Search Results (18)

Single-crystal 4H silicon carbide (4H-SiC) is a key substrate material for third-generation semiconductor devices, where surface and subsurface integrity critically affect performance and reliability. This study systematically examined the evolution of surface morphology and subsurface damage (SSD) during nanoscratching of 4H-SiC under varying normal loads (0–100 mN) using a nanoindenter equipped with a diamond Berkovich tip. Scratch characteristics were assessed using scanning electron microscopy (SEM), while cross-sectional SSD was characterised via focused ion beam (FIB) slicing and transmission electron microscopy (TEM). The results revealed three distinct material removal regimes: ductile removal below 14.5 mN, a brittle-to-ductile transition between 14.5–59.3 mN, and brittle removal above 59.3 mN. Notably, substantial subsurface damage—including median cracks exceeding 4 μm and dislocation clusters—was observed even within the transition zone where the surface appeared smooth. A thin amorphous layer at the indenter-substrate interface suppressed immediate surface defects but promoted subsurface damage nucleation. Crack propagation followed slip lines or their intersections, demonstrating sensitivity to local stress states. These findings offer important insights into nanoscale damage mechanisms, which are essential for optimizing precision machining processes to minimise SSD in SiC substrates. Full article

Investigating failure mechanisms in cutting tools used in advanced industries like biomedical and aerospace, which operate under extreme mechanical and chemical conditions, is essential to prevent failures, optimize performance, and minimize financial losses. The diamond-turning process, operating at micrometer-length scales, forms a tightly bonded built-up edge (BUE). The tribochemical interactions between a single-crystal diamond and its deformed chip induce inter-diffusion and contact, rapidly degrading the cutting edge upon BUE fracture. These effects intensify at higher deformation speeds, contributing to the observed rapid wear of diamond tools during d-shell-rich metal machining in industrial settings. In this study, these interactions were studied with niobium (Nb) as the transition metal. Tribochemical effects were observed at low deformation speeds (quasistatic; <1 mm/s), where thermal effects were negligible under in situ conditions inside the FEI /SEM vacuum chamber room. The configuration of the interface region of diamond and transition metals was characterized and analyzed using focused ion beam (FIB) milling and subsequently characterized through transmission electron microscopy (TEM). The corresponding inter-diffusion was examined by elucidating the phase evolution, element concentration profiles, and microstructure evolution via high-resolution TEM/Images equipped with an TEM/EDS system for elemental characterization. Full article

The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems. Full article

The creep behavior of Ultra-High-Performance Concrete (UHPC) was investigated by machine learning (ML) and SHapley Additive exPlanations (SHAP). Important features were selected by feature importance analysis, including water-to-binder ratio, aggregate-to-cement ratio, compressive strength at loading age, elastic modulus at loading age, loading duration, steel fiber volume content, and curing temperature. Four typical ML models—Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting Machine (XGBoost), and Light Gradient Boosting Machine (LGBM)—were studied to predict the creep behavior of UHPC. Via Bayesian optimization and 5-fold cross-validation, the ML models were tuned to achieve high accuracy (R² = 0.9847, 0.9627, 0.9898, and 0.9933 for RF, ANN, XGBoost, and LGBM, respectively). The contribution of different features to the creep behavior was ranked. Additionally, SHAP was utilized to interpret the predictions by the ML models, and four parameters stood out as the most influential for the creep coefficient: loading duration, curing temperature, compressive strength at loading age, and water-to-binder ratio. The SHAP results were consistent with theoretical understanding. Finally, the UHPC creep curves for three different cases were plotted based on the ML model developed, and the prediction by the ML model was more accurate than that by fib Model Code 2010. Full article

Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases. Full article

Metabolic-dysfunction-associated steatotic liver disease (MASLD) and metabolic syndrome (MetS) are inextricably linked conditions, both of which are experiencing an upward trend in prevalence, thereby exerting a substantial clinical and economic burden. The presence of MetS should prompt the search for metabolic-associated liver disease. Liver fibrosis is the main predictor of liver-related morbidity and mortality. Non-invasive tests (NIT) such as the Fibrosis-4 index (FIB4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), hepatic steatosis index (HIS), transient elastography (TE), and combined scores (AGILE3+, AGILE4) facilitate the detection of liver fibrosis or steatosis. Our study enrolled 217 patients with suspected MASLD, 109 of whom were diagnosed with MetS. We implemented clinical and biological evaluations complemented by transient elastography (TE) to discern the most robust predictors for liver disease manifestation patterns. Patients with MetS had significantly higher values of FIB4, APRI, HSI, liver stiffness, and steatosis parameters measured by TE, as well as AGILE3+ and AGILE4 scores. Machine-learning algorithms enhanced our evaluation. A two-step cluster algorithm yielded three clusters with reliable model quality. Cluster 1 contained patients without significant fibrosis or steatosis, while clusters 2 and 3 showed a higher prevalence of significant liver fibrosis or at least moderate steatosis as measured by TE. A decision tree algorithm identified age, BMI, liver enzyme levels, and metabolic syndrome characteristics as significant factors in predicting cluster membership with an overall accuracy of 89.4%. Combining NITs improves the accuracy of detecting patterns of liver involvement in patients with suspected MASLD. Full article

Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final reconstruction. To demonstrate the versatility of the technique, a comprehensive list of applications is also summarized, ranging from batteries to shale rocks and even some types of soft materials. Moreover, the continuous technological development, such as the introduction of the latest models of plasma and cryo-FIB, can open the way towards the analysis with this technique of a large class of soft materials, while the introduction of new machine learning and deep learning systems will not only improve the resolution and the quality of the final data, but also expand the degree of automation and efficiency in the dataset handling. These future developments, combined with a technique that is already reliable and widely used in various fields of research, are certain to become a routine tool in electron microscopy and material characterization. Full article

Due to elevated temperatures and high vacuum levels in electron beam melting (EBM), spatter formation and accumulation in the feedstock powder, and sublimation of alloying elements from the base feedstock powder can affect the feedstock powder’s reusability and change the alloy composition of fabricated parts. This study focused on the experimental and thermodynamic analysis of spatter particles generated in EBM, and analyzed sublimating alloying elements from Alloy 718 during EBM. Heat shields obtained after processing Alloy 718 in an Arcam A2X plus machine were analyzed to evaluate the spatters and metal condensate. Comprehensive morphological, microstructural, and chemical analyses were performed using scanning electron microscopy (SEM), focused ion beam (FIB), and energy dispersive spectroscopy (EDS). The morphological analysis showed that the area coverage of heat shields by spatter increased from top (<1%) to bottom (>25%), indicating that the spatter particles had projectile trajectories. Similarly, the metal condensate had a higher thickness of ~50 μm toward the bottom of the heat shield, indicating more significant condensation of metal vapors at the bottom. Microstructural analysis of spatters highlighted that the surfaces of spatter particles sampled from the heat shields were also covered with condensate, and the thickness of the deposited condensate depended on the time of landing of spatter particles on the heat shield during the build. The chemical analysis showed that the spatter particles had 17-fold higher oxygen content than virgin powder used in the build. Analysis of the metalized layer indicated that it was formed by oxidized metal condensate and was significantly enriched with Cr due to its higher vapor pressure under EBM conditions. Full article

The structural, physical–chemical, and micromechanical characteristics of Al_0.6Ti_0.4N coatings deposited by different physical vapor deposition (PVD) methods, such as cathodic arc deposition (CAD), as well as advanced HiPIMS techniques were investigated in terms of their cutting performance during the machining of an Inconel 718 alloy. XRD studies had revealed that the HiPIMS coating featured lower residual stresses and more fine-grained structure. Electrochemical characterization with the potentiostat-impendence method shows that the HiPIMS coating has a significantly lower porosity than CAD. SEM and AFM studies of the surface morphology demonstrate that the HiPIMS coating has a smoother surface and an absence of droplet phases, in contrast with CAD. XRD, combined with FIB/TEM studies, shows a difference in the crystal structure of both coatings. The micromechanical characteristics of each coating, such as hardness, elastic modulus, fracture toughness, and adhesion to the substrate, were evaluated. The HiPIMS coating was found to possess a more beneficial combination of micromechanical properties compared to CAD. The beneficial characteristics of the HiPIMS coating alleviated the damage of the coated layer under operation. Combined with grain size refinement, this results in the improved adaptive performance of the HiPIMS coating through the formation of a greater amount of thermal barrier sapphire tribo-films on the friction surface. All of these characteristics contribute to the reduction of flank and crater wear intensity, as well as notching, leading to an improvement of the HiPIMS coating’s tool life. Full article

Most accelerometers today are based on the capacitive principle. However, further miniaturization for micro integration of those sensors leads to a poorer signal-to-noise ratio due to a small total area of the capacitor plates. Thus, other transducer principles should be taken into account to develop smaller sensors. This paper presents the development and realization of a miniaturized accelerometer based on the tunneling effect, whereas its highly sensitive effect regarding the tunneling distance is used to detect small deflections in the range of sub-nm. The spring-mass-system is manufactured by a surface micro-machining foundry process. The area of the shown polysilicon (PolySi) sensor structures has a size smaller than 100 µm × 50 µm (L × W). The tunneling electrodes are placed and patterned by a focused ion beam (FIB) and gas injection system (GIS) with MeCpPtMe₃ as a precursor. A dual-beam system enables maximum flexibility for post-processing of the spring-mass-system and patterning of sharp tips with radii in the range of a few nm and initial distances between the electrodes of about 30–300 nm. The use of metal–organic precursor material platinum carbon (PtC) limits the tunneling currents to about 150 pA due to the high inherent resistance. The measuring range is set to 20 g. The sensitivity of the sensor signal, which depends exponentially on the electrode distance due to the tunneling effect, ranges from 0.4 pA/g at 0 g in the sensor operational point up to 20.9 pA/g at 20 g. The acceleration-equivalent thermal noise amplitude is calculated to be 2.4–3.4 mg/$\sqrt{\mathrm{H}\mathrm{z}}$. Electrostatic actuators are used to lead the electrodes in distances where direct quantum tunneling occurs. Full article

Microfluidic devices offer the potential to automate a wide variety of chemical and biological operations that are applicable for diagnostic and therapeutic operations with higher efficiency as well as higher repeatability and reproducibility. Polymer based microfluidic devices offer particular advantages including those of cost and biocompatibility. Here, we describe direct and replication approaches for manufacturing of polymer microfluidic devices. Replications approaches require fabrication of mould or master and we describe different methods of mould manufacture, including mechanical (micro-cutting; ultrasonic machining), energy-assisted methods (electrodischarge machining, micro-electrochemical machining, laser ablation, electron beam machining, focused ion beam (FIB) machining), traditional micro-electromechanical systems (MEMS) processes, as well as mould fabrication approaches for curved surfaces. The approaches for microfluidic device fabrications are described in terms of low volume production (casting, lamination, laser ablation, 3D printing) and high-volume production (hot embossing, injection moulding, and film or sheet operations). Full article

The aim of this study is to investigate a characteristic deformation behavior of a precipitation strengthening-type Cu-Ni-Si alloy (Cu-2.4Ni-0.51Si-9.3Zn-0.15Sn-0.13Mg) by microcompression specimens. Three micropillars with a square cross-section of 20 × 20 × 40 μm³ were fabricated by focused ion beam (FIB) micromachining apparatus and tested by a machine specially designed for microsized specimens. The three pillars were deformed complicatedly and showed different yield strengths depending on the crystal orientation. The micromechanical tests revealed work hardening by the precipitation clearly. Electron backscattered diffraction analysis of a deformed specimen showed a gradual rotation of grain axis at the grain boundaries after the compression test. Full article

The aim of this study was to compare the quality of different computer-assisted-design and computer assisted manufacturing systems (CAD-CAM) generated by only one scanner, focusing on vertical fit discrepancies and the mechanical properties. A master model was obtained from a real clinical situation: the replacement of an absent (pontic) tooth, with the construction of a fixed partial denture on natural abutments with three elements. Nine scans were performed by each tested and 36 copies were designed using a dental CAD-CAM software (Exocad). The frameworks were manufactured using three-axis and five-axis, with the same batch of the chrome-cobalt (CrCo) alloy. The frameworks were not cemented. A focus ion beam-high resolution scanning electron microscope (FIB-HRSEM) allowed us to obtain the vertical gap measurements in five points for each specimen. Roughness parameters were measured using white light interferometry (WLI). The samples were mechanically characterized by means of flexural tests. A servo-hydraulic testing machine was used with a cross-head rate of 1 mm/min. One-way ANOVA statistical analysis was performed to determine whether the vertical discrepancies and mechanical properties were significantly different between each group (significance level p < 0.05). The overall mean marginal gap values ranged: from 92.38 ± 19.24 µm to 19.46 ± 10.20 µm, for the samples produced by three-axis and five-axis machines, respectively. Roughness was lower in the five-axis machine than the three-axis one, and as a consequence, the surface quality was better when the five-axis machine was used. These results revealed a statistically significant difference (p < 0.005) in the mean marginal gap between the CAD-CAM systems studied. The flexural strength for these restorations range from 6500 to 7000 N, and does not present any statistical differences’ significance between two CAD-CAM systems studied. This contribution suggests that the number of axes improves vertical fit and surface quality due to the lower roughness. These claims show some discrepancies with other studies. Full article

The focused ion beam (FIB) has proven to be an extremely powerful tool for the nanometer-scale machining and patterning of nanostructures. In this work, we experimentally study the behavior of AISI 420 martensitic stainless steel when bombarded by Ga⁺ ions in a FIB system. The results show the formation of nanometer sized spiky structures. Utilizing the nanospiking effect, we fabricated a single-tip needle with a measured 15.15 nanometer curvature radius and a microneedle with a nanometer sized spiky surface. The nanospikes can be made straight or angled, depending on the incident angle between the sample and the beam. We also show that the nanospiking effect is present in ferritic AISI 430 stainless steel. The weak occurrence of the nanospiking effect in between nano-rough regions (nano-cliffs) was also witnessed for austenitic AISI 316 and martensitic AISI 431 stainless steel samples. Full article

This study presents a fabrication route for an electrically conductive ZrO₂–TiN ceramic nanocomposite with a nanoscale TiN phase occupying ≤30 vol% to improve the mechanical reinforcement of the zirconia matrix, and at the same time provide electrical conductivity to facilitate electro-discharge machining (EDM). The TiN nanoparticles were incorporated into a 3 mol% yttria-stabilized tetragonal zirconia (Y-TZP) powder, either by admixing a TiN nanopowder (MCP) or by using in-situ synthesis (ISS) via the forced hydrolysis of a titanyl sulphate aqueous solution and the direct nitriding of as-synthesized titania nanoparticles, followed by consolidation and rapid sintering in a spark plasma sintering (SPS) system. The initial phase composition and crystal structure of the as-synthesized powders and the sintered samples were characterized by transmission electron microscopy (TEM) and X-ray difraction (XRD). The influence of the different fabrication routes on the microstructural evolution, electrical and mechanical properties, and affinity for EDM were assessed using TEM, focused ion beam scanning electron microscopy (FIB-SEM, Vickers indentation, electrical conductivity measurements, and profilometry. The MCP synthesis route resulted in finer microstructures that are less prone to microstructural inhomogeneities; however, using the ISS route, it was possible to fabricate electrically conductive Y-TZP nanocomposites containing only 15 vol% of the TiN nanoparticulate phase. Both synthesis routes resulted in an increase of the fracture toughness with an increase of the TiN phase due to the nanoparticulate TiN reinforcement of the Y-TZP ceramic matrix via crack-bridging toughening mechanisms. As both synthesis routes yielded Y-TZP nanocomposites capable of successful EDM machining at a TiN content of ≥30 vol% for the MCP and ≥ 15 vol% TiN for the ISS, a possible mechanism was developed based on the microstructure evolution and grain growth. Full article