Advances in High-Resolution Radiation Detection Using 4H-SiC Epitaxial Layer Devices

Advances towards achieving the goal of miniature 4H-SiC based radiation detectors for harsh environment application have been studied extensively and reviewed in this article. The miniaturized devices were developed at the University of South Carolina (UofSC) on 8 × 8 mm 4H-SiC epitaxial layer wafers with an active area of ≈11 mm2. The thicknesses of the actual epitaxial layers were either 20 or 50 µm. The article reviews the investigation of defect levels in 4H-SiC epilayers and radiation detection properties of Schottky barrier devices (SBDs) fabricated in our laboratories at UofSC. Our studies led to the development of miniature SBDs with superior quality radiation detectors with highest reported energy resolution for alpha particles. The primary findings of this article shed light on defect identification in 4H-SiC epilayers and their correlation with the radiation detection properties

A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for Radioisotope Identification

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data

Neurodevelopmental disorders in children aged 2-9 years: Population-based burden estimates across five regions in India.

BACKGROUND: Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. METHODS AND FINDINGS: We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. CONCLUSIONS: The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions

High Performance Pd/4H-SiC Epitaxial Schottky Barrier Radiation Detectors for Harsh Environment Applications

Although many refractory metals have been investigated as the choice of contact metal in 4H-SiC devices, palladium (Pd) as a Schottky barrier contact for 4H-SiC radiation detectors for harsh environment applications has not been investigated adequately. Pd is a refractory metal with high material weight-to-thickness ratio and a work function as high as nickel, one of the conventional metal contacts for high performing 4H-SiC Schottky barrier detectors (SBDs). In this article, Pd/4H-SiC epitaxial SBDs have been demonstrated for the first time as a superior self-biased (0 V applied bias) radiation detector when compared to benchmark Ni/4H-SiC SBDs. The Pd/4H-SiC SBD radiation detectors showed a very high energy resolution of 1.9% and 0.49% under self- and optimized bias, respectively, for 5486 keV alpha particles. The SBDs demonstrated a built-in voltage (Vbi) of 2.03 V and a hole diffusion length (Ld) of 30.8 µm. Such high Vbi and Ld led to an excellent charge collection efficiency of 76% in the self-biased mode. Capacitance mode deep level transient spectroscopy (DLTS) results revealed that the “lifetime-killer” Z1/2 trap centers were present in the 4H-SiC epilayer. Another deep level trap was located at 1.09 eV below the conduction band minimum and resembles the EH5 trap with a concentration of 1.98 × 1011 cm−3 and capture cross-section 1.7 × 10−17 cm−2; however, the detector performance was found to be limited by charge trapping in the Z1/2 center. The results presented in this article revealed the unexplored potential of a wide bandgap semiconductor, SiC, as high-efficiency self-biased radiation detectors. Such high performance self-biased radiation detectors are poised to address the longstanding problem of designing self-powered sensor devices for harsh environment applications e.g., advanced nuclear reactors and deep space missions

Deep Learning-Based Classification of Gamma Photon Interaction in Room-Temperature Semiconductor Radiation Detectors

Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promising detectors is the wide bandgap room temperature semiconductor detectors, which depends on the interaction gamma/x-ray photons with the detector material involves Compton scattering which leads to multiple interaction photon events (MIPEs) of a single photon. For semiconductor detectors like CdZnTeSe (CZTS), which have a high overlap of detected energies between Compton and photoelectric events, it is nearly impossible to distinguish between Compton scattered events from photoelectric events using conventional readout electronics or signal processing algorithms. Herein, we report a deep learning classifier CoPhNet that distinguishes between Compton scattering and photoelectric interactions of gamma/x-ray photons with CdZnTeSe (CZTS) semiconductor detectors. Our CoPhNet model was trained using simulated 662 keV samples to resemble actual CZTS detector pulses and validated using both simulated and experimental data. The model remarkably exhibited a 100&#x0025; accuracy in predicting the type of interaction. These results demonstrated that our CoPhNet model can achieve high classification accuracy over the simulated test set. It also holds its performance robustness under operating parameter shifts such as Signal-Noise-Ratio (SNR) and incident energy. Our work thus show a positive direction for developing next-generation high energy gamma-rays detectors for better biomedical imaging

Quaternary Semiconductor Cd1−xZnxTe1−ySey for High-Resolution, Room-Temperature Gamma-Ray Detection

The application of Cd0.9Zn0.1Te (CZT) single crystals, the primary choice for high-resolution, room-temperature compact gamma-ray detectors in the field of medical imaging and homeland security for the past three decades, is limited by the high cost of production and maintenance due to low detector grade crystal growth yield. The recent advent of its quaternary successor, Cd0.9Zn0.1Te1−ySey (CZTS), has exhibited remarkable crystal growth yield above 90% compared to that of ~33% for CZT. The inclusion of Se in appropriate stoichiometry in the CZT matrix is responsible for reducing the concentration of sub-grain boundary (SGB) networks which greatly enhances the compositional homogeneity and growth yield. SGB networks also host defect centers responsible for charge trapping, hence their reduced concentration ensures minimized charge trapping. Indeed, CZTS single crystals have shown remarkable improvement in electron charge transport properties and energy resolution over CZT detectors. However, our studies have found that the overall charge transport in CZTS is still limited by the hole trapping. In this article, we systematically review the advances in the CZTS growth techniques, its performance as room-temperature radiation detector, and the role of defects and their passivation studies needed to improve the performance of CZTS detectors further

Immunization with <i>Brucella abortus</i> S19Δ<i>per</i> Conferred Protection in Water Buffaloes against Virulent Challenge with <i>B. abortus</i> Strain S544

Vaccination of cattle and buffaloes with Brucella abortus strain 19 has been the mainstay for control of bovine brucellosis. However, vaccination with S19 suffers major drawbacks in terms of its safety and interference with serodiagnosis of clinical infection. Brucella abortus S19∆per, a perosamine synthetase wbkB gene deletion mutant, overcomes the drawbacks of the S19 vaccine strain. The present study aimed to evaluate the potential of Brucella abortus S19Δper vaccine candidate in the natural host, buffaloes. Safety of S19∆per, for animals use, was assessed in guinea pigs. Protective efficacy of vaccine was assessed in buffaloes by immunizing with normal dose (4 × 1010 colony forming units (CFU)/animal) and reduced dose (2 × 109 CFU/animal) of S19Δper and challenged with virulent strain of B. abortus S544 on 300 days post immunization. Bacterial persistency of S19∆per was assessed in buffalo calves after 42 days of inoculation. Different serological, biochemical and pathological studies were performed to evaluate the S19∆per vaccine. The S19Δper immunized animals showed significantly low levels of anti-lipopolysaccharides (LPS) antibodies. All the immunized animals were protected against challenge infection with B. abortus S544. Sera from the majority of S19Δper immunized buffalo calves showed moderate to weak agglutination to RBPT antigen and thereby, could apparently be differentiated from S19 vaccinated and clinically-infected animals. The S19Δper was more sensitive to buffalo serum complement mediated lysis than its parent strain, S19. Animals culled at 6-weeks-post vaccination showed no gross lesions in organs and there was comparatively lower burden of infection in the lymph nodes of S19Δper immunized animals. With attributes of higher safety, strong protective efficacy and potential of differentiating infected from vaccinated animals (DIVA), S19Δper would be a prospective alternate to conventional S19 vaccines for control of bovine brucellosis as proven in buffaloes

Synthesis of CdZnTeSe Single Crystals for Room Temperature Radiation Detector Fabrication: Mitigation of Hole Trapping Effects Using a Convolutional Neural Network

We report the growth of Cd0.9Zn0.1Te0.97Se0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd0.9Zn0.1Te0.97Se0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored