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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Intermediate States Enable Keratin-like α-To-β Transformations in Strain-Responsive Synthetic Polypeptides

Nature’s fibrous proteins, such as α-keratin, achieve remarkable mechanical properties by undergoing strain-induced α-to-β conformational transitions. Inspired by these materials, we report a strategy for designing synthetic polypeptides that undergo similar transformations at elevated temperatures far exceeding keratin’s operational range. By employing helix-confined ring-opening polymerization (ROP) of N -carboxyanhydrides (NCAs) initiated by a short poly(γ-benzyl- L -glutamate) (PBLG) precursor, we synthesized poly( O -benzyl- L -serine) (PBLS) chains that adopt an α-helical structure yet transition into β-sheets upon heating. Compression molding at carefully chosen temperatures drives PBLS segments into an α-β intermediate state, characterized by relaxed intrachain hydrogen bonds and a hexagonal packing arrangement. Under mechanical strain, these intermediate states convert in situ into β-sheets, producing significant strain-hardening well below the spontaneous α-to-β temperature threshold. Further, this approach extends to polypeptides bearing different side chains, such as poly(S-benzyl- L -cysteine), demonstrating robust mechanical reinforcement across a wide temperature window up to ∼ 200 °C. In situ synchrotron X-ray analysis confirms that chain alignment, β-sheet formation, and domain growth occur stepwise during deformation. By harnessing the intermediate states and the supramolecular cooperativity conferred by compression-molded films, our method provides a versatile platform for developing next-generation polypeptide materials with tunable mechanical resilience and responsiveness─surpassing the temperature limitations of natural fibrous proteins and enabling potential applications demanding broad-temperature mechanical adaptability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deletion of Re -citrate synthase allows for analysis of contributions of tricarboxylic acid cycle directionality to the growth of Heliomicrobium modesticaldum

ABSTRACT Heliomicrobium modesticaldum,a phototrophic member of the phylum Firmicutes and family Clostridiales, possesses most of the enzymes specific to the reductive tricarboxylic acid (rTCA) cycle, except for the key enzyme, ATP-citrate lyase. It is thought to utilize a split TCA cycle when growing on pyruvate as a carbon source, in which the oxidative TCA (oTCA) direction generates most of the 2-ketoglutarate, but some can be produced in the reductive direction. Although a typicalSi-citrate synthase gene is not found in the genome, it was suggested that gene HM1_2993, annotated as homocitrate synthase, actually encodesRe-citrate synthase, which would function as the initial enzyme of the oTCA cycle. We deleted this gene to test this hypothesis and, if true, see what effect severing access to the oTCA cycle would have on this organism. The endogenous CRISPR-Cas system was used to replace the open reading frame with a selectable marker. The deletion mutants could grow on pyruvate but were unable to grow phototrophically on acetate + CO 2 as carbon source. Growth on acetate could be rescued by the addition of different electron sources (formate or ascorbate), suggesting that the oTCA cycle is used to oxidize acetate to generate electrons required to drive the carboxylation of acetyl-CoA. The deletion mutants were capable of growing in acetate minimal media without additional organic supplements beyond formate, demonstrating that the rTCA cycle can be employed to support sufficient 2-ketoglutarate production in this organism, unlike citrate synthase mutants in several chemoheterotrophic organisms utilizing the oTCA cycle. IMPORTANCE Heliobacteria are a unique group of phototrophic bacteria that are obligate anaerobes and possess a rudimentary system to use light as a source of energy. They do not make oxygen or fix carbon dioxide. Here, we explore their fundamental carbon metabolism to understand the role and operation of the central TCA cycle. This work shows both the role and operation of this cycle under different growth modes and explains how these organisms can obtain electrons to drive their biosynthetic metabolism. This foundational knowledge will be crucial in the future when attempts are made to use this organism as a platform for oxygen-sensitive synthesis of compounds in an anaerobe that can use light as its energy source.

Biotechnology & Applied Microbiology↗
DOE OSTI2026

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hydrodynamic Analysis and Optimization of Aquantis Marine Turbine: Cooperative Research and Development (Final Report)

The primary aim of this proposal is to improve the accurate prediction of hydrodynamic performance and dynamic load responses of the AQ10 floating axial-flow tidal turbine with a tri-cat mooring configuration. The validation of reduced-order modeling approaches with high-fidelity model will be implemented. Additionally, the frequency response domain, Response Amplitude Floating Wind (RAFT) toolbox plus an optimizer expanded for marine hydrokinetic turbines under the Submarine Hydrokinetic And Riverine Kilo-megawatt. Systems (SHARKS) program will be used for designing and exploring different key design parameters (platform dimension, mooring layout and its parameters) of next marine hydrokinetic (MHK) turbine generation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗
DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Association Mapping of Cell Wall Synthesis Regulatory Genes and Cell Wall Quality in Switchgrass

Inefficient conversion of biomass to biofuels is one of the main barriers for biofuel production from such materials. Approximately half of polysaccharides in biomass remain unused by typical biochemical conversion methods. Conversion efficiency is influenced by the composition and structure of cell walls of biomass. Grasses such as wheat, maize, and rice, as well as dedicated perennial bioenergy crops, like switchgrass, make up ~55% of biomass that can be produced in the United States. Grass cell walls have a different composition and patterning compared with dicotyledonous plants, including the well-studied model plant, Arabidopsis. This project identified genetic determinants of cell wall composition in grasses using both naturally occurring genetic variation of switchgrass and gene network reconstruction and functional assays in rice. In addition, the project linked functional data in rice and other species to switchgrass improvement efforts through curation of the most abundant class of regulators in the switchgrass genome. Characterizing natural diversity of switchgrass for variation in cell wall composition and properties, also known as quality, provides an unbiased avenue for identifying biologically viable diversity in switchgrass cell walls. To characterizing natural diversity, this project generated cell wall composition and enzymatic deconstruction data for ~450 genotypes of the Switchgrass Southern Association Collection (SSAC), a diverse collection composed of 36 switchgrass accessions from the southern U.S. distribution of switchgrass. Comparing these data with other measures of cell wall quality for the same samples demonstrated the complementary nature of the diverse characterization platforms now being used for biomass characterization. Association of the composition data with ~3.2K single nucleotide variant markers identified six significant single nucleotide variant markers co-associated with digestibility and another compositional trait. These markers might be used to select switchgrass genotypes with improved composition in breeding programs for biofuel and forage production. Because the SSAC continues to be characterized by collaborators in the bioenergy community, the data generated will be used to identify additional markers in higher resolution genotyping data to approach identifying the genes and alleles that cause natural variation in switchgrass cell wall quality. For example, these markers can be surveyed in the 2100-member Oklahoma Southern and Northern Lowland switchgrass collections that this project also characterized. An orthogonal approach to biodiversity studies, using comparative functional genomics permits systematic querying of how much regulatory information is likely to be transferable from dicots to grasses and use of accumulated functional genomics resources for better-characterized grass species, such as rice, itself a biomass source in global agriculture and in certain regions. The project generated and tested a number of specific hypotheses regarding cell wall transcription factors and enzymes of grasses. To aid identification of cell wall regulators, the project assembled a novel, highdepth and -quality gene association network using a general linearized model scoring system to combine rice gene network data. Using known or putative orthologs of Arabidopsis cell wall biosynthesis genes and regulators, the project pulled from this network a cell wall sub-network that includes 96 transcription factors. Reverse genetics of a co-ortholog of the Arabidopsis MYB61 transcription factor in rice revealed that this regulatory node has evolved the ability to regulate grass-specific cell wall synthesis enzymes. A transcription factor with such activity has not been previously characterized to our knowledge, representing a major conclusion of this work. Changes in gene expression in a protoplast-based assay demonstrated positive or negative roles in cell wall regulation for eleven other transcription factors from the rice gene network. Eight of fifteen (53%) of these have not previously been examined for this function. Some of these may represent novel grass-diverged cell wall regulators, while others are likely to have this function across angiosperms. A parallel effort of this project to expand knowledge of enzymes that have evolved to function in grass cell wall synthesis, revealed that a grass-diverged enzyme in rice, OsAT 5 , ferulates monolignols that are naturally incorporated into grass cell walls. This finding opens potential natural selection avenues for improving biomass composition for downstream processing by weak base pretreatment. Thus, this project has significantly expanded knowledge of cell wall synthesis and regulation in rice, information that can be used in reverse genetics and synthetic biology approaches to re-engineer cell walls for improved production of biofuel and high-value products. To lay the foundation for translating these results directly for switchgrass improvement, the project employed a comparative phylogenetic analysis of the major group of cell wall transcription factors that have been found to function in cell wall regulation, the R 2 R 3 MYBs. This analysis concluded that known cell wall regulators are largely conserved across switchgrass, rice, maize, poplar, and Arabidopsis. This interpretation is also largely consistent with the gene network analysis described above, though both approaches provide evidence that some co-orthologs of Arabidopsis regulators have diminished or increased in importance based on gene expression patterns. Also, several clades containing dicot cell wall regulators have expanded, consistent with the evolution of new cell wall regulators. This latter result is supported by functional analysis of the R 2 R 3 MYB protein SWAM 1 in a collaboration between this project and the DOE-funded group of Dr. S. Hazen at the University of Massachusettes. The curation of the switchgrass genome through this project provides specific targets for future engineering of switchgrass cell wall regulation and may also facilitate identification of regulators that underlie the molecular markers that are genetically linked to differences in cell wall quality. With the goal of spurring further research and technological developments in lignocellulosic biofuel production, this work has been communicated to the bioenergy and cell wall communities though various presentations and publications. To date, three manuscripts have been published, two others are near to publication, three others are in an advanced state, and two to four more are likely to be written based on analyses still in progress. In addition, project participants have presented thirteen posters and talks at regional, national, and international meetings about aspects of this project. In sum, the work supported by this funding has made and communicated significant progress in identifying the genes that grasses use for cell wall synthesis and regulation, information that will be used by project participants and others to improve the efficiency of conversion of lignocellulosic biomass to biofuels.

09 BIOMASS FUELS↗
DOE OSTI2012

Neutron and X-ray Detectors

The Basic Energy Sciences (BES) X-ray and neutron user facilities attract more than 12,000 researchers each year to perform cutting-edge science at these state-of-the-art sources. While impressive breakthroughs in X-ray and neutron sources give us the powerful illumination needed to peer into the nano- to mesoscale world, a stumbling block continues to be the distinct lag in detector development, which is slowing progress toward data collection and analysis. Urgently needed detector improvements would reveal chemical composition and bonding in 3-D and in real time, allow researchers to watch “movies” of essential life processes as they happen, and make much more efficient use of every X-ray and neutron produced by the source The immense scientific potential that will come from better detectors has triggered worldwide activity in this area. Europe in particular has made impressive strides, outpacing the United States on several fronts. Maintaining a vital U.S. leadership in this key research endeavor will require targeted investments in detector R&D and infrastructure. To clarify the gap between detector development and source advances, and to identify opportunities to maximize the scientific impact of BES user facilities, a workshop on Neutron and X-ray Detectors was held August 1-3, 2012, in Gaithersburg, Maryland. Participants from universities, national laboratories, and commercial organizations from the United States and around the globe participated in plenary sessions, breakout groups, and joint open-discussion summary sessions. Sources have become immensely more powerful and are now brighter (more particles focused onto the sample per second) and more precise (higher spatial, spectral, and temporal resolution). To fully utilize these source advances, detectors must become faster, more efficient, and more discriminating. In supporting the mission of today’s cutting-edge neutron and X-ray sources, the workshop identified six detector research challenges (and two computing hurdles that result from the corresponding increase in data volume) for the detector community to overcome in order to realize the full potential of BES neutron and X-ray facilities. Resolving these detector impediments will improve scientific productivity both by enabling new types of experiments, which will expand the scientific breadth at the X-ray and neutron facilities, and by potentially reducing the beam time required for a given experiment. These research priorities are summarized in the table below. Note that multiple, simultaneous detector improvements are often required to take full advantage of brighter sources. High-efficiency hard X-ray sensors: The fraction of incident particles that are actually detected defines detector efficiency. Silicon, the most common direct-detection X-ray sensor material, is (for typical sensor thicknesses) 100% efficient at 8 keV, 25%efficient at 20 keV, and only 3% efficient at 50 keV. Other materials are needed for hard X-rays. Replacement for 3He for neutron detectors: 3He has long been the neutron detection medium of choice because of its high cross section over a wide neutron energy range for the reaction 3He + n —> 3H + 1H + 0.764 MeV. 3He stockpiles are rapidly dwindling, and what is available can be had only at prohibitively high prices. Doped scintillators hold promise as ways to capture neutrons and convert them into light, although work is needed on brighter, more efficient scintillator solutions. Neutron detectors also require advances in speed and resolution. Fast-framing X-ray detectors: Today’s brighter X-ray sources make time-resolved studies possible. For example, hybrid X-ray pixel detectors, initially developed for particle physics, are becoming fairly mature X-ray detectors, with considerable development in Europe. To truly enable time-resolved studies, higher frame rates and dynamic range are required, and smaller pixel sizes are desirable. High-speed spectroscopic X-ray detectors: Improvements in the readout speed and energy resolution of X-ray detectors are essential to enable chemically sensitive microscopies. Advances would make it possible to take images with simultaneous spatial and chemical information. Very high-energy-resolution X-ray detectors: The energy resolution of semiconductor detectors, while suitable for a wide range of applications, is far less than what can be achieved with X-ray optics. A direct detector that could rival the energy resolution of optics could dramatically improve the efficiency of a multitude of experiments, as experiments are often repeated at a number of different energies. Very high-energy-resolution detectors could make these experiments parallel, rather than serial. Low-background, high-spatial-resolution neutron detectors: Low-background detectors would significantly improve experiments that probe excitations (phonons, spin excitations, rotation, and diffusion in polymers and molecular substances, etc.) in condensed matter. Improved spatial resolution would greatly benefit radiography, tomography, phase-contrast imaging, and holography. Improved acquisition and visualization tools: In the past, with the limited variety of slow detectors, it was straightforward to visualize data as it was being acquired (and adjust experimental conditions accordingly) to create a compact data set that the user could easily transport. As detector complexity and data rates explode, this becomes much more challenging. Three goals were identified as important for coping with the growing data volume from high-speed detectors: Facilitate better algorithm development. In particular, algorithms that can minimize the quantity of data stored. Improve community-driven mechanisms to reduce data protocols and enhance quantitative, interactive visualization tools. Develop and distribute community-developed, detector-specific simulation tools. Aim for parallelization to take advantage of high-performance analysis platforms. Improved analysis work flows: Standardize the format of metadata that accompanies detector data and describes the experimental setup and conditions. Develop a standardized user interface and software framework for analysis and data management. The diversity of detector improvements required is necessarily as broad as the range of scientific experimentation at BES facilities. This workshop identified a variety of avenues by which detector R&D can enable enhanced science at BES facilities. The Research Directions listed above will be addressed by focused R&D and detector engineering, both of which require specialized infrastructure and skills. While U.S. leadership in neutron and X-ray detectors lags behind other countries in several areas, significant talent exists across the complex. A forum of technical experts, facilities management, and BES could be a venue to provide further definition.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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 OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Engineering Ralstonia eutropha for Production of Isobutanol (IBT) Motor Fuel from Carbon Dioxide, Hydrogen, and Oxygen Project Final Report

This research project is a collaboration between the Sinskey laboratory at MIT and the Worden laboratory at Michigan State University. The goal of the project is to produce Isobutanol (IBT), a branched-chain alcohol that can serve as a drop-in transportation fuel, through the engineered microbial biosynthesis of Carbon Dioxide, Hydrogen, and Oxygen using a novel bioreactor. This final technical report presents the findings of both the biological engineering work at MIT that extended the native branched-chain amino acid pathway of the wild type Ralstonia eutropha H16 to perform this biosynthesis, as well as the unique design, modeling, and construction of a bioreactor for incompatible gasses at Michigan State that enabled the operational testing of the complete system. This 105 page technical report summarizing the three years of research includes 72 figures and 11 tables of findings. Ralstonia eutropha (also known as Cupriavidus necator) is a Gram-negative, facultatively chemolithoautotrophic bacteria. It has been the principle organism used for the study of polyhydroxybutyrate (PHB) polymer biosynthesis. The wild-type Ralstonia eutropha H16 produces PHB as an intracellular carbon storage material while under nutrient stress in the presence of excess carbon. Under this stress, it can accumulate approximately 80 % of its cell dry weight (CDW) as this intracellular polymer. With the restoration of the required nutrients, the cells are then able to catabolize this polymer. If extracted from the cell, this PHB polymer can be processed into biodegradable and biocompatible plastics, however for this research, it is the efficient metabolic pathway channeling the captured carbon that is of interest. R. eutropha is further unique in that it contains two carbon-fixation Calvin–Benson–Bassham cycle operons, two oxygen-tolerant hydrogenases, and several formate dehydrogenases. It has also been much studied for its ability in the presence of oxygen, to fix carbon dioxide into complex cellular molecules using the energy from hydrogen. In this research project, engineered strains of R. eutropha redirected the excess carbon from PHB storage into the production of isobutanol and 3-methyl-1-butanol (branched-chain higher alcohols). These branched-chain higher alcohols can be used directly as substitutes for fossil-based fuels and are seen as alternative biofuels to ethanol and biodiesel. Importantly, these alcohols have approximately 98 % of the energy content of gasoline, 17 % higher than the current gasoline additive ethanol, without impacting corn market production for feed or food. Unlike ethanol, these branched-chain alcohols have low vapor pressure, hygroscopicity, and water solubility, which make them readily compatible with the existing pipelines, gasoline pumps, and engines in our transportation infrastructure. While the use of alternative energies from solar, wind, geothermal, and hydroelectric has spread for stationary power applications, these energy sources cannot be effectively or efficiently employed in current or future transportation systems. With the ongoing concerns of fossil fuel availability and price stability over the long term, alternative biofuels like branched-chain higher alcohols hold promise as a suitable transportation fuel in the future. We showed in our research that various mutant strains of R. eutropha with isobutyraldehyde dehydrogenase activity, in combination with the overexpression of plasmid-borne, native branched-chain amino acid biosynthesis pathway genes and the overexpression of heterologous ketoisovalerate decarboxylase gene, would produce isobutanol and 3-methyl-1-butanol when initiated during nitrogen or phosphorus limitation. Early on, we isolated one mutant R. eutropha strain which produced over 180 mg/L branched-chain alcohols in flask culture while being more tolerant of isobutanol toxicity. After the targeted elimination of genes encoding several potential carbon sinks (ilvE, bkdAB, and aceE), the production titer of the improved to 270 mg/L isobutanol and 40 mg/L 3-methyl-1-butanol. Semicontinuous flask cultivation supplied the cells with sufficient nutrients while minimizing the toxicity caused by isobutanol. Under this cultivation, the R. eutropha mutant grew and produced more than 14 g/L branched-chain alcohols over the duration of 50 days. These results demonstrate that R. eutropha carbon flux can be redirected from PHB to branched-chain alcohols and that engineered R. eutropha can be cultivated over prolonged periods of time for product biosynthesis. While this bioengineering work was being done at the Sinskey laboratory at MIT, the researchers at the Worden laboratory at Michigan State were working on the design and construction of the required specialty bioreactor for incompatible gasses (BIG) that would allow the safe feeding of microbes on Carbon Dioxide, Hydrogen, and Oxygen without explosive results. The early design and assembly work in year 1 incorporated a novel microbubble generator to maximize the bioavailability of gasses within the system comprised of small scale hollow fiber reactors. The early success of the microbubble generator eliminated the need to investigate potentially toxic surfactants within the system. For operational control, the system design incorporated a Opto22-based control network. The researchers also selected the specific hollow fiber material suitable for the bioreactor application. A variety of commercially available hollow fiber membranes were compared with regard to their pore sizes, cell affinity, and potential interference to cell viability assays. The selected membrane with its spongy layer was then tested for diffusivity of O2 and CO2. The instrumented system was then fully assembled for experimentally measuring the heterotrophic growth rate of immobilized R. eutropha cells. The requisite procedures for inoculation, measurement, and cleaning were established enabling the system performance to be validated under controlled laboratory conditions. In year 2, the researchers completed the Opto22 based cross-platform control network, and the system’s communications across the Sartorius fermentation system and Bruker gas chromatograph was established via open platform communications (OPC) protocol. Using the revised system, measurements were taken of the R.eutropha cell growth rate and substrate mass transfer rate in the hollow fiber membrane. Several IBT recovery strategies were explored and resin adsorption was determined to be optimal solution for lab scale operations. The adsorption capacity of the resin column was then measured and IBT desorption using methanol has been demonstrated. With the growing body of experimental data in hand, mathematical models were constructed to demonstrate and map the cellular kinetics, mass transfer of heterotrophic and autotrophic substrates in the hollow fiber, and the adsorption process in the resin column. A structured kinetic model was constructed to describe the competition between cell mass generation and IBT production. The reactor was then scaled up from single fiber to a membrane area of 180 cm2 and then further to 1 ft2. In Year 3 of the research, the IBT mass transfer across the membrane was characterized within the system with experiments to empirically measure the IBT diffusion coefficient in the BIG spongy layer. Using the refined mathematical models, the researchers are now able to explain the experimental observations and predict bioreactor performance under a wide range of experimental conditions. The Big system is able to demonstrate continuous controlled operations with the integrated IBT recovery system. Both heterotrophic and autotrophic production have been shown during continuous operation with heterotrophic and autotrophic stages. Performance of BIG system has been measured during continuous run with alternating heterotrophic growth on fructose and autotrophic product formation on H2, CO2, and O2. Volumetric productivities of IBT at 325 mg/(L day) and of 3M1B at 50 mg/(L day) were achieved, which were comparable to that achieved under heterotrophic conditions. Using the mathematical model, researchers are able to predict system performance for scaled-up BIG system. The apparent diffusion coefficient of IBT in the spongy layer of XM-50 hollow fiber membranes has been measured at various lumen liquid flow rates. The experiment is simulated in COMSOL to validate the results. The constructed COMSOL model is able to simulate BIG system performance in both batch and continuous mode. Mathematical simulations of the system performance have been run to identify the most crucial operational conditions, identifying the rate-limiting factors in autotrophic production of IBT, and quantitating the rate of IBT catabolism. Investigations of the productivity of the production system have suggested and the modeling of the system has revealed a particular sensitivity to the catabolism of the produced IBT by the engineered R. eutropha. Experiments have been designed and executed to quantify the IBT catabolism of R. eutropha, which open up possibilities for further system improvements through future, targeted bioengineering of the strain. Finally, the researchers at Michigan State performed an economic analysis of the system, based on the collective results, and their findings are presented in full within the report.

08 HYDROGEN↗