Negative Differential Resistance Device with High Peak-to-Valley Ratio Realized by Subband Resonant Tunneling of Γ-Valley Carriers in WSe2/h-BN/WSe2 Junctions.

Resonant tunneling diodes (RTDs) are a core technology in III-V semiconductor devices. The realization of high-performance RTD using two-dimensional (2D) materials has been long awaited, but it has yet to be accomplished. To this end, we investigate a range of WSe2/h-BN/WSe2 RTD devices by varying the number of layers of source and drain WSe2. The highest peak-to-valley ratio (PVR) is demonstrated in the three-layer (3L) WSe2/h-BN/1-layer (1L) WSe2 structure. The observed PVR values of 63.6 at 2 K and 16.2 at 300 K are the highest among the 2D material-based RTDs reported to date. Our results indicate the two key conditions to achieve high PVR: (1) resonant tunneling should occur between the Γ-point bands of the source and drain electrodes, and (2) the Γ-point bands contributing to the resonant tunneling should be energetically separated from the other bands. Our results provide an important step to outperform III-V semiconductor RTDs with 2D material-based RTDs.

Control of Subband Energies via Interlayer Twisting in an Artificially Stacked WSe2 Bilayer.

Tuning the electronic structure of artificially stacked bilayer crystals using their twist angle has attracted a significant amount of interest. In this study, resonant tunneling spectroscopy was performed on trilayer WSe2/h-BN/twisted bilayer (tBL) WSe2 devices with a wide range of twist angles (θBL) of tBL WSe2, from 0° to 34°. We observed two resonant tunneling peaks, identified as the first and second lowest hole subbands at the valence band Γ point of tBL WSe2. The subband separation, which directly measured the interlayer coupling strength, was tuned by ∼0.1 eV as θBL increased toward 6° and remained nearly constant for larger θBL values. The θBL dependence was attributed to the emergence of a stable W/Se (Se/W) stacking domain in the small θBL region, owing to the atomic reconstruction of the moiré lattice in tBL WSe2. Our findings demonstrate that the twist-controlled subband energies in tBL WSe2 are predominantly determined by local atomic reconstruction.

RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study.

Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.

Crossover between rigid and reconstructed moiré lattice in h-BN-encapsulated twisted bilayer WSe2 with different twist angles.

A moiré lattice in a twisted-bilayer transition metal dichalcogenide (tBL-TMD) exhibits a complex atomic reconstruction effect when its twist angle is less than a few degrees. The influence of the atomic reconstruction on material properties of the tBL-TMD has been of particular interest. In this study, we performed scanning transmission electron microscopy (STEM) imaging of a moiré lattice in h-BN-encapsulated twisted bilayer WSe2 with various twist angles. Atomic-resolution imaging of the moiré lattice revealed a reconstructed moiré lattice below a crossover twist angle of ∼4° and a rigid moiré lattice above this angle. Our findings indicate that h-BN encapsulation has a considerable influence on lattice reconstruction, as the crossover twist angle was larger in h-BN-encapsulated devices compared to non-encapsulated devices. We believe that this difference is due to the improved flatness and uniformity of the twisted bilayers with h-BN encapsulation. Our results provide a foundation for a deeper understanding of the lattice reconstruction in twisted TMD materials with h-BN encapsulation.

Sex estimation from maxillofacial radiographs using a deep learning approach.

The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed. A saliency map was generated by gradient-weighted class activation mapping for each output. The two deep learning models achieved high values in each performance metric according to accuracy, sensitivity (recall), precision, F1 score, and areas under the receiver operating characteristic curve. Both models showed substantial differences in the positions indicated in saliency maps for male and female images. The positions in saliency maps also differed between VGG16 and DenseNet-121, regardless of sex. This analysis of our proposed system suggested that sex estimation from lateral cephalograms can be achieved with high accuracy using deep learning.

Broken Screw Rotational Symmetry in the Near-Surface Electronic Structure of AB-Stacked Crystals.

We investigate the electronic structure of 2H-NbS_{2} and h-BN by angle-resolved photoemission spectroscopy (ARPES) and photoemission intensity calculations. Although in bulk form, these materials are expected to exhibit band degeneracy in the k_{z}=π/c plane due to screw rotation and time-reversal symmetries, we observe gapped band dispersion near the surface. We extract from first-principles calculations the near-surface electronic structure probed by ARPES and find that the calculated photoemission spectra from the near-surface region reproduce the gapped ARPES spectra. Our results show that the near-surface electronic structure can be qualitatively different from the bulk electronic structure due to partially broken nonsymmorphic symmetries.

Symmetry Engineering in Twisted Bilayer WTe2.

The fabrication of artificial structures using a twisted van der Waals assembly has been a key technique for recent advancements in the research of two-dimensional (2D) materials. To date, various exotic phenomena have been observed thanks to the modified electron correlation or moiré structure controlled by the twist angle. However, the twisted van der Waals assembly has further potential to modulate the physical properties by controlling the symmetry. In this study, we fabricated twisted bilayer WTe2 and demonstrated that the twist angle successfully controls the spatial inversion symmetry and hence the spin splitting in the band structure. Our results reveal the further potential of a twisted van der Waals assembly, suggesting the feasibility of pursuing new physical phenomena in 2D materials based on the control of symmetry.

Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images.

OBJECTIVES: The objective was to evaluate the robustness of deep learning (DL)-based encoder-decoder convolutional neural networks (ED-CNNs) for segmenting temporomandibular joint (TMJ) articular disks using data sets acquired from 2 different 3.0-T magnetic resonance imaging (MRI) scanners using original images and images subjected to contrast-limited adaptive histogram equalization (CLAHE). STUDY DESIGN: In total, 536 MR images from 49 individuals were examined. An expert orthodontist identified and manually segmented the disks in all images, which were then reviewed by another expert orthodontist and 2 expert oral and maxillofacial radiologists. These images were used to evaluate a DL-based semantic segmentation approach using an ED-CNN. Original and preprocessed CLAHE images were used to train and validate the models whose performances were compared. RESULTS: Original and CLAHE images acquired on 1 scanner had pixel values that were significantly darker and with lower contrast. The values of 3 metrics-the Dice similarity coefficient, sensitivity, and positive predictive value-were low when the original MR images were used for model training and validation. However, these metrics significantly improved when images were preprocessed with CLAHE. CONCLUSIONS: The robustness of the ED-CNN model trained on a dataset obtained from a single device is low but can be improved with CLAHE preprocessing. The proposed system provides promising results for a DL-based, fully automated segmentation method for TMJ articular disks on MRI.

Gate-Defined Josephson Weak-Links in Monolayer WTe2.

Systems combining superconductors with topological insulators offer a platform for the study of Majorana bound states and a possible route to realize fault tolerant topological quantum computation. Among the systems being considered in this field, monolayers of tungsten ditelluride (WTe2 ) have a rare combination of properties. Notably, it has been demonstrated to be a quantum spin Hall insulator (QSHI) and can easily be gated into a superconducting state. Measurements on gate-defined Josephson weak-link devices fabricated using monolayer WTe2 are reported. It is found that consideration of the 2D superconducting leads are critical in the interpretation of magnetic interference in the resulting junctions. The reported fabrication procedures suggest a facile way to produce further devices from this technically challenging material and the results mark the first step toward realizing versatile all-in-one topological Josephson weak-links using monolayer WTe2 .

An in vitro study of digital impressions and three-dimensional printed models of orbital defects using mobile devices and monoscopic photogrammetry.

PURPOSE: The purpose of this study was to perform an in vitro evaluation of digital impressions using a mobile device and monoscopic photogrammetry in cases of orbital defects with undercuts. METHODS: Three 10-mm-square cubes were attached to a diagnostic cast of a patient with a right orbital defect. Still images acquired with a mobile device were used to generate facial three-dimensional (3D) data. Two types of still images were used: one was a whole face image, and the other was a defect site-focused image. For comparison, an extraoral scanner was used to obtain facial 3D data. Five dental technicians fabricated 3D printed models using additive manufacturing and measured the distances between the measurement points using a digital caliper. The discrepancy between the distances measured on the diagnostic cast of the patient and the 3D printed model was calculated. Friedman test was used to analyze the discrepancy, and the Bonferroni test was used to verify the differences between the pairs. RESULTS: Statistical significance was found with respect to the type of 3D model fabrication method. CONCLUSION: Within the limitations of this in vitro study, the results suggested that the workflow can be applied to digital impressions of the maxillofacial region.

Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs.

The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" group; panoramic radiographs with no dental anomalies were considered the "control" group. The dataset comprised 150 panoramic radiographs: 50 each of no dental anomalies, single supernumerary teeth, and odontomas. To classify the panoramic radiographs into case and control categories, we employed AlexNet, which is a convolutional neural network model. AlexNet was able to classify whole panoramic radiographs into two or three classes, according to the presence or absence of supernumerary teeth or odontomas. The performance metrics of the three-class classification were 70%, 70.8%, 70%, and 69.7% for accuracy, precision, sensitivity, and F1 score, respectively, in the macro average. These results support the feasibility of using deep learning to detect multiple dental anomalies in panoramic radiographs.

Resonant Tunneling between Quantized Subbands in van der Waals Double Quantum Well Structure Based on Few-Layer WSe2.

We demonstrate van der Waals double quantum well (vDQW) devices based on few-layer WSe2 quantum wells and a few-layer h-BN tunnel barrier. Due to the strong out-of-plane confinement, an exfoliated WSe2 exhibits quantized subband states at the Γ point in its valence band. Here, we report resonant tunneling and negative differential resistance in vDQW at room temperature owing to momentum- and energy-conserved tunneling between the quantized subbands in each well. Compared to single quantum well (QW) devices with only one QW layer possessing quantized subbands, superior current peak-to-valley ratios were obtained for the DQWs. Our findings suggest a new direction for utilizing few-layer-thick transition metal dichalcogenides in subband QW devices, bridging the gap between two-dimensional materials and state-of-the-art semiconductor QW electronics.

Dry pick-and-flip assembly of van der Waals heterostructures for microfocus angle-resolved photoemission spectroscopy.

We present a dry pick-and-flip assembly technique for angle-resolved photoemission spectroscopy (ARPES) of van der Waals heterostructures. By combining Elvacite2552C acrylic resin and 1-ethyl-3-methylimidazolium ionic liquid, we prepared polymers with glass transition temperatures (Tg) ranging from 37 to 100 â„ƒ. The adhesion of the polymer to the 2D crystals was enhanced at [Formula: see text]. By utilizing the difference in [Formula: see text], a 2D heterostructure can be transferred from a high-[Formula: see text] polymer to a lower-[Formula: see text] polymer, which enables flipping its surface upside down. This process is suitable for assembling heterostructures for ARPES, where the top capping layer should be monolayer graphene. The laser-based micro-focused ARPES measurements of 5-layer WTe2, 3-layer MoTe2, 2-layer WTe2/few-layer Cr2Ge2Te6, and twisted double bilayer WTe2 demonstrate that this process can be utilized as a versatile sample fabrication method for investigating the energy spectra of 2D heterostructures.

Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study.

BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. AIM: This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. DESIGN: Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. RESULTS: The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. CONCLUSION: CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.

Resonant Tunneling Due to van der Waals Quantum-Well States of Few-Layer WSe2 in WSe2/h-BN/p+-MoS2 Junction.

Few-layer transition metal dichalcogenides (TMDs) exhibit out-of-plane wave function confinement with subband quantization. This phenomenon is totally absent in monolayer crystals and is regarded as resulting from a naturally existing van der Waals quantum-well state. Because the energy separation between the subbands corresponds to the infrared wavelength range, few-layer TMDs are attractive for their potential to facilitate the application of TMD semiconductors as infrared photodetectors and emitters. Here, we report a few-layer WSe2/h-BN tunnel barrier/multilayer p+-MoS2 tunnel junction to access the quantized subbands of few-layer WSe2 via tunneling spectroscopy measurements. Resonant tunneling and a negative differential resistance were observed when the top of the valence band Γ-point of p+-MoS2 was energetically aligned with one of the empty subbands at the Γ-point of few-layer WSe2. These results demonstrate a critical step toward the utilization of subband quantization in few-layer TMD materials for infrared optoelectronics applications.

Titanium ion induces necrosis and sensitivity to lipopolysaccharide in gingival epithelial-like cells.

Gingival epithelial-like cells (GE-1) were cultured and used to examine the cellular responses of gingival tissues to varying concentrations of titanium (Ti) ions. Titanium ions at concentrations of more than 13 ppm significantly decreased the viability of GE-1 cells and increased LDH release from the cells into the supernatant, but had no significant effect on their caspase 3 activity. These data suggest that a high concentration of Ti ions induced necrosis of the GE-1 cells. Titanium ions at a concentration of 5 ppm significantly increased the level of CCL2 mRNA expression in GE-1 cells exposed to lipopolysaccharide derived from Porphyromonas gingivalis in a synergistic manner. Moreover, the mRNA expression levels of TLR-4 and ICAM-1 in GE-1 cells loaded with Ti ions at 9 ppm were significantly enhanced as compared with those in GE-1 cells without Ti stimulation. We suggest that Ti ions are in part responsible for monocyte infiltration in the oral cavity by elevating the sensitivity of gingival epithelial cells to microorganisms. Taken together, these data indicate that Ti ions may be involved in cytotoxicity and inflammation at the interfaces of dental implants and gingival tissue.