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Argonne maintains a wide-ranging science and technology portfolio that seeks to address complex challenges in interdisciplinary and innovative ways. Below is a list of all articles, highlights, profiles, projects, and organizations related specifically to instrumentation.

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  • 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:

    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


  • 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.

  • This invention describes a holder that was specifically designed to hold a mesh and sample solution in a simple, reliable, cost effective, and user-friendly way.
    Intellectual Property Available to License
    US Patent 16/903,601
    • 3D Printed Mesh Holder for Serial Crystallography (IN-19-083)

    The invention describes a new sample holder for serial crystallography that utilizes a magnetic compression ring to immobilize sample fluids between mesh. Advantages of the new technology include, compatibility with standard single crystallography mounting equipment, 3D printable, minimal assembly required, high reusability, and cost effective.                                 

  • This invention is a new method of fabricating concave/convex optics in which a singlestructure crystal wafer will undergo curvature due to forces created by vacuum rather than the standard compressive techniques.
    Intellectual Property Available to License
    US Patent 8,557,149
    • System and method for implementing enhanced optics fabrication

    This method attempts to reduce the amount of residual stress and aberration that occurs by eliminating the forceful bending of such standard devices. This invention solves a difficult problem faced by those wishing to create a crystal surface, with even modest curvature, without causing non-uniform residual surface stresses.

  • Method to characterize nanofiber assemblies from images
    Intellectual Property Available to License

    US Patent 9,639,926 B1
    • Image processing tool for automatic feature recognition and quantification

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.