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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

PSU ESI Review

A guide to developing an Energy Service Interface (ESI) was created as part of the Grid Modernization Laboratory Consortium 2.5.2 ESI project. The approach applies device-agnostic and service-oriented ESI principles and leverages documents such as the Interoperability Maturity Model and Common Grid Service Definitions to provide a methodology to review, develop, and update standards and profiles to engage distributed energy resources (DER) to provide grid services. This document evaluates the ESI developed by Portland State University’s Power Engineering Group under the Electric Grid of Things project funded by the U.S. Department of Energy. The evaluation explores the compliance of this specific implementation with the GMLC ESI principles to provide an example of an ESI profile and gap analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

California Bridge to the Electron-Ion Collider (EIC) (Final Technical Report)

This report summarizes the activities and outcomes of DOE Grant DE-SC0022358, a traineeship program designed to prepare students for future nuclear physics research at the Electron-Ion Collider. The project leveraged the California EIC Consortium and statewide academic networks to provide immersive research experiences spanning universities and national laboratories. Participants engaged in theoretical, computational, and experimental research related to EIC physics and took part in professional meetings and collaborative research activities. The program supported student preparation for advanced study and research careers in nuclear physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Regional Approach to Offshore Wind Energy Manufacturing in the Central Atlantic: Supply Chain

Growth of the offshore wind industry in the United States is likely to require growth in the nation's manufacturing capacity and additional resources and investments to meet energy project demands. Such growth has the potential to provide positive economic impacts for local communities, workers, and entire states. To realize the benefits of developing a domestic offshore wind supply chain, the states of Maryland, North Carolina and Virginia are working together under the Southeast and Mid-Atlantic Regional Transformative Partnership for Offshore Wind Resources (SMART-POWER) regional collaboration to advance the industry, with Delaware as a supporting state. This report is part of the SMART-POWER Workforce and Supply Chain Analysis study, funded by the National Offshore Wind Research and Development Consortium. The study assesses challenges and opportunities for the four Central Atlantic states to understand key regional strengths and identify supply chain investments that could effectively leverage their resources and provide benefits to both the member states and the offshore wind sector as a whole.

17 WIND ENERGY↗
DOE OSTI2025

A Regional Approach to Offshore Wind Energy Manufacturing in the Central Atlantic: Workforce

Meeting the additional job requirements spurred by potential growth in the offshore wind industry is likely to involve intentional and effective recruitment practices and training program development across all occupations (e.g., skilled trades, engineers, professionals) at state, regional, and local levels. The workforce development ecosystem is complex, especially for an industry like offshore wind, so assessing the readiness of an area to support the workforce needs of new manufacturing facilities involves evaluating numerous economic and training factors at an occupational level. Funded by the National Offshore Wind Research and Development Consortium and led by the National Renewable Energy Laboratory, this report represents one part of a two-part study that explores the challenges and opportunities for Delaware, Maryland, Virginia, and North Carolina in fostering regional collaboration to build a domestic supply chain for the U.S. offshore wind energy sector.

17 WIND ENERGY↗
DOE OSTI2025

Technoeconomic Analysis (TEA)

Techno-economic analysis (TEA) for the DuraMAT Consortium includes the following areas: linking solar photovoltaic (PV) technology trends to reliability implications; providing a framework to calculate technology costs, yielding insights useful for research decision-making, proposals, technology selection, and publications; and examining technology tradeoffs considering lifecycle project economics. This poster highlights results from the first and second iterations of the DuraMAT Technology Scouting reports as well as the updated Simplified PV Levelized Cost of Energy (LCOE) Calculator.

14 SOLAR ENERGY↗
DOE OSTI2024

Implementation Guide of Customer-Focused Key Performance Indicators for Electric Vehicle Charging

This report describes how individual and unique messages that are sent over in OCPP sessions and/or transactions are used to calculate the interim set of key performance indicators (KPI) established by ChargeX Consortium’s Working Group 1: Defining the Charging Experience in the report entitles “Customer-Focused Key Performance Indicators for Electric Vehicle Charging.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Pull Force Evaluation of CCS Insulated End Caps

Insulated end caps installed on DC pins of Combined Charging System (CCS) inlets have been identified as potential debris sources within electric vehicle supply equipment (EVSE) connectors, increasing the risk of electric shock and fire hazards. Although standards such as IEC 62196-1:2022 specify mechanical pull-force requirements for these end caps, it remains unclear whether these requirements provide adequate robustness under real-world conditions. To address this concern, the National Charging Experience (ChargeX) Consortium's Hardware Task Force conducted evaluations of insulated end caps used in OEM CCS inlets. This study assesses the performance of insulated end caps used in OEM CCS inlets, specifically those installed on DC pins, using the procedures defined in IEC 62196-1:2022 (Section Sign) 26.7 and SAE J3400, supplemented by additional experimental conditioning. To ensure relevance to real-world conditions, NLR collaborated with charging network operators (CNOs) to guide the selection of charging inlet samples. Material analyses of the insulated end cap samples were conducted to confirm that the tested materials reflected the types and properties commonly observed in the field. The selected samples underwent temperature and humidity conditioning followed by pull-force testing to evaluate the end-cap integrity. Although SAE J3400 inlets were not directly tested due to limited diversity in available field data, the underlying end-cap principles between CCS and SAE J3400 are comparable, allowing the study's insights to be relevant to both technologies. Overall, this study provides a structured evaluation framework to inform potential refinements to mechanical pull-force requirements in charging standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

UNIFI Specifications for Grid-Forming Inverter-Based Resources: Version 3

The UNiversal Interoperability for grid-Forming Inverters (UNIFI) Consortium addresses fundamental challenges facing the integration of grid-forming (GFM) inverters in electric grids alongside rotating machines and other inverter-based resources (IBRs). This document defines a set of UNIFI specifications for GFM IBRs that provides requirements from both a power system level as well as functional requirements at the inverter level that are intended to provide means for the vendor-agnostic operation of GFM IBRs at any scale in electric power systems.

14 SOLAR ENERGY↗
DOE OSTI2026

California Bridge to the EIC: Building a Diverse Workforce for Nuclear Physics Research at the Electron Ion Collider

The California Bridge to the Electron Ion Collider traineeship program established an integrated workforce development effort connecting University of California campuses, California State University Minority Serving Institutions, and DOE national laboratories. The program expanded participation in nuclear physics research among students from underrepresented and socioeconomically disadvantaged backgrounds while strengthening collaborative activities aligned with the future Electron Ion Collider. During the award period, trainees conducted experimental, theoretical, and computational research, participated in consortium meetings and national laboratory collaborations, and received structured mentoring and professional development. The program achieved strong outcomes in graduate school placement, STEM career transitions, and sustained engagement with DOE Nuclear Physics research.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2026

An Uncertainty-Informed and High-Fidelity Performance Forecasting Framework for Heliostat Fields

Concentrating Solar Thermal (CST) tower systems employ heliostat fields to direct solar energy to a central receiver, which then transfers the heat either directly to a thermal process (e.g., steam production) or to a thermal energy storage system for future use. Heliostat fields compose a significant proportion of the project costs of a CST tower system and the performance of the heliostats determines a plant's productivity at a given location. While CST characterization tools such as SolarPILOT and System Advisor Model (SAM) include a large collection of inputs that influence the performance of a CST tower system, many are uncertain prior to the development of the project and may have a significant impact on the overall energy delivery and profitability of a project; moreover, the fidelity of these models under default conditions may be insufficient to determine the value of component improvements such as those under development in the Heliostat Consortium. This work introduces a Monte Carlo simulation framework that incorporates uncertainty in key performance parameters to generate confidence intervals and percentile estimates for a CST solar field's energy delivery.

14 SOLAR ENERGY↗
DOE OSTI2026

Combustion synthesis of nanoscale magnesium borides with improved hydrogen uptake and release

The overarching goal of the reported project was to develop reversible metal hydrides that can be dehydrogenated and recharged at relatively low temperatures and pressures. The project complemented the research on reversible metal hydrides for hydrogen storage conducted by the Hydrogen Materials – Advanced Research Consortium (HyMARC). The students involved were trained at both the University of Texas at El Paso (UTEP) and Sandia National Laboratories (SNL). The research objectives were to fabricate nanoscale magnesium boride powders with different B/Mg atomic ratios and to investigate the effects of B/Mg ratio and particle size on the hydrogenation properties of the obtained materials. Magnesium diboride (MgB 2 ) and tetraboride (MgB 4 ) were synthesized by using combustion synthesis and by heating in a tube furnace. The latter method resulted in materials with significantly reduced impurity levels. An effective method for the removal of magnesium oxide impurities was identified. High-energy ball milling produced submicron MgB 2 and MgB 4 powders. However, hydrogenation experiments at pressures up to 700 bar have not shown any uptake of hydrogen. Thermogravimetric analysis of the decomposition of the obtained MgB 2 and MgB 4 demonstrated higher thermodynamic stability of the latter. Thus, the boron-rich nature and phase stability of magnesium borides with high B/Mg ratios make them poor candidates for direct hydrogenation. However, the synthesized MgB 4 is a promising energetic additive to solid fuels for high-speed air-breathing propulsion as it offers a lower oxidation onset temperature and greater energy density than MgB 2 .

08 HYDROGEN↗
DOE OSTI2026

CdTe Core: Final Technical Report (FTR)

CdTe is presently the cost-leading thin-film PV technology, directly competing with Si at scale, even when domestically manufactured. While an impressive technology, its efficiency remains much below the detailed balance limit with the largest cause due to its low photovoltage and fill factor. To realize gains, the carrier concentration, minority carrier lifetime, and interface recombination all need to be improved simultaneously over historic levels. Using a new defect chemistry (group V doping instead of copper) has been identified as a viable route using single crystal systems. This project focused on implementing this new defect chemistry in scalable, polycrystalline thin-film photovoltaic CdTe devices with tasks focusing improvements to the front interface, absorber, and rear interface as well as capability development & stakeholder engagement. The goal of the project was to establish a strategy using devices, test structures, detailed characterization, and modeling to quantify the sources of losses in state-of-the-art CdTe photovoltaic devices. Using this strategy and advanced synthesis, losses at the front interface, absorber, and rear interface were worked on in parallel. The final objective was to significantly improve the voltage deficit in CdTe devices to enable improvements in photovoltage and efficiency that can be implemented by industry in the near-term. Over the course of the project, the team developed new characterization techniques, analysis, and modeling which were then applied to state-of-the-art materials generated internally and collaboratively. In particular to enable rapid progress, NREL worked closely with First Solar where NREL grew complete devices as well as ones that interleaved process steps where First Solar had completed different steps such as absorber growth or absorber growth and activation using their baseline methods. Using detailed characterization and analysis including photoemission, photoluminescence, and scanning probe techniques enabled understanding of the loss pathways and area for improvements in our own and First Solar s materials. Ultimately, this contributed to the first series of new world record CdTe efficiencies since 2016, culminating in a 23.1% certified cell that was P-doped along with As-doped cells of similar performance. Internally, NREL improved the statistical variation in baseline As-doped devices and improved average photovoltage by over 100 mV. This was done through an improvement in absorber quality, changed front interface, and improved back contact. In addition to materially improving the fabrication processes at NREL, characterization, analysis, and modeling were developed and disseminated. NREL also played a pivotal role in community building over the course of this project working closely with the Cadmium Telluride Accelerator Consortium. NREL worked in a series of collaborations with academic and industry partners, leveraging knowledge and innovations from this project, as well as helped organize a series of workshops to ensure rapid progress in the field. Working closely with the academic community has led to a dissemination of knowledge; working with First Solar as increased US competitiveness First Solar expanded domestic production to ~10 GW and opened new facilities.

14 SOLAR ENERGY↗
DOE OSTI2026

Integrating CO2 Electrolysis with Gas Fermentation to Produce Valuable Fuels and Chemicals

Many industrial activities squander CO2, decreasing process yield. We envision a future where this waste carbon is instead captured, upgraded, and valorized directly at the point of emission. Within the CO2 Reduction and Upgrading Consortium (a collaboration of seven US national laboratories and industrial partners), we are pursuing this goal by developing and de-risking new technologies for low temperature CO2 electrolysis, coupled with biological upgrading of intermediates into more valuable compounds. One such process involves electrocatalytic reduction of CO2 to generate carbon monoxide (CO). As both a carbon and energy source, CO represents an attractive feedstock for microbial upgrading by certain syngas-fermenting species, such as the autotrophic bacterium Clostridium autoethanogenum. Our team has developed new genetic tools and optimized cultivation techniques to enhance C. autoethanogenum as a platform host for the biological conversion of syngas into value-added products. For example, we have created novel CRISPR-based genetic engineering techniques to build new, genome-reduced, platform strains of C. autoethanogenum with improved growth rates. Further, we ve introduced heterologous biochemical pathways into C. autoethanogenum to enable the production of high-value compounds from syngas, such as the isoprenoid precursor mevalonic acid. With the tools of electrochemistry and synthetic biology, there is virtually no limit to the spectrum of products that could be sustainably manufactured from CO2.

09 BIOMASS FUELS↗