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At least 1,693 records · Page 94

A Simple and Versatile 2-Dimensional Platform to Study Plant Germination and Growth under Controlled Humidity

We describe a simple, inexpensive, but remarkably versatile and controlled growth environment for the observation of plant germination and seedling root growth on a flat, horizontal surface over periods of weeks. The setup provides to each plant a controlled humidity (between 56% and 91% RH), and contact with both nutrients and atmosphere. The flat and horizontal geometry of the surface supporting the roots eliminates the gravitropic bias on their development and facilitates the imaging of the entire root system. Experiments can be setup under sterile conditions and then transferred to a non-sterile environment. The system can be assembled in 1-2 minutes, costs approximately 8.78$\$$ per plant, is almost entirely reusable (0.43$\$$ per experiment in disposables), and is easily scalable to a variety of plants. We demonstrate the performance of the system by germinating, growing, and imaging Wheat (Triticum aestivum), Corn (Zea mays), and Wisconsin Fast Plants (Brassica rapa). Germination rates were close to those expected for optimal conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from π-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water-soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence–structure–function relations remain poorly characterized. Here, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically active π-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20 3 = 8000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nanoscale Control of Intrinsic Magnetic Topological Insulator MnBi 2 Te 4 Using Molecular Beam Epitaxy: Implications for Defect Control

Intrinsic magnetic topological insulators have emerged as a promising platform to study the interplay between the topological surface states and ferromagnetism. This unique interplay can give rise to a variety of exotic quantum phenomena, including the quantum anomalous Hall effect and axion insulating states. Here, in this study, utilizing molecular beam epitaxy (MBE), we present a comprehensive study of the growth of MnBi 2 Te 4 thin films on Si (111), epitaxial graphene, and highly ordered pyrolytic graphite substrates. By combining a suite of in situ characterization techniques, we obtain critical insights into the nanoscale control of MnBi 2 Te 4 epitaxial growth. First, we extract the free energy landscape for the epitaxial relationship as a function of the in-plane angular distribution. Then, by employing an optimized layer-by-layer growth, we determine the chemical potential and Dirac point of the thin film at different thicknesses and how this quantity is manifested by the dopant compensation from different antisite defects. Overall, these results establish a foundation for understanding the growth kinetics of MnBi 2 Te 4 and pave the way for future applications of MBE-grown thin films in emerging topological quantum materials.

36 MATERIALS SCIENCE↗

Potential up-scaling of inkjet-printed devices for logical circuits in flexible electronics

Inkjet Technology is often mis-believed to be a deposition/patterning technology which is not meant for high fabrication throughput in the field of printed and flexible electronics. In this work, we report on the 1) printing, 2) fabrication yield and 3) characterization of exemplary simple devices e.g. capacitors, organic transistors etc. which are the basic building blocks for logical circuits. For this purpose, printing is performed first with a Proof of concept Inkjet printing system Dimatix Material Printer 2831 (DMP 2831) using 10 pL small print-heads and then with Dimatix Material Printer 3000 (DMP 3000) using 35 pL industrial print-heads (from Fujifilm Dimatix). Printing at DMP 3000 using industrial print-heads (in Sheet-to-sheet) paves the path towards industrialization which can be defined by printing in Roll-to-Roll format using industrial print-heads. This pavement can be termed as 'Bridging Platform'. This transfer to 'Bridging Platform' from 10 pL small print-heads to 35 pL industrial print-heads help the inkjet-printed devices to evolve on the basis of functionality and also in form of up-scaled quantities. The high printed quantities and yield of inkjet-printed devices justify the deposition reliability and potential to print circuits. This reliability is very much desired when it comes to printing of circuits e.g. inverters, ring oscillator and any other planned complex logical circuits which require devices e.g. organic transistors which needs to get connected in different staged levels. Also, the up-scaled inkjet-printed devices are characterized and they reflect a domain under which they can work to their optimal status. This status is much wanted for predicting the real device functionality and integration of them into a planned circuit.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗

Recent Progress in Electrochemical Upgrading of Bio-Oil Model Compounds and Bio-Oils to Renewable Fuels and Platform Chemicals

Sustainable production of renewable carbon-based fuels and chemicals remains a necessary but immense challenge in the fight against climate change. Bio-oil derived from lignocellulosic biomass requires energy-intense upgrading to produce usable fuels or chemicals. Traditional upgrading methods such as hydrodeoxygenation (HDO) require high temperatures (200–400 °C) and 200 bar of external hydrogen. Electrochemical hydrogenation (ECH), on the other hand, operates at low temperatures (<80 °C), ambient pressure, and does not require an external hydrogen source. These environmental and economically favorable conditions make ECH a promising alternative to conventional thermochemical upgrading processes. ECH combines renewable electricity with biomass conversion and harnesses intermediately generated electricity to produce drop-in biofuels. This review aims to summarize recent studies on bio-oil upgrading using ECH focusing on the development of novel catalytic materials and factors impacting ECH efficiency and products. Here, electrode design, reaction temperature, applied overpotential, and electrolytes are analyzed for their impacts on overall ECH performance. We find that through careful reaction optimization and electrode design, ECH reactions can be tailored to be efficient and selective for the production of renewable fuels and chemicals. Preliminary economic and environmental assessments have shown that ECH can be viable alternative to convention upgrading technologies with the potential to reduce CO2 emissions by 3 times compared to thermochemical upgrading. While the field of electrochemical upgrading of bio-oil has additional challenges before commercialization, this review finds ECH a promising avenue to produce renewable carbon-based drop-in biofuels. Finally, based on the analyses presented in this review, directions for future research areas and optimization are suggested.

09 BIOMASS FUELS↗

Tailored Micromagnet Sorting Gate for Simultaneous Multiple Cell Screening in Portable Magnetophoretic Cell‐On‐Chip Platforms

Abstract Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large‐scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell‐on‐chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two‐step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano‐notch in a low magnetic field system. The motion of the magnetic energy well is regulated by the configuration of the nanoscale notch and the strength and frequency of the magnetic field, accompanying the jump motion of the carriers. The proposed concepts demonstrate that multiple carriers and cells can be manipulated and sorted using optimized nanoscale multi‐notch gates for a portable magnetophoretic system. This highlights the potential for developing cost‐effective point‐of‐care testing and lab‐on‐chip systems for various single‐cell‐level diagnoses and analyses.

36 MATERIALS SCIENCE↗

Carbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection

High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.

54 ENVIRONMENTAL SCIENCES↗

Enabling Floating Offshore VAWT Design by Coupling OWENS and OpenFAST

Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.

17 WIND ENERGY↗

Electric Drive Technologies Consortium (EDTC)/ Cost competitive, high-Performance, highly Reliable (CPR) Power Devices on 4H-SiC (Final Report)

4H-Silicon carbide (4H-SiC) is a wide bandgap semiconductor that offers superior material properties over silicon, including higher critical electric field, thermal conductivity, and electron saturation velocity. These advantages make 4H-SiC highly attractive for high-voltage, high-efficiency power electronics. However, realizing the full potential of SiC requires device technologies that are not only high-performing but also manufacturable and reliable under real-world operating conditions. This report summarizes the outcomes of a five-year R&D effort funded by the U.S. Department of Energy (DOE) under the Electric Drive Technologies Consortium (EDTC), focused on developing cost-competitive, high-performance, and highly reliable (CPR) power devices on 4H-SiC substrates. The program targeted scalable and manufacturable 1.2 kV-class SiC MOSFETs optimized for next-generation electric vehicles, renewable energy systems, and industrial power conversion. The project delivered transformative advancements in SiC power device performance and ruggedness. Particularly, Specific on-resistance (R on,sp ) was reduced by up to 37%, from ~4.0 m$\Omega \cdot$cm 2 in earlier designs to an industry-leading 2.40 m$\Omega \cdot$cm 2 , driven by optimized doping, refined JFET widths, and layout engineering. Breakdown voltages (BV) exceeded 1600 V, marking improvement over legacy baselines, and demonstrating the robustness of newly implemented junction profiles and edge terminations. Short-circuit withstand time (SCWT) saw a remarkable 4$\times$ increase, from ~2 $\mu$s to over 8 $\mu$s, achieved through the successful deployment of deep P-well structures (~1.8–2.0 $\mu$m) via channeling implantation. This innovative process breakthrough enabled precise junction formation without MeV-class implantation tools, reduced leakage under high field stress, and allowed even the shortest-channel devices (down to 0.3 $\mu$m) to achieve both high BV and excellent ruggedness—breaking the traditional trade-off between conduction efficiency and blocking capability. Several novel architectures pushed the performance envelope further. JBSFETs—featuring embedded Schottky portions—eliminated bipolar degradation and drastically reduced third-quadrant leakage, while Ladder MOSFETs introduced a clever orthogonal conduction path that achieved a 15.4% reduction in R on,sp over standard linear designs. Switching performance reached new benchmarks: short-channel devices showed a 31% reduction in total switching energy compared to 0.5 $\mu$m counterparts, while maintaining manageable gate drive requirements. Layout-optimized structures not only improved transconductance but also accelerated switching transitions, pointing to real-world benefits in converter-level efficiency. The devices also passed rigorous reliability validation. Stress-tested across TDDB, HTGB, HTRB, HVP, and burn-in, the devices screened under 30 V/10 hr and 43 V/1 s protocols consistently exhibited tighter lifetime distributions and long-term oxide robustness. These screening techniques proved effective in identifying latent defects and ensuring deployment-grade reliability. Meanwhile, advanced 3D TCAD simulations revealed and resolved electric field hotspots—particularly in HEXFET corners—where fields exceeding 4.8 MV/cm were mitigated through geometry-aware layout corrections. Overall, the results of this project demonstrate a manufacturable and scalable SiC power device platform that addresses key DOE performance targets for efficient, robust, and reliable 1.2kV 4H-SiC Power Devices. The developed technologies represent a meaningful step forward in the commercial readiness of high-voltage SiC solutions and provide a strong foundation for continued advancement in wide bandgap power electronics.

42 ENGINEERING↗

Embedded Sensors and Controls to Improve Component Performance and Reliability - System Dynamics Modeling and Control System Design

This report documents the current status of the modeling, control design, and embedded control research for the magnetic bearing canned rotor pump being used as a demonstration platform for deeply integrating instrumentation and controls (I&C) into nuclear power plant components. This pump is a highly inter-connected thermo/electro/mechanical system that requires an active control system to operate. Magnetic bearings are inherently unstable system and without active, moment by moment control, the rotor would contact fixed surfaces in the pump causing physical damage. This report details the modeling of the pump rotordynamics, fluid forces, electromagnetic properties of the protective cans, active magnetic bearings, power electronics, and interactions between different dynamical models. The system stability of the unforced and controlled rotor are investigated analytically. Additionally, controllers are designed using proportional derivative (PD) control, proportional integral derivative (PID) control, voltage control, and linear quadratic regulator (LQR) control. Finally, a design optimization problem that joins the electrical, mechanical, magnetic, and control system design into one problem to balance the opposing needs of various design criteria using the embedded system approach is presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020

With the surge in electric vehicle (EV) adoption and the need for extended driving ranges, optimizing energy efficiency, particularly through thermal management, is critical, especially in extreme weather. Managing the substantial energy needed for cabin climate control and battery temperature regulation can increase energy demands by over 50 %, severely limiting range. This study conducts a comparative analysis of thermal management systems (TMS) in three popular EV vehicles, 2020 Chevrolet Bolt, 2019 Nissan Leaf Plus, and 2020 Tesla Model 3, evaluating their distinct TMS configurations and performance under varied weather conditions. Using both numerical simulations and experimental data collected on a controlled test bench at Argonne National Laboratory, we assess how TMS architecture and operational modes influence energy consumption and range. A comprehensive TMS model was developed, integrating cabin and battery thermal sub-models in the Autonomie software platform, to simulate temperature fluctuations and range impacts. Cabin climate was modeled using a mono-zonal approach, while battery cell temperature distribution was estimated through a 2D nodal structure. Each vehicle's distinct TMS setup was evaluated: the Chevrolet Bolt and Tesla Model 3 use a dual evaporator vapor compression cycle with a PTC heater for the cabin and a coolant loop for battery thermal management; the Nissan Leaf Plus employs a heat pump with a PTC heater for the cabin and air-cooling for the battery. Tests conducted at ambient temperatures of 35°C, 22°C, -7°C, and -18°C reveal significant differences in energy use and range reduction across both configurations and conditions. At 35°C, the Tesla Model 3, Chevrolet Bolt, and Nissan Leaf Plus have a range reduction of 8%, 9%, and 13%, respectively, due to air conditioning. In winter, heating technology is paramount; at -7°C, the Nissan Leaf's heat pump configuration achieves a lower range reduction (19.3%) compared to the Tesla and Chevrolet Bolt PTC heaters, which reduce range by 28.3% and 31%, respectively. Further, this study provides valuable insights for automotive engineers, EV technology researchers, and thermal management system designers aiming to enhance electric vehicle performance by understanding how different weather conditions and TMS architectures impact energy consumption and driving range.

33 ADVANCED PROPULSION SYSTEMS↗

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

59 BASIC BIOLOGICAL SCIENCES↗

High Performance Computing Modeling Advances Accelerator Science for High-Energy Physics

The development and optimization of particle accelerators are essential for advancing our understanding of the properties of matter, energy, space, and time. Particle accelerators are complex devices whose behavior involves many physical effects on multiple scales. Therefore, advanced computational tools utilizing high-performance computing are essential for accurately modeling them. In the past decade, the US Department of Energy's SciDAC program has produced accelerator-modeling tools that have been employed to tackle some of the most difficult accelerator science problems. The authors discuss the Synergia framework and its applications to high-intensity particle accelerator physics. Synergia is an accelerator simulation package capable of handling the entire spectrum of beam dynamics simulations. Our authors present Synergia's design principles and its performance on HPC platforms.

43 PARTICLE ACCELERATORS↗

A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

25 ENERGY STORAGE↗

SU-E-J-42: Customized Deformable Image Registration Using Open-Source Software SlicerRT

Purpose: SlicerRT is a flexible platform that allows the user to incorporate the necessary images registration and processing tools to improve clinical workflow. This work validates the accuracy and the versatility of the deformable image registration algorithm of the free open-source software SlicerRT using a deformable physical pelvic phantom versus available commercial image fusion algorithms. Methods: Optical camera images of nonradiopaque markers implanted in an anatomical pelvic phantom were used to measure the ground-truth deformation and evaluate the theoretical deformations for several DIR algorithms. To perform the registration, full and empty bladder computed tomography (CT) images of the phantom were obtained and used as fixed and moving images, respectively. The DIR module, found in SlicerRT, used a B-spline deformable image registration with multiple optimization parameters that allowed customization of the registration including a regularization term that controlled the amount of local voxel displacement. The virtual deformation field at the center of the phantom was obtained and compared to the experimental ground-truth values. The parameters of SlicerRT were then varied to improve spatial accuracy. To quantify image similarity, the mean absolute difference (MAD) parameter using Hounsfield units was calculated. In addition, the Dice coefficient of the contoured rectum was evaluated to validate the strength of the algorithm to transfer anatomical contours. Results: Overall, SlicerRT achieved one of the lowest MAD values across the algorithm spectrum, but slightly smaller mean spatial errors in comparison to MIM software (MIM). On the other hand, SlicerRT created higher mean spatial errors than Velocity Medical Solutions (VEL), although obtaining an improvement on the DICE to 0.91. The large spatial errors were attributed to the poor contrast in the prostate bladder interface of the phantom. Conclusion: Based phantom validation, SlicerRT is capable of achieving comparable DIR accuracy to commercial programs such as MIM and VEL.

60 APPLIED LIFE SCIENCES↗

Modular Controllable Transformers (MCT)

Large Power Transformers (LPTs) are at the heart of the electricity infrastructure and are critical pieces in today’s grid. Aging assets, long turn-around times, transportation delays and dependence on foreign manufacturing for these components have created conditions for points of failure across the system. Moreover, there is no available dynamic control on these passive sections of the grid. For all the automation embedded on the low voltage side of the low voltage side of the system there is absolutely no flexibility on the high voltage side. Changes in power flow patterns are brought about by altering dispatch patterns for generators which requires a coordinated effort and is often time consuming. Traditional FACTS based approaches such as UPFC or HVDC light involve massive modifications to the existing infrastructure. Recent instances of physical attacks on LPTs and the slow recovery process associated with fixing or replacing these damaged units has created an increased awareness for this issue. LPT designs are highly customized owing to specific field requirements. Long lead times coupled with transportation delays make procuring and installing LPTs a lengthy process. In an attempt to minimize the recovery time following an LPT failure, transformer manufacturers have developed mobile transformer units. These units typically consist of either a single-phase or three-phase transformer mounted on a truck and characterized by compact form factors. By reducing the assembly time and transport time these units offer a fast and temporary solution to LPT failures. Several efforts led by DOE, Edison Electric Company (EEI) and North American Electric Reliability Corporation (NERC) have led to the RecX program. RecX transformers consist of single phase transformers transported on specialized assemblies to enable fast restoration and minimal installation time. NERC and EEI have instituted transformer sharing programs like Spare Equipment Database (SED) as well as Spare Transformer Equipment Database (STEP) which encourage utilities to maintain and share a fleet of LPTs. Mobile transformers are typically limited to less than 100 MVA to enable transportation on a trailer. Also, the efficiency of mobile transformer units and RecX transformers is lower since they are temporary solutions. The ideal replacement solution would be one that could utilize a set of standardized transformers while incorporating flexibility to influence metrics like power flow, apparent impedance and voltage profiles. In view of these issues the team at Georgia Tech proposed a concept called Modular Controllable Transformers (MCTs). This project was commissioned as a part of DOE’s Transformer Resiliency and Advance Component (TRAC) program. The approach consists of splitting one large unit rated upwards of 100 MVA with smaller modular, standardized units. Each of these smaller units are augmented with a converter to add power flow, voltage and impedance control. Splitting the power level into multiple units has two important implications. By making smaller units, the transportation times can be reduced drastically. Moreover, even though these units have very specific impedance and other parameters, the augmented converters can make it emulate the unit being replaced. Thus, faster replacement times and added controls are achieved making this an extremely resilient approach. In the event of one modular units failure, the other two controllable units can operate in the most resilient fashion keeping the net system overloads to a minimum. Moreover, for the same probability of failure only a fraction of the system capacity is lost. The converter is augmented on to the tertiary winding of the transformer making it retrofittable in the field without massive modifications to the system. The material proposed in this report highlights design considerations to minimize recovery time as well as results displaying overall improvement in resiliency. The cost analysis of this approach shows significant savings compared to UPFC and HVDC lite based approaches. Extensive analysis was conducted on numerous aspects of this project over the course of this project. Simulation studies were conducted to prove the 5MVA converter design. Challenging BIL constraints and transient issues were addressed over the course of this project. Simulations on EMTP platform presented in consequent sections were used to finely tune the control for the same. A final design for a back-to-back 5MVA neutral-point-clamped (NPC) converter was completed in this project. Furthermore, switching components like high power IGBTs from IXYS Westcode Press-Pack series and ABB Stackpack series were identified for this application. These devices exhibit a blocking voltage of 6.5 kV and a nominal collector current of 900 A. Potential housing options as well as existing commercial option were identified for this converter design. A 56 MVA modular transformer unit was designed with Delta Star Inc. with consultation and inputs from all project members. The transformer cooling systems were chosen to be Oil Natural Air Forced (ONAF) to optimize ease of transport. A full Product Requirement Document (PRD) for the converter and the transformer was generated. The team also investigated key aspects of resiliency and how to quantify the same. A metric was developed to analyze resiliency and numerous simulation cases were presented. Increased (N-1) resiliency and outage resistance was showcased through simulation on the Texas Large scale power system. Dynamic control capabilities were also showcased on numerous system level simulations. An IEEE 30 bus system was used to show the ability of the MCT approach to reduce line congestion. Power flow control; a part of the MCTs abilities, showcased a 29% reduction in transformer loading on the same system. Full control over tie line flows was demonstrated on a modified IEEE 13 bus system. Dynamic LTC capabilities were shown through simulation and were compared to traditional LTCs. Furthermore, the ability of the MCTs to alter apparent transformer impedance was simulated to promote standardization of modular transformer units while retaining the ability to emulate a varied range of impedance characteristics. Thus granular control over power flow, voltages, impedances and was presented. Regulatory analysis conducted by Oak Ridge National Laboratory (ORNL) further bolstered the efficacy of the MCT approach and verified the added resiliency achieved due to this approach. As a conclusion, it was proved that the resilience improvement obtained from using the MCT approach is justified by just one failure in a 30-year period. This means that the benefits obtained from using the MCT approach far outweigh the cost of setting this approach up. As a part of the future efforts, the team is proposing building a (12.47-24) kV/400 kVA scaled down unit to control 5MVA of power flows. Emphasis would be laid on using commercial components to speed up the build of these units. Further the actual transport of the modular transformer unit would be a part of this operation and would provide insight into transportation issues. The approach shows much promise and adds key dynamic control on passive sub transmission and transmission sectors.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Heterologous production of an energy-conserving carbon monoxide dehydrogenase complex in the hyperthermophile Pyrococcus furiosus

In this study, carbon monoxide (CO) is an important intermediate in anaerobic carbon fixation pathways in acetogenesis and methanogenesis. In addition, some anaerobes can utilize CO as an energy source. In the hyperthermophilic archaeon Thermococcus onnurineus , which grows optimally at 80°C, CO oxidation and energy conservation is accomplished by a respiratory complex encoded by a 16-gene cluster containing a CO dehydrogenase, a membrane-bound [NiFe]-hydrogenase and a Na + /H + antiporter module. This complex oxidizes CO, evolves CO 2 and H 2 , and generates a Na+ motive force that is used to conserve energy by a Na+-dependent ATP synthase. Herein we used a bacterial artificial chromosome to insert the 13.2 kb gene cluster encoding the CO-oxidizing respiratory complex of T. onnurineus into the genome of the heterotrophic archaeon, Pyrococcus furiosus , which grows optimally at 100° C. P. furiosus is normally unable to utilize CO, however, the recombinant strain readily oxidized CO and generated H 2 at 80° C. Moreover, CO also served as an energy source and allowed the P. furiosus strain to grow with a limiting concentration of sugar or with peptides as the carbon source. Moreover, CO oxidation by P. furiosus was also coupled to the re-utilization, presumably for biosynthesis, of acetate generated by fermentation. The functional transfer of CO utilization between Thermococcus and Pyrococcus species demonstrated herein is representative of the horizontal gene transfer of an environmentally relevant metabolic capability. The transfer of CO utilizing, hydrogen-producing genetic modules also has applications for biohydrogen production and a CO-based industrial platform for various thermophilic organisms.

59 BASIC BIOLOGICAL SCIENCES↗