries.

This study uses machine learning (ML) to simplify the complex and time-consuming process of predicting the hardness of high-entropy alloys (HEAs). A stacking regression model combined with a Transformed Target Regressor (TTR) is proposed, utilizing three top-performing base models such as support vector regression (SVR), LightGBM (LGBM), and random forest (RF). The model incorporates 20 key thermodynamic, mismatch, and combination parameters (physical features) along with 18 different elements to enhance generalization and account for various input feature effects, specifically to predict the hardness of HEAs. Feature selection was done in two stages using the Pearson correlation coefficient (P_c) and conditional mutual information-based feature selection (CMIFS) methods. The impact of alloy composition and physical features on hardness was analyzed with SHapley Additive exPlanations (SHAP) values and partial dependence plots (PDPs), helping to better understand the model’s predictions. The stacked model outperformed the individual models, achieving an overall R² score of 0.88 and 0.99 for composition and physical features-based data, respectively. Additionally, the non-dominated sorting genetic algorithm II (NSGA-II) was used to optimize the hardness of the HEAs, resulting in a more than 24% increase in hardness compared to the initial data. The optimized composition of Al_17.24Fe_24.79Cr_1.95Mo_6.84 Ti_13.03 Nb_7.89 Hf_8.26 was identified as having the highest hardness. This ML workflow serves as a general framework to optimize alloy chemical spaces and input features to achieve desired properties. Overall, this model provides interpretability and generalization through ensemble learning, offering insights for designing high hardness HEAs.

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