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DOE OSTI2026

Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data

Crystallographic structure identification is crucial for understanding material properties; however, current methodologies often depend on labor-intensive and time-consuming analyses of 2D X-ray diffraction (XRD) patterns. To address these limitations, this study employs synthetic 2D XRD patterns combined with deep learning (DL) techniques to enable automated and high-throughput classification of the seven crystal systems and 230 space groups. We introduce the novel Auto Diffraction Pipeline, designed to generate synthetic 2D XRD spot patterns from crystallographic information files under diverse conditions, including varying zone axes, atomic substitution, atomic depletion and mechanical loading. These conditions enhance the realism of synthetic data, mitigating the scarcity of experimental datasets and enabling the creation of large representative training sets. Convolutional neural networks were trained and validated on these synthetic datasets to classify crystallographic structures across multiple scenarios. Our results demonstrate that integrating synthetic 2D XRD patterns with DL facilitates rapid, accurate and automated crystallographic classification, promoting the wider adoption of data-driven approaches in materials science.

Shahnazari, Ayoub [Univ. of Rochester, NY (United ↗

DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Synthetic Atmospheric River Ensembles Generated by Deep-AR

This dataset contains 35,850 synthetic landfalling atmospheric river (AR) realizations generated by the Deep-AR two-stage deep-learning framework over the Northeast Pacific and U.S. West Coast. The archive contains 25 stochastic ensemble members for each of 1,434 held-out observed seed events. Each synthetic realization is initialized from conditions 48 hours before the corresponding observed AR landfall and is generated autoregressively at 6-hour intervals over a 144-hour period. Deep-AR combines a deterministic residual network (ResNet) that advances the large-scale atmospheric state with a Wasserstein generative adversarial network (WGAN) that produces stochastic, high-resolution fields. Each HDF5 file contains 0.25° gridded synthetic integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation on a common 200 × 480 grid. The files also include coordinate and datetime arrays. This dataset supports AR hazard analysis, ensemble-based uncertainty characterization, precipitation-extremes research, and regional stress testing. Synthetic files follow the naming convention deepar.model.YYYYMMDD.HHMMSS.vNN.h5. YYYYMMDD.HHMMSS identifies the UTC initial-condition timestamp, which occurs 48 hours before the diagnosed observed landfall, and vNN identifies the zero-padded ensemble member, ranging from v01 through v25. Each synthetic file can be paired with its corresponding observed file by matching the initial-condition timestamp. The paired observed file follows the naming convention deepar.obs.YYYYMMDD.HHMMSS.h5 and is available in the separately registered oracle/deepar.obs dataset at https://wdh.energy.gov/ds/oracle/deepar.obs (DOI: https://doi.org/10.21947/3377671).

17 WIND ENERGY↗

DOE OSTI2024

LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry

Since their advent over 2 decades ago, autonomous Argo floats have revolutionized the field of oceanography, and, more recently, the addition of biogeochemical and biological sensors to these floats has greatly improved our understanding of carbon, nutrient, and oxygen cycling in the ocean. While Argo floats offer unprecedented horizontal, vertical, and temporal coverage of the global ocean, uncertainties remain about whether Argo sampling frequency and density capture the true spatiotemporal variability in physical, biogeochemical, and biological properties. As the true distributions of, e.g., temperature or oxygen are unknown, these uncertainties remain difficult to address with Argo floats alone. Numerical models with synthetic observing systems offer one potential avenue to address these uncertainties. Here, we implement synthetic biogeochemical Argo floats into the Energy Exascale Earth System Model version 2 (E3SMv2), which build on the Lagrangian In Situ Global High-Performance Particle Tracking (LIGHT) module in E3SMv2 (E3SMv2-LIGHT-bgcArgo-1.0). Since the synthetic floats sample the model fields at model run time, the end user defines the sampling protocol ahead of any model simulation, including the number and distribution of synthetic floats to be deployed, their sampling frequency, and the prognostic or diagnostic model fields to be sampled. Using a 6-year proof-of-concept simulation, we illustrate the utility of the synthetic floats in different case studies. In particular, we quantify the impact of (i) sampling density on the float-derived detection of deep-ocean change in temperature or oxygen and on float-derived estimates of phytoplankton phenology, (ii) sampling frequency and sea-ice cover on float trajectory lengths and hence float-derived estimates of current velocities, and (iii) short-term variability in ecosystem stressors on estimates of their seasonal variability.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Experiments with the Mesoscale Atmospheric Simulation System (MASS) using the synthetic relative humidity

This study is intended to examine the impact of the synthetic relative humidity on the model simulation of mesoscale convective storm environment. The synthetic relative humidity is derived from the National Weather Services surface observations, and non-conventional sources including aircraft, radar, and satellite observations. The latter sources provide the mesoscale data of very high spatial and temporal resolution. The synthetic humidity data is used to complement the National Weather Services rawinsonde observations. It is believed that a realistic representation of initial moisture field in a mesoscale model is critical for the model simulation of thunderstorm development, and the formation of non-convective clouds as well as their effects on the surface energy budget. The impact will be investigated based on a real-data case study using the mesoscale atmospheric simulation system developed by Mesoscale Environmental Simulations Operations, Inc. The mesoscale atmospheric simulation system consists of objective analysis and initialization codes, and the coarse-mesh and fine-mesh dynamic prediction models. Both models are a three dimensional, primitive equation model containing the essential moist physics for simulating and forecasting mesoscale convective processes in the atmosphere. The modeling system is currently implemented at the Applied Meteorology Unit, Kennedy Space Center. Two procedures involving the synthetic relative humidity to define the model initial moisture fields are considered. It is proposed to perform several short-range (approximately 6 hours) comparative coarse-mesh simulation experiments with and without the synthetic data. They are aimed at revealing the model sensitivities should allow us both to refine the specification of the observational requirements, and to develop more accurate and efficient objective analysis schemes. The goal is to advance the MASS (Mesoscal Atmospheric Simulation System) modeling expertise so that the model output can provide reliable guidance for thunderstorm forecasting.

Chang, Chia-Bo↗

NASA NTRS2005

Flight Test Comparison Between Enhanced Vision (FLIR) and Synthetic Vision Systems

Limited visibility and reduced situational awareness have been cited as predominant causal factors for both Controlled Flight Into Terrain (CFIT) and runway incursion accidents. NASA s Synthetic Vision Systems (SVS) project is developing practical application technologies with the goal of eliminating low visibility conditions as a causal factor to civil aircraft accidents while replicating the operational benefits of clear day flight operations, regardless of the actual outside visibility condition. A major thrust of the SVS project involves the development/demonstration of affordable, certifiable display configurations that provide intuitive out-the-window terrain and obstacle information with advanced pathway guidance. A flight test evaluation was conducted in the summer of 2004 by NASA Langley Research Center under NASA s Aviation Safety and Security, Synthetic Vision System - Commercial and Business program. A Gulfstream G-V aircraft, modified and operated under NASA contract by the Gulfstream Aerospace Corporation, was flown over a 3-week period at the Reno/Tahoe International Airport and an additional 3-week period at the NASA Wallops Flight Facility to evaluate integrated Synthetic Vision System concepts. Flight testing was conducted to evaluate the performance, usability, and acceptance of an integrated synthetic vision concept which included advanced Synthetic Vision display concepts for a transport aircraft flight deck, a Runway Incursion Prevention System, an Enhanced Vision Systems (EVS), and real-time Database Integrity Monitoring Equipment. This paper focuses on comparing qualitative and subjective results between EVS and SVS display concepts.

Arthur, Jarvis J., III↗

NASA NTRS2007

Intelligent Entity Behavior Within Synthetic Environments

This paper describes some elements in the development of realistic performance and behavior in the synthetic entities (players) which support Modeling and Simulation (M&S) applications, particularly military training. Modern human-in-the-loop (virtual) training systems incorporate sophisticated synthetic environments, which provide: 1. The operational environment, including, for example, terrain databases; 2. Physical entity parameters which define performance in engineered systems, such as aircraft aerodynamics; 3. Platform/system characteristics such as acoustic, IR and radar signatures; 4. Behavioral entity parameters which define interactive performance, including knowledge/reasoning about terrain, tactics; and, 5. Doctrine, which combines knowledge and tactics into behavior rule sets. The resolution and fidelity of these model/database elements can vary substantially, but as synthetic environments are designed to be compose able, attributes may easily be added (e.g., adding a new radar to an aircraft) or enhanced (e.g. Amending or replacing missile seeker head/ Electronic Counter Measures (ECM) models to improve the realism of their interaction). To a human in the loop with synthetic entities, their observed veridicality is assessed via engagement responses (e.g. effect of countermeasures upon a closing missile), as seen on systems displays, and visual (image) behavior. The realism of visual models in a simulation (level of detail as well as motion fidelity) remains a challenge in realistic articulation of elements such as vehicle antennae and turrets, or, with human figures; posture, joint articulation, response to uneven ground. Currently the adequacy of visual representation is more dependant upon the quality and resolution of the physical models driving those entities than graphics processing power per Se. Synthetic entities in M&S applications traditionally have represented engineered systems (e.g. aircraft) with human-in-the-loop performance characteristics (e.g. visual acuity) included in the system behavioral specification. As well, performance affecting human parameters such as experience level, fatigue and stress are coming into wider use (via AI approaches) to incorporate more uncertainty as to response type as well as performance (e.g. Where an opposing entity might go and what it might do, as well as how well it might perform).

Kruk, R. V.↗

NASA NTRS2007

A Rapid Survey of the Compatibility of Selected Seal Materials with Conventional and Semi-Synthetic JP-8

Since the synthesis of a liquid hydrocarbon fuel from coal by Franz Fischer and Hans Tropsch in 1923, there has been cyclic interest in developing this fuel for military and commercial applications. In recent years the U.S. Department of Defense has taken interest in producing a unified battlespace fuel using the Fischer Tropsch (FT) process for a variety of reasons including cost, quality, and logistics. In the past year there has been a particular emphasis on moving quickly to demonstrate that an FT fuel can be used in the form of a blend with conventional petroleum-derived jet fuel. The initial objective is to employ this semi-synthetic fuel with blend ratios as high as 50 percent FT with longer range goals to use even high blend ratios and ultimately a fully synthetic jet fuel. A significant concern associated with the use of a semi-synthetic jet fuel with high FT blend ratios is the effect these low aromatic fuels will have on fuel-wetted polymeric materials, most notably seals and sealants. These materials typically swell and soften to some degree when exposed to jet fuel and the aromatic content of these fuels contribute to this effect. Semi-synthetic jet fuels with very low aromatic contents may cause seals and sealants to shrink and harden leading to acute or chronic failure. Unfortunately, most of the material qualification tests are more concerned with excessive swelling than shrinkage and there is little guidance offered as to an acceptable level of shrinkage or other changes in physical properties related to low aromatic content. Given the pressing need for guidance data, a program was developed to rapidly survey the volume swell of selected fuel-wetted materials in a range of conventional and semi-synthetic jet fuels and through a statistical analysis to make a determination as to whether there was a basis to be concerned about using fuels with FT blend ratios as high as 50 percent. Concurrent with this analysis data was obtained as to the composition of the fuel absorbed in fuel-wetted materials through the use of GC-MS analysis of swollen samples as well as other supporting data. In this presentation the authors will present a summary of the results of the volume swell and fuel absorbed by selected O-rings and sealants as well as a description of the measurement protocols developed for this program.

Graham, John L.↗

NASA NTRS2005

Microwave and Millimeter Wave Imaging Using Synthetic Aperture Focusing and Holographical Techniques

Microwave and millimeter wave nondestructive testing and evaluation (NDT&E) methods have shown great potential for determining material composition in composite structures, determining material thickness or debond thickness between two layers, and determining the location and size of flaws, defects, and anomalies. The same testing methods have also shown great potential to produce relatively high-resolution images of voids inside Spray On Foam Insulation (SOFI) test panels using real focused methods employing lens antennas. An alternative to real focusing methods are synthetic focusing methods. The essence of synthetic focusing is to match the phase of the scattered signal to measured points spaced regularly on a plane. Many variations of synthetic focusing methods have already been developed for radars, ultrasonic testing applications, and microwave concealed weapon detection. Two synthetic focusing methods were investigated; namely, a) frequency-domain synthetic aperture focusing technique (FDSAFT), and b) wide-band microwave holography. These methods were applied towards materials whose defects were of low dielectric contrast like air void in SOFI. It is important to note that this investigation used relatively low frequencies from 8.2 GHz to 26.5 GHz that are not conducive for direct imaging of the SOFI. The ultimate goal of this work has been to demonstrate the capability of these methods before they are applied to much higher frequencies such as the millimeter wave frequency spectrum (e.g., 30-300 GHz).

Case, Joseph Tobias↗

NASA NTRS2008

Millimeter Wave Synthetic Aperture Imaging System with a Unique Rotary Scanning System

In recent years, millimeter wave imaging techniques, using synthetic aperture focusing and holographical approaches, have shown tremendous potential for nondestructive testing applications, involving materials and structures used in space vehicles, including the space shuttle external fuel tank spray on foam insulation and its acreage heat tiles. The ability of signals at millimeter wave frequencies (30 - 300 GHz) to easily penetrate inside of low loss dielectric materials, their relatively small wavelengths, and the possibility of detecting coherent (magnitude and phase) reflections make them suitable for high resolution synthetic aperture focused imaging the interior of such materials and structures. To accommodate imaging requirements, commonly a scanning system is employed that provides for a raster scan of the desired structure. However, most such scanners, although simple in design and construction, are inherently slow primarily due to the need to stop and start at the beginning and end of each scan line. To this end, a millimeter wave synthetic aperture focusing system including a custom-designed transceiver operating at 35 - 45 GHz (Q-band) and unique and complex rotary scanner was designed and developed. The rotary scanner is capable of scanning an area with approximately 80 cm in diameter in less than 10 minutes at step sizes of 3 mm and smaller. The transceiver is capable of producing accurate magnitude and phase of reflected signal from the structure under test. Finally, a synthetic aperture focusing algorithm was developed that translates this rotary-obtained magnitude and phase into a synthetic aperture focusing image of inspected structures. This paper presents the design of the transceiver and the rotary scanning system along with showing several images obtained with this system from various complicated structures.

Ghasr, M. T.↗

NASA NTRS2007

Evaluating the Effects of Dimensionality in Advanced Avionic Display Concepts for Synthetic Vision Systems

Synthetic vision systems provide an in-cockpit view of terrain and other hazards via a computer-generated display representation. Two experiments examined several display concepts for synthetic vision and evaluated how such displays modulate pilot performance. Experiment 1 (24 general aviation pilots) compared three navigational display (ND) concepts: 2D coplanar, 3D, and split-screen. Experiment 2 (12 commercial airline pilots) evaluated baseline 'blue sky/brown ground' or synthetic vision-enabled primary flight displays (PFDs) and three ND concepts: 2D coplanar with and without synthetic vision and a dynamic multi-mode rotatable exocentric format. In general, the results pointed to an overall advantage for a split-screen format, whether it be stand-alone (Experiment 1) or available via rotatable viewpoints (Experiment 2). Furthermore, Experiment 2 revealed benefits associated with utilizing synthetic vision in both the PFD and ND representations and the value of combined ego- and exocentric presentations.

Alexander, Amy L.↗

NASA NTRSDate not supplied

Multiplanar Synthetic Arrays and Their Application to Full-Scale Landing Gear Noise Source Identification

Identification and quantification of noise sources generated by the landing gears of large civil transports are critical for the development of viable abatement technologies that can be applied locally to affect solely the source regions. Leveraging validated computational data sets obtained from airframe noise simulations of a full-scale B777-300ER aircraft, a frame-work based on multiplanar synthetic arrays was developed to examine landing gear sources. The position of the arrays relative to the aircraft nose or main landing gears, the number of microphones in each array, and their distribution pattern were judiciously selected to provide adequate spatial resolution of the sources in the primary flyover, sideline, and upstream directions while maintaining reasonable signal-to-noise ratio and beamwidth over a wide frequency range. Overall, seven synthetic arrays with 800 microphones each, for a total of 5,600 microphones, were used in the framework. Fully synchronous synthetic pressure records at each microphone location were obtained from the simulated data sets via a Ffowcs-Williams and Hawkings integral approach, with flow quantities on a permeable data surface enclosing the source regions used as input. Various applications of synthetic multiplanar arrays to acoustic data analysis are presented; the advantages of the framework in properly resolving and identifying the B777 landing gear sources is demonstrated by comparing three-dimensional source distribution maps with those obtained from a synthetic version of the ground-based microphone array used during flight tests of the same aircraft.

airframe noise↗

NASA NTRS2022

Quantifying Errors in 3D CME Parameters Derived from Synthetic Data Using White-Light Reconstruction Techniques

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .

Coronal mass ejections↗

NASA NTRS2022

Quantifying Errors in 3D CME Parameters Derived From Synthetic Data Using White-Light Reconstruction Techniques

Coronal mass ejections↗

NASA NTRSDate not supplied

Harnessing Synthetic Communities and Microbial Recycling of Space Waste Streams for Biomanufacturing Applications

The long-term habitation of extraterrestrial environments such as the Moon or Mars presents significant challenges including supplying materials to sustain life. Off-world recycling of waste materials into biomanufacturer products may ameliorate this. Current literature highlights the need for efficient waste recycling systems to support the bioproduction of essential materials including foods, pharmaceuticals, and biomaterials. The study herein concerns itself with the investigation of three key aspects: 1) formulating an optimal wastewater media on which to grow recombinant microbes for bioproduction in space, 2) examining the potential for constructing stable and metabolically synergistic synthetic microbial communities for largescale and multi-tiered biomanufacturing, and 3) testing the efficacy of one such bioengineered enzyme, cutinase, on the degradation of PET plastics characteristic of those found in ISS waste as a model for recombinant recycling-based biomanufacturing of mission-critical substrates. Formulation of an optimal wastewater media involved growing several microorganisms on mixtures of synthetic planetary wastes representative of those found in space waste systems, combined with simple carbon sources derived from a physio-chemical CO conversion system to determine their growth potential. Potential synthetic microbial communities were conceptually designed, and their stability and metabolic synergism was evaluated within the context of co-cultures. Cutinase activity assays were utilized to determine the efficacy of bioengineered cutinase on PET plastic degradation. Findings will contribute to optimization of wastewater-based media formulations, data on stable synthetic microbial communities for bioproduction, and effective methods for measuring cutinase-based PET plastic degradation. These outcomes support the development of sustainable waste recycling systems for space habitation and aim to fill gaps in the current literature and proposing innovative solutions for waste recycling in space environments. By leveraging synthetic biology this study seeks to enhance the feasibility of long-term extraterrestrial habitation through sustainable resource management.

Bioprocessing↗

DOE OSTI2025

Utilizing plant synthetic biology to accelerate plant-microbe interactions research

Plant-microbe interactions are critical to ecosystem resilience and substantially influence crop production. From the perspective of plant science, two important focus areas concerning plant-microbe interactions include: 1) understanding plant molecular mechanisms involved in plant-microbe interfaces and 2) engineering plants for increasing plant disease resistance or enhancing beneficial interactions with microbes to increase their resilience to biotic and abiotic stress conditions. Molecular biology and genetics approaches have been used to investigate the molecular mechanisms underlying plant responses to various beneficial and pathogenic microbes. While these approaches are valuable for elucidating the functions of individual genes and pathways, they fall short of unraveling the complex cross-talk across pathways or systems that plants employ to respond and adapt to environmental stresses. Also, genetic engineering of plants to increase disease resistance or enhance symbiosis with microbes has mainly been attempted or conducted through targeted manipulation of single genes/pathways of plants. Recent advancements in synthetic biology tool development are paving the way for multi-gene characterization and engineering in plants in relation to plant-microbe interactions. Here, we briefly summarize the current understanding of plant molecular pathways involved in plant interactions with beneficial and pathogenic microorganisms. Then, we highlight the progress in applying plant synthetic biology to elucidate the molecular basis of plant responses to microbes, enhance plant disease resistance, engineer synthetic symbiosis, and conduct in situ microbiome engineering. Lastly, we discuss the challenges, opportunities, and future directions for advancing plant-microbe interactions research using the capabilities of plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Quantitative Dissection of Agrobacterium Virulence to Generate a Synthetic Ti Plasmid

Agrobacterium is not only a costly plant pathogen but is also an essential tool for plant transformation. Though Agrobacterium-mediated transformation (AMT) has been heavily studied, its polygenic nature and complex transcriptional regulation make identification of the genetic basis of transformational efficiency difficult through traditional genetic and bioinformatic approaches. Here, we use a bottom-up synthetic approach to systematically engineer the tumor-inducing plasmid (pTi), wherein the majority of virulence machinery is encoded. Using a validated toolkit to control Agrobacterium gene expression in planta, we perform a quantitative dissection of AMT to investigate the contributions of critical vir-genes at different expression levels. We construct a synthetic pTi capable of transient plant and stable fungal transformation and characterize bottlenecks and solutions for complex polygenic synthetic pTi designs. Our reductionist approach demonstrates how bottom-up engineering can be used to dissect and elucidate the genetic underpinnings of complex biological traits, laying the foundation for future engineering to establish full synthetic control over the critical process of AMT.

Agrobacterium-mediated transformation↗

DOE OSTI2025

Energy, greenhouse gas, and water life cycle analysis of synthetic graphite anode production in the United States

This study presents a comprehensive life cycle analysis of potential synthetic graphite battery anode material (BAM) production in the U.S. based on industrial-scale data. The analysis focuses on three impacts: greenhouse gas (GHG) emissions, total energy use, and water consumption. We also conducted sensitivity analyses to evaluate the effect of variation in process parameters and energy sources used for synthetic graphite BAM production on its life cycle GHG emissions. A detailed supply chain analysis of graphite BAM in the U.S. was also undertaken, along with a study of its associated GHG emissions. The results show GHG emissions of 29.7 kg CO 2 -eq. per kg BAM, total energy use of 580 MJ kg −1 BAM, and water consumption of 121 L kg −1 BAM for the baseline condition. The graphitization step is a major process hotspot, contributing to over 74% of all impacts. This is attributed to the energy and material input requirements for this step, particularly through the use of crucibles. Across the entire synthetic graphite production process, electricity is the primary contributor, followed by crucibles used in graphite block production, and then calcined petroleum coke. Sensitivity analyses indicate that improvement in micronization yield, reuse of crucibles, and use of low-carbon nuclear energy can significantly reduce GHG emissions of potential domestic graphite production (by ∼70%). Supply chain analysis identified major graphite BAM sources in the U.S. and showed that the U.S. has a competitive advantage in domestic production of synthetic graphite BAM in terms of reduced life cycle GHG emissions compared to present-day imported sources (by ∼20%).

Battery anode↗