Search Results (2,352)

Glass preheating prior to laser scanning is expected to enhance internal modification morphology; however, its effect on weld seam topology and welding strength have not been investigated. In the current work, the effects of preheating on ultrafast laser (184 fs and 10 ps) internal modification and welding strength of borosilicate glass slides are investigated. For the internal modification experiments, pulse energy of 30–100 µJ and repetition rate of 10 kHz are used by focusing a laser beam at the interface of optically contacted slides at room temperature (RT ≈ 23 °C), 150 and 200 °C. Welding is conducted by a pulse energy of 4.5–18 µJ and repetition rate of 200 kHz using pre-clamped glass slides with a scanning speed of 10 mm/s at RT and 150 °C. Also, for welding, the optimum number of scans and hatching spacing are identified. Filamentation experiments show that discoloration is not significant when preheat temperature reaches 200 °C. Compared to 10 ps, pulse duration of 184 fs can produce a 19% narrower plasma-modified region at both RT and 150 °C and a 13% wider heat-affected zone at 150 °C. Welding using optimum conditions of 5 scans and 200 µm hatch, and “crack-free” laser parameters produces an average strength of: 50 ± 3.2 MPa at RT and 40 ± 2 MPa at 150 °C for 184 fs compared to 35 MPa at RT and 32 MPa at 150 °C for 10 ps, using 10 replicates each. However, the welding strength upon preheating to 150 °C using 184 fs is still 25% higher compared to average reported laser welding bonding strength, while the 10 ps strength is within the reported average. The enhanced welding strength for 184 fs can be attributed to reduced microcracking, especially when “crack free” combinations are utilized. Full article

Two-dimensional (2D) materials exhibit electronic and collective excitation properties that are highly sensitive to surface chemistry and thickness, requiring surface-sensitive characterization at low electron energies. Here, we investigate graphene, hexagonal boron nitride (h-BN), molybdenum disulfide (MoS₂), and titanium carbide (Ti₃C₂) MXene using an advanced home-built scanning low-energy electron microscopy system combined with time-of-flight electron spectroscopy (SLEEM/ToF). The system uniquely records electron energy-loss spectra (EELS) from transmitted electrons rather than from the reflected electrons used in conventional SLEEM. Compared with high-energy EELS, our low-energy ToF-EELS approach offers enhanced surface sensitivity and reduced beam-induced damage, enabling direct probing of π and π + σ plasmon excitations. Additionally, complementary techniques, including scanning transmission electron microscopy (STEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), were employed to characterize structural and chemical properties. EELS were acquired for all investigated 2D materials at electron landing energies of 500–1500 eV, and in the 5–50 eV range for selected materials, including graphene and MoS₂. Analysis of these spectra enabled determination of the average plasmon positions across the measured energy range for all studied materials. Furthermore, a quantitative determination of the inelastic mean free path (IMFP) was achieved for graphene in the 10–50 eV range, yielding a value of 1.9 ± 0.2 nm. These results demonstrate the potential of SLEEM–ToF for surface-sensitive analysis of 2D materials. Full article

Biomedical Ti–Nb–Zr–Si alloys containing 12 and 18 wt.% Nb were fabricated by electron beam melting and subjected to thermomechanical processing, including forging, cross-helical rolling, and subsequent cooling or quenching. The effects of Nb content and processing route on phase composition, microstructure, and mechanical properties were systematically investigated using X-ray diffraction, scanning electron microscopy, and tensile testing. The results indicate that increasing Nb content promotes stabilization of the metastable α″ phase, leading to a significant reduction in elastic modulus. The Ti–18Nb–4Zr–1Si alloy exhibited a modulus of ~60 GPa after rolling, which further decreased to ~40 GPa after additional quenching. In contrast, the Ti–12Nb–4Zr–1Si alloy showed higher values of 76–94 GPa due to the predominance of the α′ phase. Both alloys demonstrated a favorable combination of strength and ductility. Microstructural analysis revealed the formation of silicides, whose type and morphology depend on Nb content and processing conditions. The Ti–12Nb–4Zr–1Si alloy predominantly contains (Ti,Zr)₅Si₃, whereas the Ti–18Nb–4Zr–1Si alloy exhibits complex silicides composed of (Ti,Zr)₅Si₃ and (Ti,Zr)₃Si phases. These results highlight the potential of controlling phase composition and silicide evolution to tailor mechanical properties, particularly the elastic modulus, for biomedical applications. Full article

Background: Monitoring skeletal muscle mass (SMM) during radiotherapy (RT) is important, as SMM loss is associated with poorer clinical outcomes. Cone-beam CT (CBCT), acquired before each RT fraction, offers the potential to track the lumbar skeletal muscle index (LSMI) over time. However, CBCT has lower image quality than conventional CT. This study assessed the agreement between CT and CBCT and evaluated the reliability of LSMI measurements in patients with head and neck squamous cell carcinoma. Methods: Patients who underwent both CT and CBCT on the same day during RT were included. The cross-sectional muscle area at C3 was measured, converted to L3, and used to calculate the LSMI. Two researchers analyzed all scans, with one repeating the measurements. Agreement and reliability were quantified using intraclass correlation coefficients (ICCs) and visualized with Bland–Altman plots. Results: LSMI measurements showed excellent agreement between CBCT and CT (ICC: 0.97–0.99; 95% CI: 0.95–0.99). The intrarater (ICC: 0.99; 95% CI 0.98–0.99) and interrater reliability (ICC: 0.97; 95% CI: 0.66–0.99) were high. Bland–Altman plots, however, revealed wide limits of agreement. Conclusion: CBCT provides reliable LSMI measurements and agrees well with CT, but the observed variability suggests cautious interpretation. When both modalities are available, CT remains the preferred standard for SMM assessment. Full article

The accuracy of static computer-aided implant surgery (s-CAIS) is fundamental for predictable clinical outcomes. The objective of this study was to evaluate the influence of different guide-support modalities on the linear and angular accuracy of implant placement. In this retrospective clinical investigation conducted at a single specialty hospital, a total of 180 implants were analyzed, divided into three equal groups (n = 60) based on the guide support type: tooth-supported, bone-supported, and mucosa-supported. Accuracy was assessed by superimposing preoperative virtual plans with postoperative cone-beam computed tomography (CBCT) scans, measuring linear deviations at the neck and apex of the implant, as well as angular discrepancies. The type of guide support was found to be a significant factor associated with surgical accuracy (p < 0.001). Tooth-supported guides demonstrated the highest level of accuracy, with a mean angular deviation of 1.81° ± 0.45° and linear deviations at the neck and apex of 0.59 ± 0.18 mm and 0.73 ± 0.19 mm, respectively. These were followed by bone-supported guides (2.14° ± 0.48°; 1.04 ± 0.26 mm; 1.61 ± 0.31 mm), while mucosa-supported guides exhibited the greatest deviations (2.95° ± 0.60°; 1.47 ± 0.29 mm; 1.87 ± 0.37 mm). Significant intergroup differences and large effect sizes were observed, particularly regarding angular and horizontal discrepancies. These findings demonstrate a distinct gradient of accuracy based on guide support, establishing tooth-supported guides as the most accurate, followed by bone-supported and, lastly, mucosa-supported guides. While all modalities are clinically applicable, the use of mucosa-supported guides necessitates increased safety margins to account for the increased risk of linear and angular discrepancies inherent to mucosal tissue displacement. Full article

In this paper, we consider a series of new compact A-π-D photoinitiators consisting of donor aromatic fragments (naphthalene, anthracene, phenanthrene, pyrene and perylene) and a strong acceptor tricyanoethylene group—aryltricyanoethylenes (ArTCNEs). Spectral, photophysical, and electrochemical characteristics of ArTCNEs are studied. One-photon (with LED@405 nm) and two-photon (λ = 780 nm, impulse duration of 100 fs) photopolymerization of PETA can be effectively initiated by ArTCNEs with the tertiary amine N,N-dimethylcyclohexylamine DMCHA and/or the iodonium salt diphenyliodonium chloride Iod. Based on results of experiments on photodegradation, photopolymerization and EPR spectroscopy, a photoinitiation mechanism of radical photopolymerization was proposed for two-component (AntTCNE/DMCHA) and three-component (AntTCNE/DMCHA/Iod) initiating systems. The composition containing PerTCNE/DMCHA as a photoinitiator demonstrated the best reactivity under two-photon nanolithography conditions: the polymerization threshold was 2 mW at a laser beam scanning speed of 100 μm/s, and the widest fabrication window of 11 mW was typical for it. As an example, 3D “cage” structures were fabricated using the AntTCNE-based composition, and the test structure resolution parameters, such as the minimum line width and the distance between lines of 80 and 400 nm, respectively, were achieved. MTT experiments with human dermal fibroblasts showed promising preliminary biocompatibility of the resulting polymers, which opens up possibilities for using the obtained materials in biological applications. Full article

Arc-driven powder bed fusion represents a low-cost alternative to beam-based powder bed systems, yet the morphological stability regimes governing single-track formation and the relative influence of process parameters on regime transitions have not been systematically characterised. Manual visual assessment of track morphology is inherently subjective and cannot objectively quantify the parameter hierarchy governing stability boundaries. This study addresses both limitations through two complementary contributions. A deterministic two-stage image-based framework is developed to automatically classify single-track morphology from top-view images of solidified 316L stainless steel tracks, replacing subjective assessment with a reproducible, intervention-free procedure. A gap-based continuity criterion distinguishes discontinuous from continuous melt paths; for continuous tracks, the coefficient of variation in width (CV (coefficient of variation) < 0.15) further separates geometrically stable from transitional morphologies. Building on the image-derived regime labels, two interpretable classifiers—a depth-limited Decision Tree (DT) and a regularised Logistic Regression (LR) —are fitted using applied current, scanning speed, and electrode-to-powder-bed distance as predictors. The classifiers are employed not for predictive generalisation but to extract standardised coefficients and permutation-based feature importance rankings, yielding a model-agnostic, quantitative explanation of which process parameters govern regime transitions. Stable continuous tracks are obtained only within a restricted parameter window. Permutation importance consistently ranks applied current as the dominant predictor, followed by electrode distance and scanning speed, in agreement with the thermophysical interpretation. Logistic Regression coefficients confirm that reduced stand-off distance is a necessary condition for sufficient arc constriction. Supplementary linear regression models indicate that applied current governs melt pool depth, whereas scanning speed is the primary determinant of width variation. The combined framework establishes a reproducible basis for process parameter hierarchy analysis in arc-driven powder bed systems and provides a foundation for regression-based process optimisation. Full article

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography [CBCT]-based segmentation), prosthodontic design (intraoral scan [IOS]/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs. Full article

This study investigates the residual stresses developed during the curing process of polymer fiber-reinforced composites and their influence on fracture behavior, particularly the initiation and propagation of interlaminar cracks. The main objective is to quantify how different curing histories, including incomplete cure, alter the spatial distribution of residual stresses and, in turn, affect the mode-I fracture response of carbon-fiber/epoxy laminates. A transient thermal–structural finite element framework incorporating an autocatalytic cure kinetics model was used to simulate the curing process and predict residual stress development in a unidirectional carbon-fiber/epoxy laminate with an edge crack, considering thermal, chemical, and geometric effects. The cure model was calibrated using isothermal differential scanning calorimetry data to determine the degree of cure under different thermal conditions. The key novelty of this work is the integration of a validated cure-kinetics-based curing simulation with fracture analysis, enabling direct correlation of thermal history and degree of cure with spatially varying residual stresses at the crack front and their effect on fracture toughness. Numerical load–displacement predictions were compared with double cantilever beam experimental results and showed good agreement for the curing profiles examined. The results demonstrate that residual stresses generated by different cure cycles, including hold conditions and incomplete curing, significantly influence fracture toughness. In particular, the incomplete-cure profile produced an approximately 40% reduction in toughness compared with profiles that achieved complete cure, highlighting the importance of cure history in determining final structural performance. Full article

Accurate programming of sagittal condylar inclination (SCI) and Bennett angle (BA) is important for prosthodontic treatment, yet evidence directly comparing conventional and digital recording approaches remains limited. This single-participant pilot feasibility study compared SCI and BA obtained using a digital jaw motion tracking system, a conventional facebow transfer method, and a cone-beam computed tomography (CBCT)-based registration method. Ten repeated datasets were generated for each method from one healthy adult participant. The digital system recorded mandibular motion using optical tracking and automatically calculated SCI and BA in a virtual articulator. The conventional method used a mechanical facebow and check-bite records, whereas the CBCT-based method combined one centric-relation CBCT scan with repeated protrusive and lateral interocclusal records after digital alignment. Significant differences were observed for left SCI (p = 0.036), left BA (p = 0.049), and right BA (p < 0.001), whereas right SCI was not significantly different (p = 0.197). The digital method showed the lowest standard deviations across all variables and lower coefficients of variation for left SCI, right SCI, and left BA. Within the limitations of this single-participant pilot study, digital jaw motion tracking demonstrated favorable repeatability and clinically comparable measurements, supporting its potential utility in digitally integrated prosthodontic workflows. Full article

Sub-15 nm line structures are key building blocks for advanced device prototyping, nanoscale electrodes, and lithography templates such as etch/deposition masks. Although ultrahigh-voltage (≥100 kV) electron-beam lithography (EBL) can more readily achieve extremely small critical dimensions, its tool and infrastructure requirements limit widespread adoption in many laboratories. In contrast, 30 kV field-emission SEM platforms are far more accessible; however, resolution-limit patterning at 30 kV is more sensitive to beam current, exposure dose, and development conditions, motivating the establishment of a reproducible process flow and a well-defined process window. Here, we investigate the resolution limit of isolated lines using a Zeiss Gemini 460 system operated at 30 kV and an in-house pattern generator with 950 k PMMA C2 resist. To demonstrate device-level applicability, we develop a stable lift-off process, and all critical dimensions are evaluated on metal lines after e-beam evaporation and lift-off. By screening beam current and scanning dose to build the dose-to-size relationship, we show that reducing beam current significantly improves the achievable minimum line width. Under 35 pA, using CD ≤ 15 nm as the criterion for sub-15 nm window extraction, the usable dose range is [700, 804.3] µC/cm², corresponding to a dose latitude of ~14.9%. The best performance is obtained at 700 µC/cm², yielding a transferred metal line width of 13.85 nm after lift-off. This work provides a practical resolution-limit process flow and a quantitative process window for performing sub-15 nm patterning on accessible 30 kV platforms, supported by product-level lift-off validation. Full article

In response to the shortcomings still observed in polyethylene (PE)/ethylene-vinyl acetate (EVA)/styrene-butadiene-styrene (SBS) composite modified bitumen regarding storage stratification and low-temperature performance, this paper further introduces furfural extract, elemental sulphur, stabilisers and Z-6036 into this ternary system, and employs orthogonal design to screen the additive ratios. Tests were conducted on conventional physical properties, rotational viscosity, dynamic shear rheology and bending beam rheology, focusing on the material’s temperature sensitivity, rheological behaviour, low-temperature creep resistance and phase characteristics. The modification effects were analysed using fluorescence microscopy, scanning electron microscopy and infrared spectroscopy. Compared with the control group composed of 4% PE, 4% EVA and 2% SBS, the samples obtained from the orthogonal design showed an increase in elongation at 5 °C ranging from 52.5% to 213.9%; the difference in softening points decreased from 35.2 °C to a minimum of 0.1 °C, indicating improved storage stability. The temperature sensitivity of all sample groups was reduced, with the optimal group achieving a VTS of −0.4413, representing a 46.7% improvement over the control group. At −12 °C, the m-values of all nine orthogonal samples were higher than those of the control group, with seven groups reaching m ≥ 0.3, indicating improved low-temperature stress relaxation capability. A comprehensive analysis of the experimental results indicates that the selected chemical additives are beneficial for optimising the dispersion state and compatibility of the SBS/PE/EVA ternary modified bitumen, whilst also balancing rheological properties and low-temperature crack resistance to a certain extent. Microscopic and spectroscopic analyses further suggest that internal interactions within the system have been enhanced and the phase distribution has become more uniform; however, the current evidence is insufficient to conclusively determine that a specific form of chemical cross-linking reaction has occurred. Full article

Background/Objective: To evaluate the reliability, diagnostic accuracy and time efficiency of an artificial intelligence (AI)-automated method (CephX) and a semiautomated method (INVIVO) for upper airway segmentation, the manual digital method (ITK-SNAP) was used as the reference standard. Methods: This retrospective study analyzed cone-beam computed tomography (CBCT) scans from 133 patients. The upper airway volume and narrowest cross-sectional area were measured via the three methods. Reliability and repeatability were assessed via the intraclass correlation coefficient (ICC). The time required for each segmentation method was also recorded and compared. Results: Both the AI-automated (ICC = 0.945) and semiautomated (ICC = 0.992) methods demonstrated excellent reliability for total volume measurements compared with the manual reference. For the narrowest area, the automated method showed excellent agreement (ICC = 0.943), whereas the semiautomated method showed good agreement (ICC = 0.868). All methods demonstrated excellent intrareader repeatability (ICC > 0.95) and high test–retest reliability. The AI-automated method was significantly more time-efficient, requiring less than 30 s per analysis, compared with 161.4 s for the semiautomated method and 336.6 s for the manual method. Conclusions: AI-automated and semiautomated segmentation methods are reliable and accurate alternatives to manual upper airway analysis. The AI-based approach offers a substantial advantage in time efficiency, making it a valuable tool for clinical practice. Full article

Background/Objectives: The sella turcica and sphenoid sinus are anatomically adjacent structures within the cranial base and may reflect variations related to craniofacial development. However, evidence regarding their three-dimensional characteristics in individuals with impacted canines remains limited. This study aimed to evaluate the morphological, linear, and volumetric characteristics of the sella turcica and sphenoid sinus in individuals with unilateral palatally impacted maxillary canines using cone-beam computed tomography (CBCT). Methods: This study included CBCT scans of individuals with unilateral palatally impacted maxillary canines and a control group. Linear measurements and morphology of the sella turcica were assessed. Sella turcica volume was calculated using both a geometric formula and voxel-based three-dimensional segmentation. Sphenoid sinus pneumatization patterns and volumes were also evaluated. Agreement between volumetric measurement methods was assessed using Bland–Altman analysis, and correlations between sella turcica and sphenoid sinus volumes were also analyzed. Results: Most morphological and volumetric parameters of the sella turcica and sphenoid sinus were comparable between groups. Among the linear measurements, only sella width was significantly greater in the control group, whereas other dimensions showed no significant differences. The distribution of sella turcica morphology and sphenoid sinus pneumatization patterns was similar in both groups. No significant differences were observed in sella turcica or sphenoid sinus volumes. Bland–Altman analysis demonstrated good agreement between geometric and voxel-based volumetric measurements. In addition, no significant correlation was identified between sella turcica and sphenoid sinus volumes. Conclusions: Unilateral palatally impacted maxillary canines were not associated with substantial morphological or volumetric alterations of the sella turcica or sphenoid sinus. These findings suggest that variations in these cranial base structures have limited value as indicators of unilateral palatal canine impaction. Full article

Background/Objectives: This study aimed to develop and evaluate deep learning models for the detection, localization, and classification of impacted maxillary canines, and to compare their performance with cone-beam computed tomography (CBCT) as the reference standard. Methods: This cross-sectional diagnostic accuracy study was conducted at King Abdulaziz University Dental Hospital to develop and validate artificial intelligence (AI) models for detecting and localizing maxillary canine impactions using panoramic and cone-beam computed tomography (CBCT) imaging data. A total of 641 panoramic ra and 158 CBCT scans were collected, of which 158 cases had matched panoramic–CBCT pairs for localization analysis. Images were annotated and validated by expert radiologists and orthodontists, with consensus review ensuring labeling reliability. Data augmentation expanded each panoramic and CBCT category to 500 samples for panoramic and 1000 samples for CBCT, resulting in 1935 panoramic and 5703 CBCT images after preprocessing and normalization. The datasets were divided into (training + validation) (80%) and testing (20%) subsets. MobileNetV2 architectures were used for classification, and whdiographsile, a ResNet-50–based Few-Shot Learning framework, enabled spatial localization of impacted canines. Models were trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Cohen’s kappa and 95% confidence intervals were used to assess agreement between AI predictions and expert annotations. Results: Panoramic classification achieved 94% accuracy, demonstrating the highest performance in normal cases and reduced recall for bilateral impactions. The CBCT classifier achieved 98% accuracy across positional categories. Cross-modality prediction reached 93.5% accuracy, with strong agreement compared to CBCT (Cohen’s kappa = 0.91). Expert review confirmed reliable localization of impacted canines on both imaging modalities. Conclusions: Artificial intelligence applied to panoramic radiographs supports the detection, localization, and characterization of impacted maxillary canines with performance comparable to CBCT. This approach may enable lower-radiation decision support for clinical triage. Full article