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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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  • 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
    • PLUG-IN ELECTRIC VEHICLE (PEV) SMART CHARGING MODULE (ANL-IN-14-066)

    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.

    Benefits:

    • 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.

     

    Benefits:

    • 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.

    Benefits:

    • Designed to handle autonomous workflow

    • User-friendly content addition and editing

    • Data hierarchy designed for material samples

     

    Inventions, Applications, Industries:

    1. Autonomous material discovery

    • Combinatorial experiments

    • High-dimensional processing-property relationships

     

    1. Lab automation:

    • Facilitates integration solutions for new instruments

    • Avoids resource deadlocks

    • Dynamic workflow adjustments

     

  • Method of synthesizing materials for photovoltaic applications; utilizses relatively abundant, cheap, and non-toxic elements to produces photoactive films with average internal quantum efficiency of 12%.
    Intellectual Property Available to License
    US Patent 8,741,386
    • Atomic layer deposition of quaternary chalcogenides (ANL-IN-12-049)

    Methods and systems are provided for synthesis and deposition of chalcogenides (including Cu2ZnSnS4). Binary compounds, such as metal sulfides, can be deposited by alternating exposures of the substrate to a metal cation precursor and a chalcogen anion precursor with purge steps between.

  • Safe, scaleable, and economically feasible method of producing a family of high-voltage redox shuttles that provides overchange protection for Li-ion batteries; good electrochemical performance with high solubility in the electrolyte
    Intellectual Property Available to License
    US Patent 10,008,743
    • High voltage redox shuttles, method for making high voltage redox shuttles (ANL-IN-13-104)

    The invention provides a method for producing a molecule capable of undergoing reduction-oxidation when subjected to a voltage potential, the method comprising phosphorylating hydroquinone to create a first intermediate; rearranging the first intermediate to an aryl-bis-(phosphonate) thereby creating a second intermediate comprising phosphorous alkoxy groups; alkylating (e.g., methylating) the second intermediate; converting the alkoxy groups to halides; and substituting the halides to alkyl or aryl groups. Also provided is a system for preventing overcharge in a Lithium-ion battery, the method comprising a mixture of a redox shuttle with electrolyte in the battery such that the shuttle comprises between about 10 and about 20 weight percent of the mixture.

  • This invention comprises a system and method for the automated high-throughput characterization of edge coupled nanophotonic devices.
    Intellectual Property Available to License

    Please contact us for additional information.

  • This invention comprises a prototype device on a doped heterogeneous film
    Intellectual Property Available to License

    Please contact us for additional information.

  • An atomistic simulation toolkit for bridging length and time scales.Invention: Multi-fidelity scale bridging between various flavors of molecular dynamics (i.e. ab-initio, classical and coarse-grained models) has remained a long-standing challenge.
    Intellectual Property Available to License

    Invention:

    Multi-fidelity scale bridging between various flavors of molecular dynamics (i.e. ab-initio, classical and coarse-grained models) has remained a long-standing challenge. BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) is a framework that leverages machine learning principles to address this challenge.

    Opportunity and Solution 

    BLAST provides users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (i.e., force fields) for molecular simulations. BLAST is designed to address several long-standing problems in the molecular simulation community, such as unintended misuse of existing force fields due to a knowledge gap between developers and users, bottlenecks in traditional force field development approaches, and other issues relating to the accuracy, efficiency, and transferability of force fields. The BLAST architecture consists of a web user-friendly interface, front-end and back-end web services, and machine learning algorithms that run on high-performance computing (HPC) clusters.

  • SDN Multiple Operating System Rotational Environment (SMORE) utilizes software defined networking (SDN) to programmatically switch the flow of packets from users to a given set of servers. By periodically switching which servers respond to user requests.
    Intellectual Property Available to License

    Cybersecurity issues are a day-to-day struggle for businesses and organizations. Keeping information secure can be a herculean task. SMORE-MTD, developed by Argonne’s Joshua Lyle and Nate Evans with laboratory funding, defends against cybersecurity attacks by using software-defined networking to manipulate network paths that service user requests.

    By randomly selecting which server and service will respond to a given user’s request, SMORE-MTD makes it more difficult for an attacker to identify which services to attack. SMORE-MTD also increases organizations’ resilience by preventing an attacker exploit from being routed to the vulnerable software, forcing attackers into repeated attacks that are more likely to be noticed. SMORE-MTD also eliminates the need to install and maintain configuration software on each host in rotation, which reduces complexity and increases the amount of software available for use.

  • Catalytic material for selective conversion of waste plastics to higher value products, such as wax and lubricants. This single-step process is solvent-free and requires low temperatures and pressures.
    Intellectual Property Available to License
    US Patent 16/749,885 and 17/000,969
    • Catalytic polymer processing and upcycling (IN-18-075), (IN-19-098), (IN-19-144)

    A method of upcycling polymers to useful hydrocarbon materials. A catalyst with nanoparticles on a substrate selectively docks and cleaves longer hydrocarbon chains over shorter hydrocarbon chains. The catalyst includes metal nanoparticles in an order array on a substrate, and the nanoparticles exhibit an edge to facet ratio to provide for more interactions with the facets. The catalyst can be used to produce lubricants with superior tribological performance.

  • A method for synthesis of PtNi nanocages.
    Intellectual Property Available to License
    US Patent 10,637,072
    • Systems and Methods for PtNi Nanocages (IN-18-004)

    A method for synthesis of PtNi nanocages by synthesizing Pt1Ni6 nanoparticles and acid leaching to form PtNi nanocages. The acid leaching removes nickel selectively from the core of the nanoparticle.