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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Respiratory energy demands and scope for demand expansion and destruction

Photosynthesis is the primary energy input to plants, but most plant metabolic processes are powered much or all of the time by ATP and NAD(P)H that come from respiratory oxidation of photosynthetically produced carbohydrates, that is, “dark respiration” (Amthor, 1994). The processes of growth, nutrient uptake and assimilation, active transport, and maintenance are thus all clients of respiration and compete for their share of a respiratory energy budget that is capped by photosynthate income. The economic principle of opportunity cost applies to these clients’ competing demands: spending respiratory energy on process A means missing the benefit of investing that energy in process B (Mahmoudabadi et al., 2019). This principle is key to assessing prospects for crop improvement by metabolic engineering. As de Lorenzo (2015) put it: “metabolism…frames and ultimately resolves whether a given genetic program (existing…or engineered) can be deployed or not.” Foreign or reconfigured native processes bolted on to a crop-plant’s metabolic chassis must compete for respiratory energy with native ones without crashing the energy economy. A proviso on plant carbon budgets is that photosynthetic carbon fixation (“source activity”) can in certain cases increase to meet increased carbon demand (“sink activity”), that is, budget envelopes are not always fixed (Smith et al., 2018). However, as there is some consensus that productivity is most often limited or co-limited by carbon supply (Ainsworth and Long, 2005; Körner, 2015; Sonnewald and Fernie, 2018), we make this our basal assumption in the analyses below.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Performance of Public Film Cooling Geometries Produced Through Additive Manufacturing

Abstract Film cooling is an essential cooling technology to allow modern gas turbines to operate at high temperatures. For years, researchers in this community have worked to improve the effectiveness of film cooling configurations by maximizing the coolant coverage and minimizing the heat flux from the hot gas into the part. Working toward this goal has generated many promising film cooling concepts with unique shapes and configurations. However, until recently, many of these designs were challenging to manufacture in actual turbine hardware due to limitations with legacy manufacturing methods. Now, with the advances in additive manufacturing, it is possible to create turbine parts using high-temperature nickel alloys that feature detailed and unique geometry features. Armed with this new manufacturing power, this study aims to build and test the promising designs from the public literature that were previously difficult or impossible to implement. In this study, different cooling hole designs were manufactured in test coupons using a laser powder bed fusion process. Each nickel alloy coupon featured a single row of engine scale cooling holes, fed by a microchannel. To evaluate performance, the overall cooling effectiveness of each coupon was measured using a matched Biot test at engine relevant conditions. The results showed that certain hole shapes are better suited for additive manufacturing than others and that the manufacturing process can cause significant deviations from the performance reported in the literature.

Engineering↗
DOE OSTI2020

Spatiotemporal flame propagations, combustion and solid particle emissions from lean and stoichiometric gasoline direct injection engine operation

Increased particle number and mass emissions in gasoline direct injection (GDI) engines should require to investigate in-cylinder flame and combustion characteristics associated with primary source of particle emissions. In this article, in-cylinder spatiotemporal flame luminosity is quantitatively characterized to features combustion process and solid particle emissions from a GDI engine operating in two lean and one stoichiometric modes. Low- and high-steady state operating points were used to compare combustion strategies on flame development and emission characteristics. A fiber-optic sensor composed of eight measurement channels detected the flame front and the direction of the flame propagation in the combustion chamber. Solid particle emissions in the exhaust were measured using an engine exhaust particle sizer and a micro soot sensor. Results of the experiments showed that two lean combustion modes by injection strategies resulted in distinct combustion and flame development. Lean combustion modes generated high diffusion flame by burning stratified rich-mixture. Although the lean cases resulted in strong diffusion flames, the lean-homogeneous produced similar particle size distributions with the stoichiometric mode with high ash particles. Piston pool fires on the piston surface in the lean-stratified mode induced a large accumulation mode with high particle mass concentrations.

42 ENGINEERING↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $6B annually (at $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year),and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE)missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2020

Production of Cf-252 and other transplutonium isotopes at Oak Ridge National Laboratory

In 1957 Glenn T. Seaborg conceived and advocated for the construction of the High Flux Isotope Reactor (HFIR) and the Transuranium Processing Plant (since then renamed the Radiochemical Engineering Development Center, or REDC) at Oak Ridge National Laboratory. There, heavily shielded hot cells, glove boxes, and laboratories allow recovery of transuranium elements produced in substantial quantities. Seaborg’s vision of HFIR and REDC producing milligram quantities of berkelium, californium, and einsteinium has been fulfilled beginning in 1966 through May 2019 with 78 production campaigns yielding a cumulative totals of 1.2 g of 249 Bk, 10.2 g of 252 Cf, 39 mg of 253 Es, and 15 pg of 257 Fm. Notably, 252 Cf is a neutron source used in many industrial applications including oil exploration; process control systems for the cement industry, coal analysis, and power production; sources to start nuclear reactors and perform nondestructive materials analyses; homeland security and national defense detection devices; and medical research. Isotopes made available through transplutonium production at HFIR/REDC have enabled scientists to study the nuclear properties and reactions, chemical properties, optical properties, and solid-state properties of transplutonium elements. Long-lived isotopes have served as targets in heavy ion accelerators to produce heavier elements leading to the discovery of 104 Rf, 105 Db, 106 Sg, 113 Nh, 114 Fl, 115 Mc, 116 Lv, 117 Ts, and 118 Og. This paper reviews the evolution of the processing flowsheets to produce, separate, and purify transplutonium isotopes, which have evolved over 50 years of operation at HFIR and REDC, and summarizes directions of future work to improve the efficiency of the production operations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Na + -gated nanochannel membrane for highly selective ammonia (NH 3 ) separation in the Haber-Bosch process

Currently, cryogenic condensation is the predominant process for recovering ammonia (NH 3 ) in the Haber-Bosch (HB) process, which is highly energy intensive. To be more compatible with the reaction conditions in the HB process and thus minimize the pressure and temperature swing during reactant recycling, energy-efficient technologies for NH 3 extraction at elevated temperature and pressure are greatly needed. In this work, the Na + -gated nanochannel membrane, shown exclusively for water conduction in our previous work, also exhibited highly NH 3 -selective performance, with NH 3 /H 2 selectivity as high as 4,280 and NH 3 /N 2 selectivity > 10,000 at temperature up to 250 °C and pressure up to 35 bar. Excellent stability of the Na + -gated nanochannel membrane was demonstrated during a 100-h run in ternary NH 3 /H 2 /N 2 gas mixture at 200 °C and 35 bar, consistent with structural characterization by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transfer infrared (FTIR) spectroscopy. A techno-economic analysis (TEA) was conducted for the HB process using the Na + -gated nanochannel membrane and the traditional HB process with condenser. Finally, under the optimized separation conditions, > 80% energy savings and approximately 20% reduction of the net NH 3 production cost can be achieved, demonstrating the great potential of the Na + -gated nanochannel membrane for NH 3 separation in the HB process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mixed matrix membranes for post-combustion carbon capture: From materials design to membrane engineering

Mixed matrix materials (MMMs) containing nanofillers dispersed in continuous polymer matrices have emerged as an exciting and versatile platform to develop membranes with superior CO 2 /N 2 separation performance for post-combustion carbon capture. Both polymers and nanofillers can be vertically designed and engineered to combine the advantages of excellent processability (derived from polymers) and strong size-sieving ability or/and significant permanent porosity (originated from nanofillers). However, the MMMs face major challenges, such as interfacial incompatibility, particle agglomeration, and poor thin-film formability. This report provides a comprehensive yet critical review of various polymers and nanofillers (such as metal-organic frameworks, covalent organic frameworks, and two-dimensional materials) with promising CO 2 /N 2 separation properties. We exhaustively describe strategies to improve interfacial compatibility, such as in situ syntheses of polymers and nanofillers and functionalization of both components to improve adhesion. Moreover, we highlight various approaches to engineer the MMMs into thin-film composite (TFC) membranes. The review reveals the structure/property relationship in these MMMs and outlines the challenges and opportunities to realize their potential for practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In-cylinder spray evolution in a motored central-injection gasoline engine: Imaging and simulating the effects of flash-boiling and intake crossflow

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. Further, it is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2023

Distributed Ledger Technology for Fault Tolerant Distribution Grid Operations

This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.001% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Phase transition in two-dimensional monolayer (1L)-molybdenum disulfide induced by atomic S-basal plane gliding via synchrotron X-ray monochromatic beam radiation for superior electronic performance

Here, in this work, we report a novel approach to reduce the channel resistance by inducing a phase transition behavior from 2H to 1T in a monolayer MoS 2 (1L-MoS 2 ) by a synchrotron X-ray monochromatic beam (mono-beam) radiation. The effects of the biphase structure by the mono-beam on the 1L-MoS2 film were investigated using Raman spectra, photoluminescence (PL) spectra, scanning tunneling microscopy, and scanning tunneling spectroscopy, respectively. Through material characterization, we identified that the lateral sliding of S-vacancies along the S-plane in the 1L-MoS 2 is the key reason for the origin of unidirectional phase transition. The precise phase engineering triggered by the mono-beam radiation process allows the realization of field-effect transistors (FET) with 2X improvement in mobility toward a high on/off ratio (~10 8 ) and a near-ideal subthreshold swing of ~88 mV per decade. The validity of the phase engineering could be further extended for its application as a memory device, exhibiting a gate tunable conduction modulation behavior and a high resistance ratio of ~10 2 at a gate bias of 5 V with endurance of ~100 cycles. Furthermore, an artificial neural network using the synaptic weight update with accuracy of ~93 % was achieved.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Orbital engineering of C 3 N monolayer to design efficient synergistic sites electrocatalyst for boosting alkaline hydrogen evolution

Alkaline water electrolyzer (AWE) is one of the promising technologies for hydrogen production at the industrial level. However, energetic inefficiency and low current density impede the development of AWE. Compared with acidic conditions, the Volmer step in alkaline hydrogen evolution reaction (HER) involves extra water dissociation, whose barrier is one of the most vital reasons for the sluggish kinetics of alkaline HER. Herein, choosing C 3 N monolayer as an ideal theoretical model, we design several empty orbitals through intentional metal doping, and further construct synergistic sites on the C 3 N monolayer to accelerate both water dissociation and hydrogen adsorption for alkaline HER. Furthermore, the as-designed Be-doped and Cr-doped C 3 N monolayers exhibit rather low theoretical overpotential of 0.476 eV and 0.216 eV for alkaline HER, respectively, which are even lower than Pt (1 1 1) surface. Moreover, by comparing the water dissociation behaviors on metal-doped C3N monolayer, we find that the empty orbitals with suitable orientation and energy level are useful for promoting the water dissociation process, indicating that we can use orbital engineering strategy to regulate the adsorption strength between adsorbate and surface site. Consequently, it is reasonable to suggest that our orbital engineering strategy would significantly benefit the design of highly efficient alkaline HER electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Engineered reduction of S-adenosylmethionine alters lignin in sorghum

Abstract Background Lignin is an aromatic polymer deposited in secondary cell walls of higher plants to provide strength, rigidity, and hydrophobicity to vascular tissues. Due to its interconnections with cell wall polysaccharides, lignin plays important roles during plant growth and defense, but also has a negative impact on industrial processes aimed at obtaining monosaccharides from plant biomass. Engineering lignin offers a solution to this issue. For example, previous work showed that heterologous expression of a coliphage S -adenosylmethionine hydrolase (AdoMetase) was an effective approach to reduce lignin in the model plant Arabidopsis. The efficacy of this engineering strategy remains to be evaluated in bioenergy crops. Results We studied the impact of expressing AdoMetase on lignin synthesis in sorghum ( Sorghum bicolor L. Moench). Lignin content, monomer composition, and size, as well as biomass saccharification efficiency were determined in transgenic sorghum lines. The transcriptome and metabolome were analyzed in stems at three developmental stages. Plant growth and biomass composition was further evaluated under field conditions. Results evidenced that lignin was reduced by 18% in the best transgenic line, presumably due to reduced activity of the S -adenosylmethionine-dependent O -methyltransferases involved in lignin synthesis. The modified sorghum features altered lignin monomer composition and increased lignin molecular weights. The degree of methylation of glucuronic acid on xylan was reduced. These changes enabled a ~20% increase in glucose yield after biomass pretreatment and saccharification compared to wild type. RNA-seq and untargeted metabolomic analyses evidenced some pleiotropic effects associated with AdoMetase expression. The transgenic sorghum showed developmental delay and reduced biomass yields at harvest, especially under field growing conditions. Conclusions The expression of AdoMetase represents an effective lignin engineering approach in sorghum. However, considering that this strategy potentially impacts multiple S -adenosylmethionine-dependent methyltransferases, adequate promoters for fine-tuning AdoMetase expression will be needed to mitigate yield penalty.

Tian, Yang↗
DOE OSTI2020

Genetic circuit design automation for yeast

Cells can be programmed to monitor and react to their environment using genetic circuits. Design automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, in this work we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

AUTONOMIE VID

Autonomie Vehicle Information Database (VID) offers a comprehensive list of vehicle specifications since 1990. The database details more than 65,000 vehicles with hundreds of attributes. The database is the result of the development of a general automated data collection framework as well as the development of building blocks for processing, cleaning, integrating and analyzing complex data. The data has undergone several layers of outlier detections processes, machine learning based imputations methods have been used to deal with missing data problems, and new fields have been created according to the rules of feature engineering. Thanks to this streamlined data pipelines, the resulting processed aggregated data should deliver a unique level of information to the user in which the content can be efficiently maintained and updated.

Moswd, Ayman↗