10 research outputs found
Application of Deep Neural Network to an Accelerated Prediction of a Severe Accident in Nuclear Power Plants
No full textRecent nuclear severe accidents have spurred interest in the development of advanced accident management support tools (AMSTs) to enhance decision-making during crises. This study examines the efficacy of deep neural networks (DNNs) in accelerating severe accident predictions within nuclear power plants (NPPs), focusing on a loss-of-component-cooling-water (LOCCW) accident scenario. Through analysis of 10,780 simulated LOCCW accident scenarios across varied component failures and mitigation strategy implementations, time series datasets were synthesized at 15, 30, and 60-min intervals. The evaluation demonstrated that convolutional neural network (CNN)-integrated models outperformed standalone architectures in prediction accuracy across all temporal resolutions. Notably, higher temporal resolutions in training datasets significantly improved mean absolute error (MAE) and root mean squared error (RMSE), thereby enhancing prediction precision for immediate subsequent time steps. However, the augmentation of temporal resolution did not uniformly improve overall scenario prediction performance, as assessed by dynamic time warping (DTW) distance, due to cumulative prediction error in higher resolution models. These findings elucidate the nuanced relationship between temporal resolution and predictive accuracy, offering valuable insights for the development of sophisticated AMSTs aimed at bolstering nuclear safety and accident management strategies.
Leveraging explainable AI for reliable prediction of nuclear power plant severe accident progression
No full textPast severe accidents have highlighted the importance of reducing human error by operators in accident situations. To support operators, machine learning-based accident management support tools have been proposed due to its rapid computation and generalization capabilities. However, the lack of explainability in these models, often perceived as "black-boxes," remains a significant challenge. To address this issue, Explainable AI (XAI) techniques are being integrated across various domains. This study evaluates the applicability of XAI techniques in predicting the state of the OPR1000 reactor during a subset scenario of total-loss-of-component-cooling-water accident with dynamic random failure assumption. Accident scenarios, including various safety component failures and mitigation strategies, were simulated using the Modular Accident Analysis Program (MAAP) code. Two types of XAI techniques-Shapley Additive Explanations (SHAP) and attention-based architectures-are tested alongside conventional black-box models. The results demonstrate that relationships among thermalhydraulic variables can be explained via feature importance, and that the impacts of component failures and mitigation strategies are phenomenologically explainable. Additionally, the study highlights the importance of robust, domain knowledge-based data engineering.
MELCOR Simulation of Effects of Cavity Flooding Strategy on the RPV Failure and Containment Leak for OPR1000
No full textThis work was supported by the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety (KOFONS), granted financial resource from the Nuclear Safety and Security Commission(NSSC), Republic of Korea (No. 1403002
Remote epitaxy through graphene enables two-dimensional material-based layer transfer
No full textEpitaxy-the growth of a crystalline material on a substrate-is crucial for the semiconductor industry, but is often limited by the need for lattice matching between the two material systems. This strict requirement is relaxed for van der Waals epitaxy(1-10), in which epitaxy on layered or two-dimensional (2D) materials is mediated by weak van der Waals interactions, and which also allows facile layer release from 2D surfaces(3,8). It has been thought that 2D materials are the only seed layers for van der Waals epitaxy(3-10). However, the substrates below 2D materials may still interact with the layers grown during epitaxy (epilayers), as in the case of the so-called wetting transparency documented for graphene(11-13). Here we show that the weak van der Waals potential of graphene cannot completely screen the stronger potential field of many substrates, which enables epitaxial growth to occur despite its presence. We use density functional theory calculations to establish that adatoms will experience remote epitaxial registry with a substrate through a substrate-epilayer gap of up to nine angstroms; this gap can accommodate a monolayer of graphene. We confirm the predictions with homoepitaxial growth of GaAs(001) on GaAs(001) substrates through monolayer graphene, and show that the approach is also applicable to InP and GaP. The grown single-crystalline films are rapidly released from the graphene-coated substrate and perform as well as conventionally prepared films when incorporated in light-emitting devices. This technique enables any type of semiconductor film to be copied from underlying substrates through 2D materials, and then the resultant epilayer to be rapidly released and transferred to a substrate of interest. This process is particularly attractive in the context of non-silicon electronics and photonics, where the ability to re-use the graphene-coated substrates(8) allows savings on the high cost of non-silicon substrates.
Bilayer Interdiffused Heterojunction Organic Photodiodes Fabricated by Double Transfer Stamping
No full textPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/1/adom201600784-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/2/adom201600784_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/3/adom201600784.pd
Reliability of Mixed‐Heterojunction Organic Photovoltaics Grown via Organic Vapor Phase Deposition
No full textPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111173/1/aenm201401952.pd
High-throughput manufacturing of epitaxial membranes from a single wafer by 2D materials-based layer transfer process
No full textLayer transfer techniques have been extensively explored for semiconductor device fabrication as a path to reduce costs and to form heterogeneously integrated devices. These techniques entail isolating epitaxial layers from an expensive donor wafer to form freestanding membranes. However, current layer transfer processes are still low-throughput and too expensive to be commercially suitable. Here we report a high-throughput layer transfer technique that can produce multiple compound semiconductor membranes from a single wafer. We directly grow two-dimensional (2D) materials on III–N and III–V substrates using epitaxy tools, which enables a scheme comprised of multiple alternating layers of 2D materials and epilayers that can be formed by a single growth run. Each epilayer in the multistack structure is then harvested by layer-by-layer mechanical exfoliation, producing multiple freestanding membranes from a single wafer without involving time-consuming processes such as sacrificial layer etching or wafer polishing. Moreover, atomic-precision exfoliation at the 2D interface allows for the recycling of the wafers for subsequent membrane production, with the potential for greatly reducing the manufacturing cost. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.11Nsciescopu
ELTD1 as a multi-focal target for malignant gliomas: Preclinical studies
No full text© 2021 The Author(s). Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.Background: Glioblastoma (GBM) is the most aggressive malignant primary brain tumor in adults. These high-grade gliomas undergo unregulated vascular angiogenesis, migration and cell proliferation allowing the tumor cells to evade cell-cycle checkpoints and apoptotic pathways. The Epidermal growth factor, latrophilin, and seven transmembrane domain-containing 1 on chromosome 1 (ELTD1) is an angiogenic biomarker that is highly expressed in malignant gliomas. Novel treatments targeting ELTD1 with monovalent monoclonal (mmAb) and single chain variable fragment (scFv) antibodies were effective in increasing animal survival, decreasing tumor volume and normalizing the vasculature. Due to the success of our antibody treatments on angiogenesis, this study sought to determine if our anti-ELTD1 treatments affected other aspects of tumorigenesis (cell proliferation, migration, and apoptosis) in a G55 glioma xenograft preclinical mouse model. Methods: Tumor tissue from untreated, mmAb and scFv anti-ELTD1 treated animals was used to quantify the positivity levels of human mitochondrial antibody, c-MET and Ki-67 for cellular proliferation, migratory markers CD44v6, TRPM8, and BMP2, and cleaved caspase 3 to assess apoptotic activity. Results: This approach demonstrated that our anti-ELTD1 treatments directly affected and decreased the human tumor cells within the tumor region. Additionally, there was a significant decrease in both cellular proliferation and migration due to anti-ETLD1 therapy. Lastly, anti-ELTD1 treatments successfully increased apoptotic activity within the tumor region. Conclusion: Our data suggest that anti-ELTD1 therapies would be effective against malignant gliomas by having a multi-focal effect and targeting all four aspects of tumorigenesis.N
A Fully-Human Antibody Specifically Targeting a Membrane-Bound Fragment of CADM1 Potentiates the T Cell-Mediated Death of Human Small-Cell Lung Cancer Cells
No full textSmall-cell lung cancer (SCLC) is the most aggressive form of lung cancer and the leading cause of global cancer-related mortality. Despite the earlier identification of membrane-proximal cleavage of cell adhesion molecule 1 (CADM1) in cancers, the role of the membrane-bound fragment of CAMD1 (MF-CADM1) is yet to be clearly identified. In this study, we first isolated MF-CADM1-specific fully human single-chain variable fragments (scFvs) from the human synthetic scFv antibody library using the phage display technology. Following the selected scFv conversion to human immunoglobulin G1 (IgG1) scFv-Fc antibodies (K103.1–4), multiple characterization studies, including antibody cross-species reactivity, purity, production yield, and binding affinity, were verified. Finally, via intensive in vitro efficacy and toxicity evaluation studies, we identified K103.3 as a lead antibody that potently promotes the death of human SCLC cell lines, including NCI-H69, NCI-H146, and NCI-H187, by activated Jurkat T cells without severe endothelial toxicity. Taken together, these findings suggest that antibody-based targeting of MF-CADM1 may be an effective strategy to potentiate T cell-mediated SCLC death, and MF-CADM1 may be a novel potential therapeutic target in SCLC for antibody therapy
Vertically stacked full color micro-LEDs via two-dimensional material-based layer transfer
No full textMicro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance1,2. However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density3,4,5,6. Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down7,8,9,10,11,12,13,14, but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique15,16,17,18 that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. These results establish routes to creating full-colour µLED displays for augmented and virtual reality, while also offering a generalizable platform for broader classes of three-dimensional integrated devices.Y
