Heteroepitaxial Growth of B5 -Site-Rich Ru Nanoparticles Guided by Hexagonal Boron Nitride for Low-Temperature Ammonia Dehydrogenation.

Ruthenium is one of the most active catalysts for ammonia dehydrogenation and is essential for the use of ammonia as a hydrogen storage material. The B5 -type site on the surface of ruthenium is expected to exhibit the highest catalytic activity for ammonia dehydrogenation, but the number of these sites is typically low. Here, a B5 -site-rich ruthenium catalyst is synthesized by exploiting the crystal symmetry of a hexagonal boron nitride support. In the prepared ruthenium catalyst, ruthenium nanoparticles are formed epitaxially on hexagonal boron nitride sheets with hexagonal planar morphologies, in which the B5 sites predominate along the nanoparticle edges. By activating the catalyst under the reaction condition, the population of B5 sites further increases as the facets of the ruthenium nanoparticles develop. The electron density of the Ru nanoparticles also increases during catalyst activation. The synthesized catalyst shows superior catalytic activity for ammonia dehydrogenation compared to previously reported catalysts. This work demonstrates that morphology control of a catalyst via support-driven heteroepitaxy can be exploited for synthesizing highly active heterogeneous catalysts with tailored atomic structures.

Panoptic Segmentation on Panoramic Radiographs: Deep Learning-Based Segmentation of Various Structures Including Maxillary Sinus and Mandibular Canal.

Panoramic radiographs, also known as orthopantomograms, are routinely used in most dental clinics. However, it has been difficult to develop an automated method that detects the various structures present in these radiographs. One of the main reasons for this is that structures of various sizes and shapes are collectively shown in the image. In order to solve this problem, the recently proposed concept of panoptic segmentation, which integrates instance segmentation and semantic segmentation, was applied to panoramic radiographs. A state-of-the-art deep neural network model designed for panoptic segmentation was trained to segment the maxillary sinus, maxilla, mandible, mandibular canal, normal teeth, treated teeth, and dental implants on panoramic radiographs. Unlike conventional semantic segmentation, each object in the tooth and implant classes was individually classified. For evaluation, the panoptic quality, segmentation quality, recognition quality, intersection over union (IoU), and instance-level IoU were calculated. The evaluation and visualization results showed that the deep learning-based artificial intelligence model can perform panoptic segmentation of images, including those of the maxillary sinus and mandibular canal, on panoramic radiographs. This automatic machine learning method might assist dental practitioners to set up treatment plans and diagnose oral and maxillofacial diseases.

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs.

Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

Top-Down Syntheses of Nickel-Based Structured Catalysts for Hydrogen Production from Ammonia.

We report the fabrication and catalytic performance evaluation of highly active and stable nickel (Ni)-based structured catalysts for ammonia dehydrogenation with nearly complete conversion using nonprecious metal catalysts. Low-temperature chemical alloying (LTCA) followed by selective aluminum (Al) dealloying was utilized to synthesize foam-type structured catalysts ready for implementation in commercial-scale catalytic reactors. The crystalline phases of Ni-Al alloy (NiAl3, Ni2Al3, or both) in the near-surface layer were controlled by tuning the alloying time. The best-performing catalyst was obtained from a Ni foam substrate with a NiAl3/Ni2Al3 overlayer synthesized by LTCA at 400 °C for 20 h. The developed Ni catalyst exhibited an activity enhancement of 10-fold over the nontreated Ni foam and showed outstanding activities of 15 800 molH2molNi-1h-1 (TOF: 4.39 s-1) and 19 978 molH2molNi-1h-1 (TOF: 5.55 s-1) at 550 and 600 °C, respectively. This performance is unprecedented compared with previously reported Ni-based ammonia cracking catalysts with higher-end performance (TOFs of 0.08-1.45 s-1 at 550 °C). Moreover, this catalyst showed excellent stability for 100 h at 600 °C while discharging an extremely low NH3 concentration of 1034 ppm. The NH3 concentration in the exhaust gas was further reduced to 690 and 271 ppm at 700 and 800 °C, respectively, while no deactivation was observed at these elevated temperatures. Through material characterizations, we clarified that controlling the degree of Al alloying in the outermost layer of Ni is a crucial factor in determining the activity and stability because residual Al possibly modifies the electronic structure of Ni for enhanced activity as well as transforming to acidic alumina for increased intrinsic activity and stability.