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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Catalytic Fast Pyrolysis Oil Stand-Alone and Co-Hydroprocessing Case Studies: Design, Cost, and Sustainability Based on Process Model Predictions

Refined liquid fuels provide conveniences for road, rail, air, and marine transportation due to high energy density, ease of transport and storage, and existing production and distribution infrastructure. Biomass conversion technologies can help diversify and increase the supply of liquid fuels towards a more robust energy future. The backbone of such development must include reliable, high-volume biomass supply chains and efficient utilization of those resources. These activities, if pursued, will lead to rural infrastructure development and new jobs.

09 BIOMASS FUELS↗
DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗
DOE OSTI2025

One-Dimensional Multi-Velocity Capabilities for Arbitrary Lagrangian-Eulerian Normal Contact Mechanics

Lagrangian and Arbitrary Lagrangian Eulerian (ALE) hydrodynamics codes such as FLAG form the backbone of many mission-critical multi physics simulations at Los Alamos National Laboratory. Critical to pre forming high fidelity simulations with these codes are Lagrangian and ALE contact algorithms, which allow materials to collide, slide, and sep arate throughout a simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SCEPTRE: A Cyber-Physical Emulation Capability

Cyber-physical systems form a critical but vulnerable backbone to US critical infrastructure. Recent high-profile cyber-attacks have shown the need for increased assessment and hardening of these systems. However, such assessments and investigations into advanced technologies to harden these systems is difficult due to their operational nature. Instead, modeling of these systems is heavily leveraged. Investigation into these complex systems and their potential cascading failures requires comprehensive modeling of both the cyber and physical components of the system. This paper introduces SCEPTRE, an emulation capability to address this need.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Passband Signal Detection at the Edge

Algorithms for radio frequency (RF) spectrum awareness need to be compatible with edge hardware to be practical for many applications. We developed a signal detection and classification model for the ZCU111 RF System-on-a-Chip (RFSoC) that operates on the fast Fourier transform of passband RF data. The system can detect and classify multiple signals of interest and display the predictions in real-time. The model consists of a modified ConvNeXt backbone and YOLOv3 head to operate on the Deep Learning Processing Unit on the RFSoC. We gathered datasets for training and testing by using a software defined radio to transmit example signals of Wi-Fi 802.11 b/g, Wi-Fi 802.11 n, FM Radio, LTE and LTE-M. By leveraging multiple inputs on the RFSoC frontend, the datasets span up to 4 GHz of bandwidth. The models showed high performance in classification accuracy, center frequency error, bandwidth error, and detection accuracy for both single and multi-signal datasets.

42 ENGINEERING↗
DOE OSTI2025

WRS Capabilities Booklet [Slides]

WRS is the digital backbone of the Weapons Program—delivering trusted data assets, cyber-assured software and systems, and AI-enabling software—that transform insights into decisive action. We empower physicists, engineers, researchers, and scientists to think faster, act strategically, and stay ahead in an ever-evolving threat landscape. Our efforts ensure critical nuclear weapons data remains secure, accessible, and usable—supporting mission-critical work, informed decision making, and scientific advancement at LANL and across the Nuclear Security Enterprise (NSE).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Klickitat Valley Health Central Utility Plant Modernization

This project replaced legacy heating and cooling equipment at Klickitat Valley Health (KVH), a rural hospital in Goldendale, Washington. Work included constructing a new chiller building, installing modern hydronic piping systems, replacing the main Emergency Department air handler (Air Handlining Unit (AHU-1)), upgrading electrical systems, and converting the Emergency Department wing from electric reheat to hydronic heating and cooling. Commissioning, testing, adjusting, and balancing were completed under this grant. These upgrades improve energy efficiency, operational resilience, and system reliability while preparing the campus for future electrification and renewable energy integration. They also support growth in healthcare services for a rural population. The project directly reduces energy costs and provides a foundation for future decarbonization measures, including a FEMA-funded microgrid and a large-scale ground source heat pump (GSHP) project. These follow-on initiatives would not have been possible without the hydronic backbone built through this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Mechanical Solutions Scan Report

Power lines, poles, and towers are the backbone of the United States (U.S.) electric-power grid. These transmission and distribution networks route electricity from generator to loads. The characteristics of these routes are rapidly changing -- trending towards decentralized renewable generation, electric heating, vehicle charging, and large data-center loads. Coupled with aging infrastructure and the increased frequency of extreme weather events, there is concern about the future reliability and transmission capacity of conductors and adjacent components. This scan report seeks to provide an overview of mechanical solutions to challenges caused by extreme weather events associated with components of transmission and distribution infrastructure, including conductor heat sag, ice accumulation, wind, and wildfire. Many options could increase transmission capacity or reliability, and these are at various stages of technological readiness. Some have only been lab tested, while some have been widely deployed in the U.S. or overseas for decades. The solution categories and providers featured in this report are intended to be comprehensive at the time of publication and to serve as a reference for decision-makers concerned about transmission and distribution reliability. There are two other categories of large, complex solutions, which are not covered in this report: replacing existing conductors with advanced conductors and implementing digital grid enhancing technologies. A separate scan report titled “Advanced Conductor Scan Report,” which discusses advanced carbon-core conductors, was published by the Idaho National Laboratory (INL) in 2023. Information on digital technologies, such as dynamic line ratings, power-flow controllers, and other power electronics and communications-based devices, can be found on the Grid- Enhancing Technologies landing page. Mechanical grid-enhancing technologies, or solutions covered in this report, often do not require full equipment replacement and do not rely on digital components. Mechanical technologies are overlooked because they may be older, simpler, or seemingly “more obvious” than digital or carbon-core technologies. However, it is wise to consider mechanical solutions in a thorough evaluation of grid enhancing technology solutions.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Bipolar Membranes Electrodialysis for Water Purification and Resource Recovery

Reverse osmosis concentrate (ROC) from inland brackish water desalination and wastewater recycling presents moderate salinity, posing significant challenges for treatment and disposal. Existing treatment technologies predominantly rely on energy-intensive thermal methods, large-area evaporation strategies, or deep-well disposal, which often result in the loss of water value. Electrified membrane brine treatment offers a promising alternative by utilizing renewable energy sources, enabling complete water recycling. This work proposes the development of next-generation bipolar membrane electrodialysis (BMED) systems capable of desalinating brine while simultaneously recovering valuable resources. However, several critical challenges remain in the areas of membrane engineering for BMED. These include: 1) decreased current efficiency during desalination due to the low conductivity of feed chambers, 2) fouling and scaling from multivalent ions and contaminants in the feed, and 3) the retention and carryover of ROC contaminants, including PFAS compounds. Our research focuses on the development of novel anion exchange membranes (AEMs) and cation exchange membranes (CEMs) based on synthesized multiblock copolymers. We investigate the effects of block length, ion exchange capacity (IEC), and polymer backbone structure on cation and anion transport (e.g., Na+, Cl-, OH-, H+) for both our synthesized membranes and commercial membranes. Fundamental properties of these membranes, such as water uptake, IEC, and ionic conductivity, are also evaluated to optimize performance for BMED applications.

08 HYDROGEN↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2024

Exploring Object Detection and Image Classification Tasks for Niche Use Case in Naturalistic Driving Studies

Naturalistic driving studies consist of drivers using their personal vehicles and provide valuable real-world data, but privacy issues must be handled very carefully. Drivers sign a consent form when they elect to participate, but passengers do not for a variety of practical reasons. However, their privacy must still be protected. One large study includes a blurred image of the entire cabin which allows reviewers to find passengers in the vehicle; this protects the privacy but still allows a means of answering questions regarding the impact of passengers on driver behavior. A method for automatically counting the passengers would have scientific value for transportation researchers. We investigated different image analysis methods for automatically locating and counting the non-drivers including simple face detection and fine-tuned methods for image classification and a published object detection method. We also compared the image classification using convolutional neural network and vision transformer backbones. Our studies show the image classification method appears to work the best in terms of absolute performance, although we note the closed nature of our dataset and nature of the imagery makes the application somewhat niche and object detection methods also have advantages. We perform some analysis to support our conclusion.

Peruski, Ryan↗
DOE OSTI2022

Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

The regulation of the cell wall by glycosylphosphatidylinositol-anchored proteins in Arabidopsis

A polysaccharides-based cell wall covers the plant cell, shaping it and protecting it from the harsh environment. Cellulose microfibrils constitute the cell wall backbone and are embedded in a matrix of pectic and hemicellulosic polysaccharides and glycoproteins. Various environmental and developmental cues can regulate the plant cell wall, and diverse glycosylphosphatidylinositol (GPI)-anchored proteins participate in these regulations. GPI is a common lipid modification on eukaryotic proteins, which covalently tethers the proteins to the membrane lipid bilayer. Catalyzed by a series of enzymic complexes, protein precursors are post-translationally modified at their hydrophobic carboxyl-terminus in the endomembrane system and anchored to the lipid bilayer through an oligosaccharidic GPI modification. Ultimately, mature proteins reach the plasma membrane via the secretory pathway facing toward the apoplast and cell wall in plants. In Arabidopsis, more than three hundred GPI-anchored proteins (GPI-APs) have been predicted, and many are reported to be involved in diverse regulations of the cell wall. In this review, we summarize GPI-APs involved in cell wall regulation. GPI-APs are proposed to act as structural components of the cell wall, organize cellulose microfibrils at the cell surface, and during cell wall integrity signaling transduction. Besides regulating protein trafficking, the GPI modification is potentially governed by a GPI shedding system that cleaves and releases the GPI-anchored proteins from the plasma membrane into the cell wall.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗
DOE OSTI2022

Hot Hydride Superconductivity Above 550 K

The search for room temperature superconductivity has accelerated in the last few years driven by experimentally accessible theoretical predictions that indicated alloying dense hydrogen with other elements could produce conventional superconductivity at high temperatures and pressures. These predictions helped inform the synthesis of simple binary hydrides that culminated in the discovery of the superhydride LaH 10 with a superconducting transition temperature T c of 260 K at 180 GPa. We have now successfully synthesized a metallic La-based superhydride with an initial T c of 294 K. When subjected to subsequent thermal excursions that promoted a chemical reaction to a higher order system, the Tc onset was driven irreversibly to 556 K. X-ray characterization confirmed the formation of a distorted LaH 10 based backbone that suggests the formation of ternary or quaternary compounds with substitution at the La and/or H sites. The results provide evidence for hot superconductivity, aligning with recent predictions for higher order hydrides under pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Future Vision for Autonomous Ocean Observations

Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass; and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as EGO for the emerging glider operations should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

The Hyperthermophilic Restriction-Modification Systems of Thermococcus kodakarensis Protect Genome Integrity

Thermococcus kodakarensis (T. kodakarensis), a hyperthermophilic, genetically accessible model archaeon, encodes two putative restriction modification (R-M) defense systems, TkoI and TkoII. TkoI is encoded by TK1460 while TkoII is encoded by TK1158. Bioinformative analysis suggests both R-M enzymes are large, fused methyltransferase (MTase)-endonuclease polypeptides that contain both restriction endonuclease (REase) activity to degrade foreign invading DNA and MTase activity to methylate host genomic DNA at specific recognition sites. In this work, we demonsrate T. kodakarensis strains deleted for either or both R-M enzymes grow more slowly but display significantly increased competency compared to strains with intact R-M systems, suggesting that both TkoI and TkoII assist in maintenance of genomic integrity in vivo and likely protect against viral- or plasmid-based DNA transfers. Pacific Biosciences single molecule real-time (SMRT) sequencing of T. kodakarensis strains containing both, one or neither R-M systems permitted assignment of the recognition sites for TkoI and TkoII and demonstrated that both R-M enzymes are TypeIIL; TkoI and TkoII methylate the N 6 position of adenine on one strand of the recognition sequences GTGAAG and TTCAAG, respectively. Further in vitro biochemical characterization of the REase activities reveal TkoI and TkoII cleave the DNA backbone GTGAAG(N) 20 /(N) 18 and TTCAAG(N) 10 /(N) 8 , respectively, away from the recognition sequences, while in vitro characterization of the MTase activities reveal transfer of tritiated S-adenosyl methionine by TkoI and TkoII to their respective recognition sites. Together these results demonstrate TkoI and TkoII restriction systems are important for protecting T. kodakarensis genome integrity from invading foreign DNA.

59 BASIC BIOLOGICAL SCIENCES↗