Search Results (1,662)

This study investigates the microstructural and mechanical evolution of Ti6Al4V alloy surfaces modified through laser surface alloying (LSA) with antimicrobial elements silver (Ag) and copper (Cu) to enhance surface performance for biomedical applications. The as-received Ti6Al4V exhibited a typical equiaxed $\alpha \text{-}\beta$ microstructure with baseline hardness. Following LSA treatment using a 1000 W pulsed laser, distinct transformations were observed in the melt zone (MZ) and heat-affected zone (HAZ), influenced by the specific alloying element. Ag incorporation led to the development of ultrafine acicular martensitic structures and a higher fraction of high-angle grain boundaries, resulting in moderate hardness improvement. In contrast, Cu alloying promoted the formation of Ti₂Cu intermetallic phases, dendritic morphologies, and pronounced solute segregation, leading to a more significant increase in hardness. Electron Backscatter Diffraction(EBSD) and Energy Dispersive Spectroscopy (EDS) analyses revealed grain refinement, texture evolution, and elemental redistribution across the modified regions, while X-ray Diffraction XRD confirmed the presence of new phases. The comparative analysis highlights that although both Ag and Cu improve microstructural complexity and hardness, Cu-modified zones exhibited higher hardness values than Ag-modified zones, suggesting a stronger surface strengthening effect under the tested conditions. These findings contribute valuable insights into the structure–property relationships of LSA-modified Ti alloys, supporting their potential for durable and antimicrobial biomedical implants. Full article

This study investigates the use of the complex reductant FeAlSiCa as an alternative to the conventional FeSiCr in the EAF smelting of FeCr. The smelting process using FeAlSiCa is characterized by a stable furnace operation, active discharge of metal and slag, and effective phase separation. It was found that a 20% excess of FeAlSiCa over the stoichiometric requirement leads to a sharp increase in Si content in the FeCr alloy, with approximately 85% Cr recovery into the metal. A stoichiometric amount of FeAlSiCa results in a metal with 1.5–1.6% Si content and about 80% Cr recovery. A comparable Cr recovery using FeSiCr was achieved only when applying a 20% excess of that reductant. The use of FeAlSiCa also holds promise for technological sustainability due to its low production cost and the utilization of waste materials during its synthesis. The resulting slags are solid and rock-like and show no signs of disintegration after storage for more than 45 days. Full article

Chemiresistive gas sensors are extensively employed in environmental monitoring, disease diagnostics, and industrial safety due to their high sensitivity, low cost, and miniaturization. However, the high cross-sensitivity and poor selectivity of gas sensors limit their practical applications in complex environmental detection. In particular, the mechanisms underlying the selective response of certain chemiresistive materials to specific gases are not yet fully understood. In this review, we systematically discuss material design strategies and system integration techniques for enhancing the selectivity and sensitivity of gas sensors. The focus of material design primarily on the modification and optimization of advanced functional materials, including semiconductor metal oxides (SMOs), metallic/alloy systems, conjugated polymers (CPs), and two-dimensional nanomaterials. This study offers a comprehensive investigation into the underlying mechanisms for enhancing the gas sensing performance through oxygen vacancy modulation, single-atom catalysis, and heterojunction engineering. Furthermore, we explore the potential of emerging technologies, such as bionics and artificial intelligence, to synergistically integrate with functional sensitive materials, thereby achieving a significant enhancement in the selectivity of gas sensors. This review concludes by offering recommendations aimed at improving the selectivity of gas sensors, along with suggesting potential avenues for future research and development. Full article

To investigate the failure behavior of Ti6Al4V alloy under complex stress states, this study designed tensile specimens with different notches to achieve high, medium, and low stress triaxiality conditions. By adjusting the width of the notch spacing of the specimens, the failure mode can be transformed from tension-dominated fracture to shear stress-dominated fracture, which enables further examination of the damage model’s effectiveness. A shear-modified Gurson–Tvergaard–Needleman (GTN) model was employed to predict the failure behavior under various stress states. For calibrating the GTN parameters, a machine learning approach was adopted. Back propagation (BP) neural networks were used to construct surrogate models for predicting the fracture strains of three typical specimens, and genetic algorithms (GAs) were integrated for optimization, to minimize the discrepancy in fracture strains between experimental results and finite element analysis (FEA). Finally, an optimal set of parameters was determined. This set of parameters can effectively predict the failure behavior of all specimens, including not only the stress–strain curves, but also the failure modes (fracture locations). Full article

As emergent material candidates for extreme environments, refractory high-entropy alloys (HEAs) or refractory multi-principal-element alloys (RMPEAs) comprising refractory metals feature qualities such as high radiation tolerance, corrosion resistance, and mechanical strength. A set of MoNbTi-based RMPEA samples with Al, Cr, V, and Zr additions are prepared by spark plasma sintering and investigated for their response to irradiation using 10 MeV Si⁺ ions with a dose of $1.43\times {10}^{15}$ ions/cm². Positron annihilation spectroscopy and transmission electron microscopy are employed as atomic- and meso- scale techniques to reveal how chemical complexity, nanotwinning, and phase fractions play an important role in radiation-induced defect accumulation and damage tolerance. The study provides experimental evidence of nanotwinning acting as an effective sink for radiation-induced point defects. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

Corner radius end mills (CREMs) are widely used in machining due to their unique tool geometry, which improves surface quality. Variations in cutting force during machining significantly impact machining quality. Therefore, precisely predicting cutting forces is critical for controlling machining chatter and enhancing accuracy. Traditional element force models have complex formulas and high computational demands when considering tool runout. This paper proposes a hybrid prediction model for CREMs that integrates the separate-edge-forecast method and the BP neural network. The integration approach incorporates runout effects into cutting force coefficients and addresses nonlinear effects from runout. The accuracy of the cutting force prediction model was validated through side milling on 7075 aluminum alloy. The results indicate that the maximum error between the predicted and measured forces is 9.43%, demonstrating that this model ensures high prediction accuracy while reducing computation cost. Full article

The BN-350 is the first industrial fast neutron reactor in the history of nuclear energy. It is currently undergoing decommissioning. One of the key challenges of decommissioning is managing the solid radioactive waste that has accumulated throughout the reactor’s operational life. At the moment, the accumulated solid radioactive waste is stored in a storage facility within the BN-350 reactor complex. An analysis showed that more than ~7262 tons with 5.17 × 10¹⁴ Bq activity of various types of solid radioactive waste have been accumulated over the reactor operation. They are mainly represented by materials with low activity. At the same time, the main share of activity is comprised of highly active waste with a total mass of ~170 tons and an activity of 4.73 × 10¹⁴ Bq. A solid radioactive waste management strategy has been developed. It includes all stages from collection and classification to transportation and long-term storage. Modern technologies now offer new possibilities. Some radioactive waste can be processed and reused in other economic sectors. In particular, recycling metals and alloys can reduce the volume of solid radioactive waste. It can also return valuable materials to industrial use. Full article

Fatigue failure of overhead line conductors made of AlMgSi alloys is much more complex than fatigue failure of a single wire. The main difference lies in the fretting phenomenon, which is a significant mechanism initiating fatigue damage. It is generated because of micro-movements between individual wires or outer wires and overhead line fittings. Such movements are mainly caused by aeolian vibrations, which lead to degradation of wire surface, initiation of microcracks, and premature failure of multiple wires. Research based on laboratory experiments and modeling studies simulating real operating conditions made it possible not only to identify the mechanisms leading to failure but also to assess the impact of working conditions on their evolution. According to the obtained results, properly selected heat treatment parameters influence both the mass decrease of the wires and number of cycles to failure due to fretting fatigue. Further development of materials, protective coatings, and methods of durability prediction will reduce the impact of fretting on fatigue failure and thus increase the reliability of power lines. Full article

Nanoporous gold nanoparticles (np-AuNPs) combine inertness, a nanoscale structure, and a porous framework with high surface area, conductivity, and biocompatibility, making them ideal for biosensing, catalysis, fuel cells, and drug delivery. Their open pore structure and low-coordinated atoms enhance biomolecule capture and mass transfer, while their tunable size, pore volume, and ease of surface modification make them promising biosensor transducers. However, synthesizing colloidal np-AuNPs in a simple way with controllable size and scalability remains challenging. The existing approaches mostly rely on specialized equipment, complex setups, and expert knowledge, while still facing challenges in terms of scalability. In this study, we present a simple, seedless, wet-chemical synthesis of colloidal np-AuNPs via the co-reduction of Au/Ag alloys followed by dealloying. By adjusting the Au:Ag ratio, we produced np-AuNPs sized ~120–530 nm, which were immobilized on electrodes for detecting lipopolysaccharide (LPS), a toxic component of Gram-negative bacterial membranes. The LPS biosensor exhibited excellent sensitivity towards detecting wild-type LPS, with a low limit of detection (LOD) of 0.1244 ng/L. This work demonstrates the effective synthesis and application of np-AuNPs in LPS biosensing. Full article

Complex concentrated alloys (CCAs) have attracted significant attention due to their potential to develop materials with enhanced properties, such as increased hardness and strength. These properties are strongly influenced by the chemical composition and the processing method used. Body-centred cubic (BCC) structures are known to have high hardness but low fracture toughness, whereas face-centred cubic (FCC) structures typically exhibit lower hardness but higher toughness. In this study, Al-Cr-Cu-Fe-Mn-Ni CCAs with three distinct compositions were produced using pressureless sintering. One set of samples was prepared with equiatomic composition (composition E), whereas the compositions of the other two sets were defined based on thermodynamic calculations to obtain sintered samples predominantly formed by BCC (composition B) or FCC (composition F) phases. The samples were characterized using X-ray diffraction, scanning and transmission electron microscopy, energy-dispersive X-ray spectroscopy, electron backscatter diffraction, density measurements, hardness measurements, and uniaxial compression tests. For all compositions, good agreement was obtained between the phases predicted by thermodynamic calculations and those experimentally detected. In addition, significant differences in the mechanical properties were observed between samples with each composition. The samples with composition B exhibited the highest hardness, but almost no ductility. In contrast, samples with composition F showed the lowest yield strength and hardness, but the highest ductility. Samples with composition E had intermediate values between those of samples B and F. These differences were attributed to differences in the proportions and properties of the BCC and FCC phases in each composition and demonstrate that the mechanical properties of Al-Cr-Cu-Fe-Mn-Ni CCAs can be tailored using compositions defined based on thermodynamic calculations. Full article

High-entropy alloys (HEAs) have shown great promise for applications in extreme service environments due to their exceptional mechanical properties and thermal stability. However, traditional alloy design often struggles to balance multiple properties such as strength and ductility. Constructing heterogeneous microstructures has emerged as an effective strategy to overcome this challenge. With the rapid advancement of additive manufacturing (AM) technologies, their unique ability to fabricate complex, spatially controlled, and non-equilibrium microstructures offers unprecedented opportunities for tailoring heterostructures in HEAs with high precision. This review highlights recent progress in utilizing AM to engineer heterogeneous microstructures in high-performance HEAs. It systematically examines the multiscale heterogeneities induced by the thermal cycling effects inherent to AM techniques such as selective laser melting (SLM) and electron beam melting (EBM). The review further discusses the critical role of these heterostructures in enhancing the synergy between strength and ductility, as well as improving work-hardening behavior. AM enables the design-driven fabrication of tailored microstructures, signaling a shift from traditional “performance-driven” alloy design paradigms toward a new model centered on “microstructural control”. In summary, additive manufacturing provides an ideal platform for constructing heterogeneous HEAs and holds significant promise for advancing high-performance alloy systems. Its integration into alloy design represents both a valuable theoretical framework and a practical pathway for developing next-generation structural materials with multiple performance attributes. Full article

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels. Full article

A key goal when managing copper alloy heritage is preventing “bronze disease,” which damages surface detail and may disintegrate objects by oxidation and hydrolysis of nantokite (CuCl), forming voluminous copper trihydroxychlorides (Cu₂(OH)₃Cl). The success of mitigation strategies is difficult to evaluate due to the complexity of copper alloy corrosion profiles, limitations in non-destructive analytical methods and incomplete understanding of the corrosion mechanisms and reactions involved in bronze disease. Without better understanding, it is impossible to design truly effective solutions for the safe storage and display of archaeological copper alloys. Advancing current understanding, this paper examines oxidation and hydrolysis of CuCl using oxygen consumption, Fourier transform infrared spectroscopy and Raman spectroscopy, recognised as the basis of bronze disease. Variables potentially affecting bronze disease processes are evaluated, including relative humidity (RH) (15–80%RH at 20 °C) and the presence of metallic copper with CuCl and their respective ratios. Results confirm that these variables influence the reaction mechanisms and kinetics of bronze disease. The rate of oxidation and hydrolysis of CuCl accelerates with RH, and its effect is quantified. The presence of copper is shown to be important for producing bronze disease; it facilitates a cyclic reaction forming Cu₂(OH)₃Cl, increases its formation rate at lower RH than by hydrolysis of CuCl alone and prevents formation of soluble chloride compounds. The formation of Cu₂(OH)₃Cl without counteracting copper ions is shown to promote formation of CuCl₂ and CuCl₂·2H₂O, accelerating bronze disease. This new understanding is used to better quantify risk of bronze disease as a function of RH, providing a more quantitative tool for managing preservation of archaeological copper alloy collections. Full article

The aim of this study was to evaluate the prospects of using natural fucosylated chondroitin sulfate (FCS) from the sea cucumber Cucumaria japonica as the active component of an implantable biodegradable scaffold to stimulate hematopoiesis in mice with cyclophosphamide (CPh)-induced myelosuppression. The scaffolds were based on bioresorbable Fe–Mn–C and Fe–Mn–Pd alloys after equal-channel angular pressing (ECAP). The efficiency of the developed constructs with FCS was compared with the activity of the same scaffolds loaded with recombinant human granulocyte colony stimulating factor, as well as solutions of these active compounds administered subcutaneously after the end of the cyclophosphamide (CPh) course. It was found that implantation of the Fe–Mn–C scaffold loaded with FCS most effectively stimulated hematopoiesis, providing a complex effect. This design of the developed constructs contributed to an increase in the concentration not only of leukocytes and neutrophils, but also platelets in the blood, promoted the proliferation of bone marrow cells, increasing the concentration of Ki-67(+) cells, and contributed to the restoration of the morphology of the animals’ spleen. Full article