The CONsortium on Nuclear sECurity Technologies (CONNECT) is funded by the National Nuclear Security Administration Minority Serving Institution Partnership Program (NNSA MSIPP). This program is designed to build a pipeline between the Department of Energy's sites and labs and minority-serving institutions in STEM disciplines and bring a heightened awareness of NNSA plants and laboratories to institutions with a common interest in STEM research fields. Students studying physics, computer science, electrical engineering, and mechanical engineering, among other disciplines, will work together on interdisciplinary research and network with experts in the nuclear security field.

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Recent Submissions

  • Identification of Pd-Si Compounds in Diffusion Couple Studies to Complement AGR PIE reports 

    Stone, Jordan; Cavazos, Steven; Montoya, Katherine; Kirtley, Thomas; George, Gisele; Sooby, Elizabeth, S. (7/28/2022)
    Palladium (Pd) and silver (Ag) are known fission products of TRISO particle nuclear fuels [1]. Pd is known to corrode the silicon carbide (SiC) layer of a TRISO particle, which can compromise the overall fuel performance ...
  • Understanding of Additive Manufacturing with the Combination of the Flexible Manufacturing Process 

    Perkins, Briley; Ewuzie, Emmanuel; Aristizabal, Mauricio; Balcer, Matt; Millwater, Henry (7/28/2022)
    Additive Manufacturing is 3D printing which requires a set of repeating steps throughout the process. STL, or Standard Triangle Language, is the most important step in the printing process that allows the model to be ...
  • Additive Manufacturing & G Code 

    Ewuzie, Emmanuel; Perkins, Briley; Aristizabal, Mauricio; Balcer, Matthew; Millwater, Harry (7/28/2022)
    Additive Manufacturing (AM) is the process of building physical objects by layering materials. It is controlled by G code and a myriad of other codes. CAD models are sliced and converted into a layer by layer code (G Code). ...
  • Strategic Freezing 

    Seligman, Zachary; Patrick, David; Fernandez, Amanda (7/28/2022)
    Convolutional neural networks (CNNs) are notoriously data-intensive, requiring significantly large datasets for training accurately in an appropriate runtime. Recent approaches aiming to reduce this requirement focus ...
  • Identifying Gamma Radiation Anomaly Signals Using Quantum Computation Methods 

    Foate, Joshua; Valdez, Luis; Alamaniotis, Miltos (7/28/2022)
    Collected data using a radiation detector to identify anomalies in the presence of naturally occurring radioactive material. Samples of the anomaly signals were put into a Hopfield neural network to train the network ...
  • Semantic Segmentation for Materials Classification of Nuclear Fuels 

    Mohanadhas, Daniel; Snyder, Chris; Fernandez, Amanda (7/28/2022)
    Semantic segmentation, the task of classifying objects in an image at a pixel level, has been done since 2012. While our method is not new, our application is. Unlike most tasks which are on clearly-defined objects, the ...
  • Probe Beam Deflection Technique as a Characterization Method for Nuclear Materials 

    Carle, Lydia; Escudero, Jose; Flowers, Jacob; Nash, Kelly (7/28/2022)
    Probe beam deflection technique (PBDT) was evaluated as a potential method of characterization for both radioactive and non radioactive materials. This method was chosen for its non destructive nature. The analyte of ...
  • Fabrication of mini UB2 ingots via Arc melt synthesis using a customized copper hearth 

    Facundo, Jesus, U; Montoya, Katherine; Kirtley, Thomas; Sooby, Elizabeth S. (7/28/2022)
    As growing efforts take place to enhance the operational safety of nuclear reactors, fuel composites have been explored as replacement to the traditionally used Uranium dioxide (UO2). One potential candidate that has been ...