Search Results (432)

Ceria is an important redox catalyst due to the facile Ce³⁺/Ce⁴⁺ switching at its surface. Therefore, in situ determination of the oxidation state of surface cerium cations is of significant interest. Infrared spectroscopy of probe molecules such as CO holds great potential for this purpose. However, the ability of CO to reduce Ce⁴⁺ cations is an important drawback as it alters the initial cerium speciation. Dinitrogen (N₂), due to its chemical inertness, presents an attractive alternative. We recently demonstrated that low-temperature ¹⁵N₂ adsorption on stoichiometric ceria leads to the formation of complexes with Ce⁴⁺ cations on the (110) and (100) planes (bands at 2257 and 2252 cm⁻¹, respectively), while the (111) plane is inert. Here, we report results on the low-temperature ¹⁵N₂ adsorption on reduced ceria nanoshapes (cubes, polyhedra, and rods). A main band at 2255 cm⁻¹, with a weak shoulder at 2254 cm⁻¹, was observed. We attributed these bands to ¹⁵N₂ adsorbed on Ce³⁺ sites located on edges and corners as well as on {100} facets. In conclusion, ¹⁵N₂ adsorbs on the most acidic surface Ce³⁺ sites and enables their distinction from Ce⁴⁺ cations. Full article

Refractory high-entropy alloys (HEAs) are promising candidates for next-generation nuclear applications, particularly fusion reactors, due to their excellent high-temperature mechanical properties and irradiation resistance. Here, the microstructure and mechanical behavior were investigated for an equimolar WTaTiVZr HEA, designed from a palette of low-activation elements. The as-cast alloy exhibited a dendritic microstructure composed of W-Ta rich dendrites and Zr-Ti-V rich inter-dendritic regions, both possessing a body-centered cubic (BCC) crystal structure. Room temperature bulk compression tests showed ultra-high strength of around 1.6 GPa and plastic strain ~6%, with fracture surfaces showing cleavage facets. The alloy also demonstrated excellent high-temperature strength of ~650 MPa at 500 °C. Scratch-based fracture toughness was ~38 MPa√m for the as-cast WTaTiVZr HEA compared to ~25 MPa√m for commercially used pure tungsten. This higher value of fracture toughness indicates superior damage tolerance relative to commercially used pure tungsten. These results highlight the alloy’s potential as a low-activation structural material for high-temperature plasma-facing components (PFCs) in fusion reactors. Full article

Silicon (Si) and silicon carbide (SiC) are second- and third-generation semiconductor materials with excellent properties that are particularly suitable for applications in scenarios such as high temperature, high voltage, and high frequency. Si/SiC wafers face warpage and bending problems during production, which can seriously affect subsequent processing. Fast, accurate, and comprehensive detection of thickness, thickness variation, and flatness (including bow and warpage) of SiC and Si wafers is an industry-recognized challenge. Frequency scanning interferometry (FSI) can synchronize the upper and lower surfaces and thickness information of transparent parallel thin wafers, but it is still affected by multiple interfacial harmonic reflections, reflectivity asymmetry, and phase modulation uncertainty when measuring SiC thin wafers, which leads to thickness calculation errors and face reconstruction deviations. To this end, this paper proposes a high-precision facet reconstruction method for SiC/Si structures, which combines harmonic spectral domain decomposition, refractive index gradient constraints, and partitioning optimization strategy, and introduces interferometric signal “oversampling” and weighted fusion of multiple sets of data to effectively suppress higher-order harmonic interferences, and to enhance the accuracy of phase resolution. The multi-layer iterative optimization model further enhances the measurement accuracy and robustness of the system. The flatness measurement system constructed based on this method can realize the simultaneous acquisition of three-dimensional top and bottom surfaces on 6-inch Si/SiC wafers, and accurately reconstruct the key parameters, such as flatness, warpage, and thickness variation (TTV). A comparison with the Corning Tropel FlatMaster commercial system shows that this method has high consistency and good applicability. Full article

Semiconductor device fabrication is conducted through highly precise manufacturing processes. An essential component of the semiconductor package is the lead frame on which the silicon dies are assembled. Impurities such as oxides or organic matter on the surfaces have an impact on the process yield. Plasma cleaning is a vital process in semiconductor manufacturing, employed to enhance production yield through precise and efficient surface preparation essential for device fabrication. This paper explores the various facets of plasma cleaning, with a particular emphasis on its application in the cleaning of lead frames used in semiconductor packaging. To provide comprehensive context, this paper also reviews the critical role of plasma in advanced and emerging packaging technologies. This study investigates the fundamental physics governing plasma generation, the design of plasma systems, and the composition of the plasma medium. A central focus of this work is the comparative analysis of different plasma systems in terms of their effectiveness in removing organic contaminants and oxide residues from substrate surfaces. By utilizing reactive species generated within the plasma—such as oxygen radicals, hydrogen ions, and other chemically active constituents—these systems enable a non-contact, damage-free cleaning method that offers significant advantages over conventional wet chemical processes. Additionally, the role of non-reactive species, such as argon, in sputtering processes for surface preparation is examined. Sputtering is the ejection of individual atoms from a target surface due to momentum transfer from an energetic particle (usually an ion). Sputtering is therefore a physical process driven by momentum transfer. Energetic ions, such as argon (Ar⁺), are accelerated from the plasma to bombard a target surface. Upon impact, these ions transfer sufficient kinetic energy to atoms within the material’s lattice to overcome their surface binding energy, resulting in their physical ejection. This paper also provides a comparative assessment of various plasma sources, including direct current, dielectric barrier discharge, radio frequency, and microwave-based systems, evaluating their suitability and efficiency for lead frame cleaning applications. Furthermore, it addresses critical parameters affecting plasma cleaning performance, such as gas chemistry, power input, pressure regulation, and substrate handling techniques. The ultimate aim of this paper is to provide a concise yet comprehensive resource that equips technical personnel with the essential knowledge required to make informed decisions regarding plasma cleaning technologies and their implementation in semiconductor manufacturing. This paper provides various tables which provide the reader with comparative assessments of the various plasma sources and gases used. Scoring mechanisms are also introduced and utilized in this paper. The scores achieved by both the sources and the plasma gases are then summarized in this paper’s conclusions. Full article

With the increase in heavy-load traffic and the growing frequency of extreme weather events, traditional rock aggregates, due to poor morphological stability, are unable to meet the performance requirements of high-grade asphalt pavements in complex environments. Most existing research on metal reinforcement focuses on fiber forms. This study innovatively introduces L-shaped multi-faceted metal aggregates (LFMAs). Through surface energy analysis and tests such as the Marshall test, rutting test, water immersion Marshall test, and freeze–thaw splitting test, the effects of the dosage and particle size of LFMAs on the performance of asphalt mixtures are explored. The results show that LFMAs can form an effective bond with SBS modified asphalt, improving the high-temperature stability and low-temperature crack resistance of asphalt mixtures. Under both water immersion and freeze–thaw conditions, the resistance of asphalt mixtures to water damage decreases with the increase in the dosage of metal aggregates. This research expands the application of three-dimensional metal aggregates, breaks through the limitations of fiber-based materials, and provides a new direction for the development of high-performance asphalt mixtures. Full article

Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. We propose the Weighted Importance Score and Frequency Count (WISFC) framework, which combines importance magnitude and consistency by aggregating ranked outputs from diverse explainers. WISFC assigns a weighted score to each feature based on its rank and frequency across model-explainer pairs, providing a robust ensemble feature-importance ranking. Unlike simple consensus voting or ranking heuristics that are insufficient for capturing complex relationships among different explainer outputs, WISFC offers a more principled approach to reconciling and aggregating this information. By aggregating many “weak signals” from brute-force modeling runs, WISFC can surface a stronger consensus on which variables matter most. The framework is designed to be reproducible and generalizable, capable of taking important outputs from any set of machine-learning models and producing an aggregated ranking highlighting consistently important features. This approach acknowledges that any single model is a simplification of complex, multidimensional phenomena; using multiple diverse models, each optimized from a different perspective, WISFC systematically captures different facets of the problem space to create a more structured and comprehensive view. As a consequence, this study offers a useful strategy for researchers and practitioners who seek innovative ways of exploring complex systems, not by discovering entirely new variables but by introducing a novel mindset for systematically combining multiple modeling perspectives. Full article

Aerogels have gained much interest in the last decades due to their specific properties, such as high porosity, high surface area, and low density, which have caused them to be used in multiple and varied fields. As the applicability of aerogels is tightly correlated to their morpho-structural features, special consideration must be allocated to the fabrication method. An emerging technique for producing nanostructured materials with tailored morphology and dimensions is represented by continuous-flow microfluidics. In this context, this work explores the synergic combination of aerogel-based materials with microfluidic synthesis platforms to generate advanced nanocomposite adsorbents for water decontamination. Specifically, this study presents the novel synthesis of a magnetic silica-based aerogel using a custom-designed 3D microfluidic platform, offering enhanced control over nanoparticle incorporation and gelation compared to conventional sol–gel techniques. The resulting gel was further dried via supercritical CO₂ extraction to preserve its unique nanostructure. The multi-faceted physicochemical investigations (XRD, DLS, FT-IR, RAMAN, SEM, and TEM) confirmed the material’s uniform morphology, high porosity, and surface functionalization. The HR-MS FT-ICR analysis has also demonstrated the advanced material’s adsorption capacity for various pesticides, suggesting its adequacy for further environmental applications. An exceptional 93.7% extraction efficiency was registered for triazophos, underscoring the potential of microfluidic synthesis approaches in engineering advanced, eco-friendly adsorbent materials for water decontamination of relevant organic pollutants. Full article

The safe removal of residual flammable contaminants from vertical mixer blades is a crucial challenge in aerospace propellant production. While robotic cleaning has become the preferred solution due to its precision and operational safety, the complex helical geometry of mixer blades presents significant challenges for robotic systems, primarily in three aspects: (1) dynamic sub-region division, requiring simultaneous consideration of functional zones and residue distribution, (2) ensuring path continuity across surfaces with varying curvature, and (3) balancing time–energy efficiency in discontinuous cleaning sequences. To address these challenges, this paper proposes a novel robotic cleaning path planning method for complex curved surfaces. Firstly, we introduce a blade surface segmentation approach based on the k-means++ clustering algorithm, along with a sub-surface patch boundary determination method using parameterized curves, to achieve precise surface partitioning. Subsequently, robot cleaning paths are planned for each sub-surface according to cleaning requirements and tool constraints. Finally, with total cleaning time as the optimization objective, a genetic algorithm is employed to optimize the path combination across sub-facets. Extensive experimental results validate the effectiveness of the proposed method in robotic cleaning path planning. Full article

Laser powder bed fusion (LPBF) of SiCp/AlSi10Mg is promising in many industrial fields. In this paper, the characteristics of a 15 wt.% 1200 mesh SiCp/AlSi10Mg metal matrix composite fabricated by LPBF were investigated systematically, i.e., from a single track to a block. It was found that when the laser energy input was high enough, the single track was continuous and not distorted; when the laser energy input was low, the single track was unstable and wrinkled. The densification of the LPBFed composite sample was influenced significantly by the surface morphologies and geometric dimensions of the single tracks. As high as 98.9% relative density was achieved when the optimized processing parameters were used. Because of the good wettability and the interfacial reaction during the process, the interface of SiC and the matrix showed good bonding. Near the interface of SiC and the matrix, needle-shaped phase Al₄SiC₄ could be found both in the single track and block, and the faceted particle Si was formed in the block because of the interfacial reaction. The microhardness of the LPBFed SiCp/AlSi10Mg composites was much higher than that of the LPBFed unreinforced AlSi10Mg. A coefficient of friction of 0.178 and wear rate of 2.02 × 10⁻⁴ mm³/(N⋅m) were achieved for the LPBFed composites. The main wear mechanism was delamination wear, accompanied by abrasive wear. The maximum yield strength and ultimate compressive strength were 566.6 MPa and 764.1 MPa, respectively. The fracture mode of the LPBFed composites is mainly brittle fracture. This study provides a theoretical and technical basis for LPBFed SiCp/AlSi10Mg 3D parts. Full article

(1) Background: Asomate, as a dithiocarbamate compound, is moderately toxic to the human body; thus, it is necessary to develop a rapid and efficient method for detection. To meet this need, this study introduced a rapid, non-destructive, and efficient method for detecting asomate residues on the surface of apples based on surface-enhanced Raman spectroscopy (SERS) combined with flexible substrates. (2) Methods: Concave Au nanorods (AuCNAs) were synthesized in advance. Then, the AuCNAs were loaded on an electrostatically spun film to generate a flexible TiO₂/ZrO₂/AuCNAs substrate for detection. (3) Results: The flexible substrate exhibited strong SERS activity, with an enhancement factor (EF) up to 9.40 × 10⁷ for 4-MBA. Meanwhile, the finite-difference time-domain (FDTD) simulation showed that the localized surface plasmon resonance (LSPR) effects related to the enhancement of the SERS signal are mainly generated from the ‘hot spots’ in AuCNAs. The density functional theory (DFT) simulation detailedly revealed that the SERS peaks could be generated by the interaction among asomate molecules, disassociated Au atoms, and Au facets. Moreover, the asomate in apple peel was analyzed with the limit of detection (LOD) as low as below 10 nM, allowing for the rapid detection of asomate directly on apple peels. (4) Conclusions: The flexible TiO₂/ZrO₂/AuCNAs film can be used for the in situ detection of asomate in apple peel at low concentrations. Moreover, the simulation methods, including FDTD and DFT, explained the mechanism of SERS from the flexible substrates. Full article

A novel ultrasound-assisted method for synthesizing small, uniform stannous oxide (SnO) at room temperature was proposed in this work. The experimental results showed that the median particle size D₅₀ of SnO prepared by ultrasound was 5.2 μm, with a particle size distribution ranging from 2.9 to 8.7 μm and exhibiting a homogeneous micromorphology. This solves the problems of a median particle size D₅₀ higher than 20 μm, a wide range of particle size distributions, and uneven micromorphology in conventional preparation. The XRD and SEM results revealed that the introduction of ultrasound promoted the conversion of the intermediate product Sn₆O₄(OH)₄ to SnO, increased the exposure of the (001) and (002) crystal facets, promoted tetragonal growth, and suppressed particle aggregation, leading to finer and more uniformly distributed stannous oxide particles. BET and XPS analyses further demonstrated that ultrasound increased the specific surface area and the O-Sn²⁺ content, indicating enhanced surface reactivity. Full article

The exploration of the coupling trend between urban agglomeration development (UAD) and land surface temperature (LST) expansion is of great significance, and it is of scientific value for the regulation of the thermal environment of urban agglomerations, the optimization of urban spatial planning, and the achievement of sustainable urban development. This study employs an array of remote sensing datasets from multiple sources—employing a multi-faceted approach encompassing an overall coupling situation analysis model, a coordination and evaluation system, a geographically weighted spatial autocorrelation algorithm, and landscape pattern quantification indicators—to explore the mutual feedback mechanism and spatial coupling characterization of LST and UAD in the Guanzhong Plain Urban Agglomeration (GZPUA). The results of the study can provide data support for urban spatial planning and thermal environment regulation. The results indicate the following findings: (1) In the GZPUA, the nighttime light (NTL) and land surface temperature (LST) centroids show a significant tendency toward approaching one another, with a spatial offset decreasing from 45.0 km to 9.1 km at the end, indicating a strengthening trend in the photothermal system’s coupling synergy. (2) The coordination of light and heat in the study area exhibits significant non-equilibrium development, with a dynamic trend of urban development space shifting towards the southwest. It confirms the typical regional response law of rapid urbanization. (3) The Moran’s I index of the photothermal system in the study area increased from 0.289 to 0.335, an increase of 15.9%. The proportion of “high–high” (H-H)/“low–low” (L-L)-type regions with clustering distribution of cold and hot spots reaches 58.01%, and their spatial continuity characteristics are significantly enhanced, indicating a significant trend of spatial structural integration between urban heat island effect and construction land expansion. Full article

Background/Objectives: Accurate implant impressions are critical for capturing the three-dimensional (3D) spatial positioning of implants. Digital workflows using intraoral scanners (IOSs) and scan bodies offer distinct advantages over conventional elastomeric techniques. However, the geometry of scan bodies may influence the precision and trueness of IOS-acquired data, and optimal design parameters remain undefined. This systematic review aims to evaluate the effects of scan body geometry on the trueness of digital implant impressions captured using IOSs. Methods: A systematic search was conducted across PubMed, Scopus, EMBASE, Web of Science, the Cochrane Library, and Google Scholar up to 25 February 2025. Eligible studies assessed the impact of scan body geometry on the accuracy of implant-level impressions acquired with IOSs. Study quality was assessed using the Quality Assessment Tool for In Vitro Studies of Dental Materials (QUIN). Results: Twenty-eight studies were included, of which twenty-six were in vitro. The included studies, published between 2020 and 2025, demonstrated that variations in macro- and micro-geometries influenced both linear and angular trueness. Cylindrical designs with optimal dimensions generally outperformed cuboidal or spherical forms. Structural modifications, such as rigid bar extensions and surface facets, often improved scan accuracy. Some hybrid or modified designs performed comparably to conventional scan bodies. According to QUIN, 27 studies were moderate quality and one had high quality. Conclusions: Scan body geometry affected the accuracy of intraoral implant digital impressions. Designs featuring rigid extensions or simplified geometries improve trueness and precision. Further standardized clinical studies are needed to define optimal design features and validate current in vitro findings. Full article

Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. Full article

In chemical vapor deposition (CVD)-mediated graphene growth, copper foil serves as both a catalyst for methane decomposition and as a substrate for graphene nucleation and growth. Due to the low solubility of carbon in copper and the ease of transferring graphene from its surface, copper—particularly the Cu(111) facet—is widely favored for high-quality, monolayer graphene synthesis. In this article, the thermodynamic processes involved in methane dissociation and graphene nucleation on the Cu(111) surface were investigated using density functional theory (DFT). Molecular dynamics simulations were performed for structural optimization and to evaluate the reaction energies. Additionally, the average adsorption energies (ΔEad) of carbon clusters with varying atomic numbers on the Cu(111) surface were calculated. The graphene growth process was further modeled using the kinetic Monte Carlo (KMC) method to simulate carbon atom migration and nucleation dynamics. Thermodynamic analysis based on equilibrium component data was conducted to examine the influence of key operational parameters—temperature, pressure, and the CH₄/H₂ partial pressure ratio—on the graphene deposition rate. Full article