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Contract Opportunity

TECHNOLOGY LICENSING OPPORTUNITY

Radiation Spectroscopy and Isotope Identification Using Machine Learning

A new radiation spectroscopy technique using machine learning to achieve highly accurate measurements in real time with reduced or eliminated human factor.

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 radiation spectroscopy 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: Various industries including nuclear, medical, and non-proliferation, have been using radiation spectroscopic analysis for decades. Despite its wide use, this technology has always struggled to provide accurate spectroscopy measurements for their applications. Efforts have been made, and continue, to improve the mathematical models for peak-fitting and subsequent analysis but the core concept of spectroscopy remains the same. Conventional methods commonly take many days to perform because of the substantial human factor involved in the analysis, just for the analysis to be inaccurate. All of these factors contribute to an increase in the financial and time expense, which leads user to either accept lower quality results, or forgo measurements all together.

Description: Researchers at INL have developed a new method for spectroscopy that relies on networks of nonlinear, hierarchical learning functions like those used for image-classification. This process can identify and quantify the presence of radioisotopes by processing collected radiation spectra using advanced machine learning. Improvements in machine learning capabilities make it possible to design a technique which allows the computer to act in a similar fashion to an expert scientist analyzing a radiation spectrum. This method would not only substantially improve the capabilities of radiation spectra analysis but would also reduce or even eliminate the human factor involved with the conventional approach.

Benefits:

• Using machine learning immunizes this method from uncertainties introduced by improper peak fitting or template matching involved in traditional methods.


• The results can be inferred in real time by a single forward pass of the trained network of learning functions, rather than a two-step collect, then analyze process like traditional methods.


• Real time analysis substantially reduces the time required to obtain the desired results from measurements.

Applications:

• Any radiation spectroscopic analysis including, but not limited to:
  
  
  
  • Neutron
  
  
  • Gamma Ray
  
  
  • X-ray
  
  
  
  

Development Status: TRL 3. Proof-of-concept has been completed via a prototype experiment.

IP Status: Provisional Patent Application No. 63/063,183, “Machine-Learned Spectrum Analysis,” BEA Docket No. BA-1172

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.

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