Energy Resolution Upscaling for Radiation Detectors BA-1346

TECHNOLOGY LICENSING OPPORTUNITY Energy Resolution Upscaling for Radiation Detectors A new method capable of yielding high-energy resolution data that usually requires expensive and cryogenically-cooled semiconductor detectors, from inexpensive and room-temperature operated scintillators. Opportunity: Idaho National Laboratory (INL), managed and operated by Battelle Energy Alliance, LLC (BEA), is offering the opportunity to enter into a license and/or collaborative research agreement to commercialize this new energy resolution upscaling technique. This technology transfer opportunity is part of a dedicated effort to convert government-funded research into job opportunities, businesses and ultimately an improved way of life for the American people. Overview: Currently, there are numerous applications in which high-energy resolution radiation detectors are needed to properly yield data with the fidelity required by the user. Proper measurement of an emitted or transmitted particle or photon energy is necessary to produce the science or application that the user requires. Scenarios in which high-fidelity (i.e. high energy resolution) data is required by users generally require expensive and/or complex and sensitive detectors to generate their required data. One such example is with gamma-ray spectroscopy, where high-purity germanium (HPGe) detectors are required due to their extremely good gamma-ray energy resolution. However, there are several complications to HPGe. Their detection efficiency is often very low. Their inability to handle high-count-rate situations (such as in high radiation fields) leads to high dead time and often degradation of the quality of data (i.e. peak deformation) and their requirement to be cooled to cryogenic temperatures limits the cases/locations of their deployment. Description: Researchers at Idaho National Laboratory have developed a solution that offers the ability to resolve high-resolution data from low-resolution detectors (such as high-purity germanium detector-like data from much less expensive and simpler sodium iodide detectors). This solution would allow for the use of higher-efficiency and higher-rate capable detectors while also seeing at least 10x reduction in cost per detector. Benefits: Provides a means of yielding high-energy resolution data from inexpensive and room-temperature operated scintillators. Greatly reduces cost and complexity of developing measurement systems. Enables measurements that cannot possibly be achieved with current analysis methods, including, but not limited to: Special nuclear material tracking Space-based measurements Confined or remote nuclear tracking or monitoring, Greatly increases the accuracy of modern dose-rate monitoring systems. Applications: Gamma-ray or X-ray Spectroscopy X-ray Technicians Quality control engineers Detector operators that rely upon the accurate measurement of gamma-/x-rays, neutrons, or other particles. Development Status: TRL 5, technology has been demonstrated in a laboratory environment with real-world conditions. IP Status: US Patent Application No. 17/649,031, Increasing Energy Resolution, and Related Methods, Systems, and Devices, BEA Docket No. BA-1346. INL is seeking to license the above intellectual property to a company with a demonstrated ability to bring such inventions to the market. Exclusive rights in defined fields of use may be available. Added value is placed on relationships with small businesses, start-up companies, and general entrepreneurship opportunities. Please visit Technology Deployment s website at https://inl.gov/inl-initiatives/technology-deployment for more information on working with INL and the industrial partnering and technology transfer process. Companies interested in learning more about this licensing opportunity should contact Andrew Rankin at td@inl.gov.

Machine Learning-Enhanced Spectroscopy Technology for High-Resolution Radiation Detection Using Low-Cost Detectors Transforms low-energy resolution gamma- and x-ray detector data into high-resolution spectra reducing cost, size, and cooling requirements without sacrificing performance. Technology Summary This INL technology enables high-energy-resolution radiation spectroscopy using low-cost, room-temperature detectors such as sodium iodide (NaI) scintillators. Traditionally, researchers and engineers rely on high-purity germanium (HPGe) detectors, lanthanum bromide (LaBr3) or similar for applications requiring fine energy discrimination; however, these systems are expensive, fragile, or require cryogenic cooling. The presented approach applies a compact convolutional neural network (CNN) architecture to reconstruct high-energy-resolution spectra from low-resolution measurements. Using four convolution-max pooling layer pairs (128 16 filters) followed by dense layers, the model captures spectral features typically only visible with HPGe detectors. The network contains roughly 1.6 million parameters (6.2 MB total), enabling fast, portable deployment in embedded or field devices. The technology offers a new analytical pathway for radiation spectroscopy maintaining data fidelity while reducing total system cost, weight, and operational complexity. Problem Addressed High cost and complexity of high-energy-resolution detectors: HPGe systems provide excellent energy resolution (~0.2%) but are 10 100 more expensive than scintillation-based systems. Limited operational flexibility: HPGe detectors require cryogenic cooling and are unsuitable for mobile or high-radiation environments. Low detection efficiency and count-rate performance: HPGe detectors have lower detection efficiency per detector volume and cannot handle high count rates without peak deformation or detector dead time, leading to data degradation. Restricted deployment scenarios: Field, space-based, and confined monitoring applications require detectors that are robust, efficient, and thermally independent. Solution Data-driven energy resolution enhancement: Employs a convolutional neural network to reconstruct high-resolution spectra from low-resolution detector inputs. Compact, deployable model: 1.6M-parameter neural network (6.2 MB) allows rapid inference on low-power devices. Detector-agnostic implementation: Can be adapted for gamma, x-ray, neutron, or charged-particle spectroscopy. Scalable to various hardware: Applicable to NaI, CsI, or plastic scintillators, enabling energy peak discrimination comparable to HPGe without cryogenic operation. Key Advantages Cost Reduction: Enables ?10 lower system cost and maintenance by replacing HPGe with NaI or other inexpensive detectors. Operational Simplicity: Eliminates need for liquid nitrogen or cryogenic cooling systems. Higher Throughput: Supports higher count rates with minimal peak deformation. Improved Deployability: Suitable for remote, field, and mobile environments where HPGe is impractical. Cross-Technology Applicability: Adaptable for gamma-ray, x-ray, and neutron detection systems. Market Applications Nuclear materials monitoring and safeguards real-time isotope discrimination without cryogenic infrastructure. Space-based radiation detection lightweight, low-power alternative to HPGe for satellite payloads. Industrial quality control and non-destructive testing improved spectral resolution using existing NaI-based systems. Medical and environmental radiation monitoring portable spectrometers with enhanced fidelity for imaging and dosimetry. Homeland security and defense deployable gamma-ray detection for special nuclear material tracking. This notice is not a solicitation for funding or a commitment by DOE/INL to procure services. Rather, it is intended solely to notify industry of an INL technology available for licensing and commercialization.

Machine Learning-Enhanced Spectroscopy Technology for High-Resolution Radiation Detection Using Low-Cost Detectors Transforms low-energy resolution gamma- and x-ray detector data into high-resolution spectra reducing cost, size, and cooling requirements without sacrificing performance. Technology Summary This INL technology enables high-energy-resolution radiation spectroscopy using low-cost, room-temperature detectors such as sodium iodide (NaI) scintillators. Traditionally, researchers and engineers rely on high-purity germanium (HPGe) detectors, lanthanum bromide (LaBr3) or similar for applications requiring fine energy discrimination; however, these systems are expensive, fragile, or require cryogenic cooling. The presented approach applies a compact convolutional neural network (CNN) architecture to reconstruct high-energy-resolution spectra from low-resolution measurements. Using four convolution-max pooling layer pairs (128 16 filters) followed by dense layers, the model captures spectral features typically only visible with HPGe detectors. The network contains roughly 1.6 million parameters (6.2 MB total), enabling fast, portable deployment in embedded or field devices. The technology offers a new analytical pathway for radiation spectroscopy maintaining data fidelity while reducing total system cost, weight, and operational complexity. Problem Addressed High cost and complexity of high-energy-resolution detectors: HPGe systems provide excellent energy resolution (~0.2%) but are 10 100 more expensive than scintillation-based systems. Limited operational flexibility: HPGe detectors require cryogenic cooling and are unsuitable for mobile or high-radiation environments. Low detection efficiency and count-rate performance: HPGe detectors have lower detection efficiency per detector volume and cannot handle high count rates without peak deformation or detector dead time, leading to data degradation. Restricted deployment scenarios: Field, space-based, and confined monitoring applications require detectors that are robust, efficient, and thermally independent. Solution Data-driven energy resolution enhancement: Employs a convolutional neural network to reconstruct high-resolution spectra from low-resolution detector inputs. Compact, deployable model: 1.6M-parameter neural network (6.2 MB) allows rapid inference on low-power devices. Detector-agnostic implementation: Can be adapted for gamma, x-ray, neutron, or charged-particle spectroscopy. Scalable to various hardware: Applicable to NaI, CsI, or plastic scintillators, enabling energy peak discrimination comparable to HPGe without cryogenic operation. Key Advantages Cost Reduction: Enables ?10 lower system cost and maintenance by replacing HPGe with NaI or other inexpensive detectors. Operational Simplicity: Eliminates need for liquid nitrogen or cryogenic cooling systems. Higher Throughput: Supports higher count rates with minimal peak deformation. Improved Deployability: Suitable for remote, field, and mobile environments where HPGe is impractical. Cross-Technology Applicability: Adaptable for gamma-ray, x-ray, and neutron detection systems. Market Applications Nuclear materials monitoring and safeguards real-time isotope discrimination without cryogenic infrastructure. Space-based radiation detection lightweight, low-power alternative to HPGe for satellite payloads. Industrial quality control and non-destructive testing improved spectral resolution using existing NaI-based systems. Medical and environmental radiation monitoring portable spectrometers with enhanced fidelity for imaging and dosimetry. Homeland security and defense deployable gamma-ray detection for special nuclear material tracking. This notice is not a solicitation for funding or a commitment by DOE/INL to procure services. Rather, it is intended solely to notify industry of an INL technology available for licensing and commercialization.