Image showes a group picture of the Energy I-Corps Cohort 13 participants in a virtual setting.

Members of Energy I-Corps Cohort 14

The opening session for Energy I-Corps Cohort 14 was held March 21–25, 2022, and the closing session was held May 17–19, 2022.

Cohort 14 was composed of 16 teams from Argonne National Laboratory (ANL), Idaho National Laboratory (INL), Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), Sandia National Laboratory (SNL), and SLAC National Accelerator Laboratory (SLAC).

The entirety of Cohort 14 was held virtually due to the COVID-19 pandemic.

Teams and Technologies

Team 163: RoboDT – ANL

Members of this Cohort 14 team.
Team 163: RoboDT, ANL
  • Principal Investigator: Young Soo Park
  • Entrepreneurial Lead: Jerry Nolen
  • Industry Mentor: Justin Tanaka

This project paves the path to commercialization of ANL’s robotic digital twin (RDT) technology, which allows network-distributed integration of robotic technologies—control remote sensing, real-time modeling, mixed-reality, and artificial intelligence—on a digital twin platform. The RDT technology will be instrumental in implementing robotic applications in broad industry needs and U.S. Department of Energy technology programs including manufacturing, nuclear industry, energy systems, vehicle technology, scientific discovery, and radioisotope production

Team 164: MASTERRI – INL

Members of this Cohort 14 team.
Team 164: MASTERRI, INL
  • Principal Investigator: Courtney Otani, PI
  • Entrepreneurial Leads: Pierce Russell, Bjorn Vaagensmith
  • Industry Mentor: Allison Reardon

The INL technology Modeling and Simulation for Targeted Electrical Reliability and Resilience Improvement (MASTERRI) allows for quantifiable failure analysis of complex systems/networks such as electrical grids. It identifies the level of vulnerability to cyber and other failure of specific components along with the resulting reliability of the system if that component were to fail.

Team 165: Feedforward K9 – INL

Members of this Cohort 14 team.
Team 165: Feedforward K9, INL
  • Principal Investigator: Ruixuan Li
  • Entrepreneurial Lead: Tina Miyake
  • Industry Mentor: Jay Umholtz

Nuclear incidents are often due to human errors in information gathering, response planning, and execution. Random human errors could be resolved by incorporating greater automation to the above listed processes, whereas design-induced human errors must be resolved with proper design. While automation removes some human reliability-related problems, other issues such as the loss of situation awareness and the decreased failure performance have become safety concerns under a higher degree of automation. The feedforward visualization technique leverages the capabilities of artificial-intelligence technologies, including machine learning and control logic (i.e., procedures), to provide decision support and alert operators.

Team 166: Bioreactor – LLNL

Members of this Cohort 14 team.
Team 166: Bioreactor, LLNL
  • Principal Investigator: Fang Qian
  • Entrepreneurial Lead: Samantha Ruelas
  • Industry Mentor: Phil Reeves

Methane is a potent greenhouse gas and more than 50% of its emission originates from biogas. To convert low-grade biogas into chemical intermediates for industrial production, we developed novel solid-state bioreactors that overcome the gas-to-liquid mass transfer limit and are therefore suitable for methanotrophic biocatalysis. These bioreactors are designed with large-surface area and gas-permeable 3D geometries, fabricated by additive manufacturing techniques, and open up new possibilities in reducing methane emission at different scales.

Team 167: Disease Precognition – LANL

Members of this Cohort 14 team.
Team 167: Disease Precognition, LANL
  • Principal Investigator: Kelly Moran
  • Entrepreneurial Lead: Sara Del Valle
  • Industry Mentor: Teegwendé Valérie Porgo

Infectious diseases are the leading cause of illness and death around the globe—severely impacting populations, societies, and economies. The Global Disease Forecasting Center (GDFC) offers a robust platform to assess trends and accurately identify potential disease outbreaks within communities. GDFC leverages diverse real-time big data feeds, employing proprietary models and machine learning approaches to analyze these noisy data streams and create actionable information. The GDFC will be useful to public health officials, key decision makers, pharmaceutical companies, other service providers, and the general public to prepare for, mitigate, and prevent emerging and reemerging infectious diseases.

Team 168: Phase Changers – NREL

Members of this Cohort 14 team.
Team 168: Phase Changers, NREL
  • Principal Investigator: Lin Simpson
  • Entrepreneurial Lead: Aaron Selnick
  • Industry Mentor: Bruce Lanning

The Phase Changer team at NREL is interested in developing and helping to commercialize novel technologies that can provide large-utility-scale energy storage (hundreds of terawatt hours) required for a future where renewables is the dominant form of energy generation and simultaneously capture greenhouse gases. Gases like carbon dioxide and methane will be collected directly from the air and/or from industrial emitters like steel, cement, and chemicals. We hope to do all this for a cost less-than present non-renewable energy generation today while also expanding industrial chemical manufacturing.

Team 169: Under the C – NREL

Members of this Cohort 14 team.
Team 169: Under the C, NREL
  • Principal Investigator: Ben Maurer
  • Entrepreneurial Lead: Gabriella Lahti
  • Industry Mentor: Philip Pienkos

Our technology is an in situ river microplastic sensor platform that is networked, low-cost, self-powered, user-deployable, and capable of producing scientific-quality data. This technology would provide continuous year-round data sampling and would mark NREL's first step in closing the loop of microplastic pollution. Self-powering would be enabled by converting water current energy into electricity to power a Raman laser and continuous data transmission. Raman data would be run through machine learning algorithms trained on existing microplastic and common pollutant databases. The algorithms would detect characteristic Raman signals from every polymer present and calculate concentrations from relative intensities of Raman signals.

Team 170: WindEZ – NREL

Members of this Cohort 14 team.
Team 170: WindEZ, NREL
  • Principal Investigator: P.J. Stanley
  • Entrepreneurial Lead: Kelsey Shaler
  • Industry Mentor: Nick Smith

WindEZ will facilitate industry, academic, and general adoption of open-source NREL wind-focused software. NREL develops many powerful wind-plant simulation tools including FAST, Farm, Floris, and others that are useful in wind-plant design and analysis. However, using these tools requires command line coding experience. The necessary experience can be daunting and discourage many from using these tools. WindEZ will make adoption and continued use of NREL's wind-focused software a smoother experience for a general user. WindEZ could include GUI development, comprehensive tutorials, or a host of other possibilities—the specifics of which will be guided by feedback from various stakeholders.

Team 171: Real-Twin – ORNL

Members of this Cohort 14 team.
Team 171: Real-Twin, ORNL
  • Principal Investigators: Yunli Shao, (Ross) Chieh Wang
  • Entrepreneurial Leads: Jinghui Yuan, PT Jones
  • Industry Mentor: Mark Malone

Validating emerging mobility technologies such as autonomous vehicles often requires extensive efforts to generate and test a comprehensive set of scenarios in traffic microsimulation, virtual environments, and/or everything-in-the-loop (XIL) simulation. The core of Real-Twin is a realistic and streamlined scenario generation capability that ingests real-world data and establishes a twin representation of the transportation system. Real-Twin (semi-) automatically generates relevant scenarios based on user specified technology and available data and implements these scenarios into different traffic/vehicle simulators (e.g., VISSIM, SUMO, IPG CarMaker, CARLA) and/or XIL to assess the performance and impact of the technology.

Team 172: FrozEn – PNNL

Members of this Cohort 14 team.
Team 172: FrozEn, PNNL
  • Principal Investigator: Guosheng Li
  • Entrepreneurial Lead: Miller Li
  • Industry Mentor: Neil Kidner    

The FrozEn battery team at PNNL presents a new approach—so called "freeze-thaw" battery technology—to long-duration/seasonal electricity storage in a rechargeable battery without self-discharge/degradation associated with conventional rechargeable batteries. The freeze-thaw battery remains indefinitely in a state of hibernation to avoid self-discharge and "awakens" for operation only when the temperature of the battery is slightly elevated at which point the solid electrolyte melts into a liquid state for the normal battery operation. The present invention opens up new possibilities for seasonal energy storage, helping to stabilize the grid during fluctuations associated with seasonal and/or intermittent generation of renewable energy sources.

Team 173: ShAPE Recycling – PNNL

Members of this Cohort 14 team.
Team 173: ShAPE Recycling, PNNL
  • Principal Investigator: Xiao Li
  • Entrepreneurial Lead: Md. Reza-E-Rabby
  • Industry Mentor: Hunter Martin

Shear Assisted Processing and Extrusion (ShAPE) is an advanced manufacturing technology that converts bulk billet or finely divided precursors into valuable products with high-efficiency and require no external heat source. The metal waste precursor is plastically deformed, heated, consolidated, and extruded into high-quality rod or tube. ShAPE recycling of metal scraps can reduce energy consumption by 30%, cost by 45%, and reduce CO2 emissions by 55% compared to traditional melting processes. This PNNL unique capability is potentially pivotal to deploying the U.S government’s zero-carbon emissions agenda.

Team 174: ZAV-SNL – SNL

Members of this Cohort 14 team.
Team 174: ZAV-SNL, SNL
  • Principal Investigator: Peter Choi, PI
  • Entrepreneurial Leads: Adrian Chavez, Katherine Marie Foster
  • Industry Mentor: Adam Greenhood

Key management services (KMS) are notoriously expensive and challenging to maintain and secure. Zero-trust Application for Vehicle (ZAV) eliminates the need for KMS by dynamically generating cryptographic keys as they are needed. Since cryptographic keys are never stored anywhere, it significantly reduces the KMS related risks and available attack surfaces for cyber hackers. ZAV also provides a comprehensive cybersecurity solution (e.g., confidentiality, integrity, authenticity/availability [CIA]) for all things related to vehicle communication (e.g., controlling, sensing, messaging). With ZAV, every single message is verified with CIA assurances using instantly recognized hardware identities and with automated encryption key tracking capabilities.

Team 175: CAML – SNL

Members of this Cohort 14 team.
Team 175: CAML, SNL
  • Principal Investigator: Mark Kinnan
  • Entrepreneurial Lead: Sakeeth Desai
  • Industry Mentor: Matthew Ennis

Computational modeling and machine learning strategies to identify new reaction pathways for chemical-based recycling solutions.

Team 176: Hydrogen Ships – SNL

Members of this Cohort 14 team.
Team 176: Hydrogen Ships, SNL
  • Principal Investigator: Vincent Neary
  • Entrepreneurial Lead: Marisa Montes
  • Industry Mentor: Bill Staby

A sailing renewable hydrogen energy ship transfers wind-sail power to hydrokinetic power captured by relatively small water turbines attached to the ship’s hull. The electric energy generated by this hydrokinetic turbine is used to manufacture and store pressurized or liquefied hydrogen after splitting water in an electrolyzer.

Team 177: nDETECT – SNL

Members of this Cohort 14 team.
Team 177: nDETECT, SNL
  • Principal Investigator: Mara Schindelholz
  • Entrepreneurial Lead: Wendy Rue
  • Industry Mentor: Robert Delcampo    

The technology is a long-lived, direct-electrical readout sensor for gas detection. The sensor is composed of an interdigitated electrode with a nanoporous adsorbent layer. The nanoporous phase can be tuned to selectively adsorb gases of interest, and the electrical response (e.g., impedance) directly correlated to gas concentration.

Team 178: GRIP – SLAC

Members of this Cohort 14 team.
Team 178: GRIP, SLAC
  • Principal Investigator: Alyona Teyber
  • Entrepreneurial Lead: Gustavo Cezar
  • Industry Mentor: Rahul Kar

Grid Resilience and Intelligence Platform (GRIP) is designed to address one of the most pressing concerns with electricity distribution utilities today: extreme weather resilience. The project’s approach uses data and technology agnostic methodology to present analytics that can be easily deployed on any electrical utility platform to help them make informed operational decisions before, during and after an event. GRIP is of particular use in areas where wildfire events are frequent while the Public Safety Power Shutoff protocols are vaguely defined and often lack consideration of impact equity, real-time conditions, and customer needs as well as distributed generation and energy storage.