The Fabrication and Characterization of Ni/4H-SiC Schottky Diode Radiation Detectors with a Sensitive Area of up to 4 cm².

Silicon carbide (SiC) detectors of an Ni/4H-SiC Schottky diode structure and with sensitive areas of 1-4 cm² were fabricated using high-quality lightly doped epitaxial 4H-SiC material, and were tested in the detection of alpha particles and pulsed X-rays/UV-light. A linear energy response to alpha particles ranging from 5.157 to 5.805 MeV was obtained. The detectors were proved to have a low dark current, a good energy resolution, and a high neutron/gamma discrimination for pulsed radiation, showing the advantages in charged particle detection and neutron detection in high-temperature and high-radiation environments.

Graphene and 2D Hexagonal Boron Nitride Heterostructure for Thermal Management in Actively Tunable Manner.

Thermal management is a critical task for highly integrated or high-power semiconductor devices. Low dimensional materials including graphene and single-layer hexagonal boron nitride (BN) are attractive candidates for this task because of their high thermal conductivity, semi-conductivity and other excellent physical properties. The similarities in crystal structure and chemistry between graphene and boron nitride provide the possibility of constructing graphene/BN heterostructures bearing unique functions. In this paper, we investigated the interfacial thermal transport properties of graphene/BN nanosheets via non-equilibrium molecular dynamics (NEMD) simulations. We observed a significant thermal rectification behavior of these graphene/BN nanosheets, and the rectification ratio increased with the system length increases up to 117%. This phenomenon is attributed to the mismatch of out-of-plane phonon vibration modes in two directions at the interface. In addition, we explored the underlying mechanism of the length dependence of the thermal transport properties. The results show promise for the thermal management of this two-dimensional heterostructure in an actively tunable manner.

A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation.

BACKGROUND: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. METHOD: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. RESULTS AND CONCLUSIONS: Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with 77% mAP, 97.5% AP50, and 95.5% AP75 segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.