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Intellectual Property

Below is a comprehensive list of articles, events, projects, references and research related content that is specific to the term described above. Use the filter to narrow the results further. To explore additional science and technology topics that Argonne researchers and engineers may be working on please visit our Research Index.

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  • The inventors have developed a technique to substantially increase room temperature gamma radiation detection and yield ratio in these materials.
    Intellectual Property Available to License

    Please contact us for additional information.

  • This invention introduces a series of lithium-containing semiconductors for detecting thermalized neutrons.
    Intellectual Property Available to License
    US Patent Application US17/252,776
    • Lithium-containing chalcophosphates for thermal neutron detection


    This invention introduces a series of lithium-containing semiconductors LiMP2Q6 (M = In, Bi, Sb, As, Al, Ga; Q = S, Se, Te) for detecting thermalized neutrons. Lithium may be enriched lithium-6 or natural lithium. The very high resistivity of this compound allows for large-area detector and higher applied voltage on the compound, allowing for increased efficiency and gamma-ray discrimination.


    This material has the capacity to detect thermal neutrons with improved sensitivity and selectivity (thermal neutrons versus gamma rays) over prior art.

  • Advanced R&D, integration, and commercialization of polymer refractive X-ray optical components
    Intellectual Property Available to License
    US Patent 17/039,624
    • Method of Printing and Implementing Refractive X-Ray Optical Components (ANL-IN-20-070)


    Using high-resolution polymerization lithography, this technology enables rapid and cost-efficient printing of refractive X-ray optics, such as phase correctors and compound refractive lenses (CRLs) with a better-than-100 nm printing resolution. These optics have shown a higher quality and better performance than conventional lenses, such as those commercially available Be CRLs. Supported on the small flat substrates, these lenses can be quickly deployed into an X-ray beam delivery system.

    Opportunity & Solution

    To fully utilize DOE’s high coherence of the diffraction-limited X-ray sources, there is an urgent need for a giant leap forward in the manufacturing capabilities of X-ray refractive optics that are required for controllable wavefronts in the applications of coherent-based experiment methods. To meet these goals, Argonne National Laboratory researchers have been developing a customizable strategy to manufacture and deploy polymer-based refractive X-ray optics at synchrotron beamlines.


    • Improve multiple polymerization printing lithography schemes for better lens quality and shape controls.
    • Improve the printing resolution to 20-50 nm.
    • Scale-up the procedure for high-throughput optics fabrication, aiming at a useful lens array set per one to a few hours.
    • Investigate high-energy (>20 kev) and high-coherence applications of printed optics.
    • Design commercialization-ready assembly scheme for stand-alone instrument that allows fast optics alignment and flexible operation to meet various experimental requirements at synchrotron beamlines.
    • Develop a cost-effective transfocator mechanism for rapid lens exchange and replacement.
  • This invention comprises a predictive machine learning framework that leverages high performance computing and high-level quantum calculations to predict, determine, and validate phase diagrams in chemical systems without any recourse to experimental info
    Intellectual Property Available to License
    US Patent 16/807,081
    • Systems and Methods for Generating Phase Diagrams for Metastable Material States (ANL-IN-19-012)

    This invention introduces a new paradigm for materials science and solid-state chemistry by moving away from an explorative experimental synthesis, followed by phenomenological modeling approach, towards a predictive process by identifying phase space where metastable states occur from machine learning and first principles.

  • This invention comprises the introduction of an active learning method that starts training a neural network with a single” data point.
    Intellectual Property Available to License
    US Patent 16/847,098
    • Active Learning From Sparse Training Data (ANL-IN-19-013)

    Please contact us for additional information

  • This invention addresses a critical issue of multiple objective optimization and is a general approach applicable to all types of model or property optimization.
    Intellectual Property Available to License
    US Patent 16/704,936
    • Systems and Methods for Hierarchical Multi-Objective Optimization (ANL-IN-19-010)

    This invention comprises a solution to the problem of simultaneous optimization of multiple objectives by creating a hierarchy of objectives with passing criteria” which allows an optimization algorithm to understand the relative importance of the objectives and also provides a pathway for it to tackle the list of objectives in a systematic baby-stepping manner that is based on well defined target values rather than arbitrary weights.

  • Process and apparatus for mixed solid separations, including the separation of metal from plastic and metal from metal.
    Intellectual Property Available to License
    US Patent 7,954,642


    Argonne’s full-scale mixed solid separation module. Available for collaborative demonstration projects. IP package includes a granted US patent and a set of copyrighted engineering manuals.

    Opportunity & Solution

    Electrical and electronic waste (E-waste) is a fast-growing segment of the global solid waste stream. Recycled materials such as metals, plastic and glass formed the largest market share. This technology has been demonstrated to significantly decrease contamination of low-grade E-waste plastic and metal recycling fractions.


    ▪ Provides new opportunities to increase E-waste value

    ▪ Low cost

    ▪ Easy to operate

    ▪ Modular design (basic sink/float; kinetic separation; froth flotation)

  • This software uses trained machine learning algorithms to classify features in a scanning tunneling microscopy (STM) topography, and feeds the results to a custom automated atomic manipulation subroutine, thereby circumventing the requirement for operator
    Intellectual Property Available to License

    Invention Opportunity & Solution


    • This program will allow for the building of complex structures that were previously unattainable.
    • The automation will save the operator time.


    Applications, Industries

    • Scanning probe microscopy software development
  • A smart charging system for charging a plug-in electric vehicle (PEV) includes an electric vehicle supply equipment (EVSE) configured to supply electrical power to the PEV through a smart charging module coupled to the EVSE.
    Intellectual Property Available to License
    US Patent 9,758,046

    A smart charging system for charging a plug-in electric vehicle (PEV) includes an electric vehicle supply equipment (EVSE) configured to supply electrical power to the PEV through a smart charging module coupled to the EVSE. The smart charging module comprises an electronic circuitry which includes a processor. The electronic circuitry includes electronic components structured to receive electrical power from the EVSE, and supply the electrical power to the PEV. The electronic circuitry is configured to measure a charging parameter of the PEV. The electronic circuitry is further structured to emulate a pulse width modulated signal generated by the EVSE. The smart charging module can also include a first coupler structured to be removably couple to the EVSE and a second coupler structured to be removably coupled to the PEV.

  • Polybot is a modular self-driving laboratory software environment that combines artificial intelligence (AI), automation, and experimentations. The robotics software of Polybot facilitates the rapid setup of hardware modules, implementation of experimenta
    Intellectual Property Available to License

    A flexible user scripting interface for the definitions of robot-performed experimental procedures

    Opportunity & Solution

    Autonomous experiments require orchestrating robotic movements and functions of hardware modules to perform a sequence of experimental steps designed and specified by a user who is familiar with the type of experiments (peptide synthesis, nanostructure characterization, etc.), but not necessarily accustomed to robotics hardware or computer programming. Our workflow scripting interface utilizes the Python programming language to simplify the scripting of hardware control sequences, and organizes them into logical entries that represent experimental steps. The entire experimental procedure is encompassed within a single script, allowing easy archival. The design enables sophisticated backend processing of the workflow script to enable seamless integration with machine learning/optimization frameworks and the robotics system control. Since the experimental procedure is presented in a form that resembles the original hardware control sequences, a wide range of experimental workflows and hardware configurations can be programmed with ease due to the readability and flexibility of the Python scripting language.


    • flexible scripting (no hidden codes)
    • experimental steps embedded clearly in control sequences
    • minimal programming skills required


    A ML-based scheduler for concurrent sample processing in autonomous experiments

    Opportunity & Solution

    A scheduler is as a software component that orchestrates the execution of steps (e.g., robot arm movement, solution stirring, temperature annealing, etc.) for performing an autonomous experiment. It is challenging to build a scheduler that enables seamless integration of non-vendor specific hardware and can efficiently optimize the execution order of steps in an experiment involving multiple hardware modules and concurrent processing of samples. Our software comprises a new scheduler using the widely used Python programming language. Since it is built using a general language that is increasingly common in the robotics and scientific fields, one can easily couple it with script-based hardware APIs and utilize machine learning to provide automatic data-driven tuning and hardware-aware scheduling of experimental steps. The ease of integration with existing software codes enables faster progress to be made across laboratories and reduces the cost of autonomous systems thus making them available to less well-funded laboratories.


    • Enables high-throughput material processing and synthesis
    • Leverages machine learning for closed-loop experiments
    • Fully autonomous


    Data structure and data workflow for handling material samples in high-throughput experiments

    Opportunity & Solution

    High-throughput experiments enabled by robotics generate large volumes of heterogeneous data, coming from multiple hardware modules in different data formats. There needs to be an easy way to standardize data and store them in a universal format such that data and metadata of individual material samples are well organized. This software consists of a material sample data class/object that utilizes JSON file in the backend for data storage. The sample data class is implemented using the Python programming language. It couples the flexibility of JSON files with rigid schema to make data files compatible with database handling. There are also functions for smart creation of unique sample ID based on workflow and input parameters, as well as handling the underlying data writing and conversion. Instead of the traditional approach of specifying file paths within an experimental workflow, we developed an object/data-oriented approach that initiates actions from the sample data class, providing an elegant way of handling the file path related issues in concurrent/parallel processing of material samples.


    • Designed to handle autonomous workflow
    • User-friendly content addition and editing
    • Data hierarchy designed for material samples


    Inventions, Applications, Industries:

    • Autonomous material discovery
    • Combinatorial experiments
    • High-dimensional processing-property relationships


    Lab automation:

    • Facilitates integration solutions for new instruments
    • Avoids resource deadlocks
    • Dynamic workflow adjustments