Displaying 1 - 3 of 27
|ID#||Organization type||Agency interests||Capabilities offered||Equipment & facilities||Organization area of interest(s)||Other offerings||Partner capabilities sought||Partner type||Partner area of interest(s)||Other partner skills sought|
||We are using fluorescent nitrogen vacancy NV-center diamonds for quantum-based sensing of biomarkers of stress and for quantum-based screening of biomarkers of breast cancer. We are funded by the AFRL and by the National Cancer Institute, respectively, for these projects. We prepare our own NV-center nanodiamonds, and coat them with biocompatible polymers. We conjugate proteins and/or nucleic acids to their surface and use them for quantum sensing (spin-dependent fluorescence). Quantum sensing is through an all optical approach termed optically detected magnetic resonance. We use a custom built device as a magnetometer and have successfully reduced its size as part of a contract with the AFRL. We are currently at TRL 3 and will be at TRL 4 by the end of summer.||
||We prepare fluorescent nitrogen-vacancy nanodiamonds and use them for spin-dependent fluorescence studies. We have a complete wet chemistry lab with fume hoods and other equipment needed for nanomaterials analysis including FTIR, dynamic light scattering, nanodrop, epifluorescence microscope, microbalances, analytical balances, plate reader, furnaces, freezers, refrigerators and a number of small equipment. We have two 3D printers, and micromilling devices. We build our own optical equipment and are using a custom-built confocal spectrometer for fluorescence studies.||We are looking for a government partner and a academic institution that might want to help us with the Student Experiential Learning component. I feel we are a good fit for quantum sensing as we have a physical device and nanomaterials that can perform quantum-based sensing. Not sure if biological sensing is what this topic is looking for. We would appreciate some guidance on this matter from a government partner.||Academic research partner||
||Digital Engineering The team has extensive experience in AI/machine learning and digital engineering, and has recently developed a physics-guided Bayesian neural learning method (considering physical knowledge, learning capability and uncertainty quantification together) for dynamic system modeling and optimization. Areas of interest include: - Application of AI/machine learning in complex engineering systems (such as UAVs, hypersonic propulsion and high-power energy systems) and digital twins of such systems; - Development of trustworthy and explainable AI models, based on the physics-guided Bayesian neural learning method; - Development of engineering workflow deploying the surrogate AI-based models, including model training, validation and integration, real-time simulation, and hardware-in-the loop testing; - Implementation of AI-based surrogate models on high-performance computing platforms such as real-time processors, GPGPUs, FPGAs and quantum computing resources; - Integration and demonstration of AI-driven surrogate models for design and development of systems such as high-power energy systems, hypersonic gradient printed structures, and dc power emulators. Quantum Sensing - Quantum machine learning algorithms and implementation on quantum edge-computing hardware for quantum sensor data processing; - Machine learning algorithms for quantum sensor characterization and adaptive measurement; - A quantum sensor dynamics modeling and simulation framework based on classical or quantum computing resources; - Machine learning driven quantum optimal control software and FPGA-based implementation for quantum sensors and processors; - Leveraging emerging quantum sensor data processing algorithms to solve practical challenging problems of importance to Air Force and NASA missions, such as command and control, air traffic management, risk evaluation, UAV teaming/safety, logistics optimization, etc.||
||Digital engineering platforms and demonstration environment Quantum sensor hardware and computing resources||Both academic and industry partners||
||I am an expert in computational material Science, covering methods such as atomistic simulation (DFT, MD), and continuum models (FEM) to generate synthetic data sets for ML. Also, the physics-based modeling of technologies such as additive manufacturing (AM), alloy design for AM, radiation damage in space, electrical modeling of Beta-Gallium Oxide MOSFETs, thermal modeling, and structural optimization.||Academic research partner|
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