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At least 1,099 records · Page 61

Fluorogenic Biosensing with Tunable Polydiacetylene Vesicles

Polydiacetylenes (PDAs) are conjugated polymers that are well known for their colorimetric transition from blue to red with the application of energetic stimulus. Sensing platforms based on polymerized diacetylene surfactant vesicles and other structures have been widely demonstrated for various colorimetric biosensing applications. Although less studied and utilized, the transition also results in a change from a non-fluorescent to a highly fluorescent state, making polydiacetylenes useful for both colorimetric and fluorogenic sensing applications. Here, we focus on the characterization and optimization of polydiacetylene vesicles to tune their sensitivity for fluorogenic sensing applications. Particularly, we look at how the structure of the diacetylene (DA) hydrocarbon tail and headgroup affect the self-assembled vesicle size and stability, polymerization kinetics, and the fluorogenic, blue to red phase transition. Longer DA acyl tails generally resulted in smaller and more stable vesicles. The polymerization kinetics and the blue to red transition were a function of both the DA acyl tail length and structure of the headgroup. Decreasing the acyl tail length generally led to vesicles that were more sensitive to energetic stimuli. Headgroup modifications had different effects depending on the structure of the headgroup. Ethanolamine headgroups resulted in vesicles with potentially increased stimuli responsivity. The lower energy stimulus to induce the chromatic transition was attributed to an increase in headgroup hydrogen bonding and polymer backbone strain. Boronic-acid headgroup functionalization led to vesicles that were generally unstable, only weakly polymerized, and unable to fully transform to the red phase due to strong polar, aromatic headgroup interactions. This work presents the design of PDA vesicles in the context of biosensing platforms and includes a discussion of the past, present, and future of PDA biosensing.

Chemistry↗

ASC Trilab L2 Codesign Milestone 2015

For the FY15 ASC L2 Trilab Codesign milestone Sandia National Laboratories performed two main studies. The first study investigated three topics (performance, cross-platform portability and programmer productivity) when using OpenMP directives and the RAJA and Kokkos programming models available from LLNL and SNL respectively. The focus of this first study was the LULESH mini-application developed and maintained by LLNL. In the coming sections of the report the reader will find performance comparisons (and a demonstration of portability) for a variety of mini-application implementations produced during this study with varying levels of optimization. Of note is that the implementations utilized including optimizations across a number of programming models to help ensure claims that Kokkos can provide native-class application performance are valid. The second study performed during FY15 is a performance assessment of the MiniAero mini-application developed by Sandia. This mini-application was developed by the SIERRA Thermal-Fluid team at Sandia for the purposes of learning the Kokkos programming model and so is available in only a single implementation. For this report we studied its performance and scaling on a number of machines with the intent of providing insight into potential performance issues that may be experienced when similar algorithms are deployed on the forthcoming Trinity ASC ATS platform.

97 MATHEMATICS AND COMPUTING↗

A consolidated bioprocess design to produce multiple high-value platform chemicals from lignocellulosic biomass and its technoeconomic feasibility

5-Hydroxymethyl furfural (HMF) and furfurals are DOE-listed platform chemicals that can be derived from the renewable carbon in the lignocellulosic biomasses and have the potential to replace petroleum-derived alter- natives. High substrate cost and use of expensive solvents limit the economic feasibility of bio-based HMF production on an industrially relevant scale. The study presents an experimental optimized condition that maximizes the chemical-free production of HMF and furfurals without lowering the yield of total fermentable sugars from Saccharum bagasse. Hydrothermal pretreatment at 210 °C for 15 min yielded approximately 10%, 12%, and 46% of HMF, furfurals, and fermentable sugars per gram of dry biomass, respectively. Additionally, the study proposes a consolidated bioprocess model to produce and recover four high-value bioproducts i.e., HMF, furfurals, ethanol, and acetic acid based on the experimental results and evaluates its technoeconomic feasibility considering HMF as the main product. The minimum selling price (MSP) of HMF was estimated to be 930.6 USD/ t which is competitive with its petroleum-derived precursor alternative p-xylene (1,113 USD/t). The sensitivity analysis performed for the process parameters suggests that pretreatment cost and revenues from coproducts immensely influence the MSP of HMF. The preliminary technoeconomic analysis performed on the consolidated bioprocess design indicates that additional revenue streams from diversified coproducts in biorefineries aid in lowering the MSP of high-value bioproducts.

09 BIOMASS FUELS↗

Building a quantum computing architecture using 3D superconducting cavities

Quantum computers promise advantages over classical machines for solving certain complex problems, but building processors that truly deliver this advantage remains a central challenge, particularly due to limited coherence times. Three-dimensional superconducting radio-frequency (SRF) cavities offer an attractive platform due to their exceptionally long lifetimes. However, since these harmonic systems require nonlinear elements, such as transmons, for control, additional losses are often introduced. In this talk, I will present a multimode quantum system based on an elliptical SRF cavity hosting two cavity modes weakly coupled to an ancillary transmon circuit. This architecture is carefully engineered to preserve coherence while enabling efficient control. By optimizing the design to mitigate transmon-induced decoherence, we realize single-photon lifetimes of 20.6 ms and 15.6 ms in the two modes, with pure dephasing times exceeding 40 ms. Using sideband interactions and error-resilient protocols, such as measurement-based correction and post-selection, we demonstrate high-fidelity state control, including preparation of Fock states up to N=20 with fidelities above 95% (to our knowledge, the highest reported to date), as well as high-fidelity two-mode entanglement. These results highlight 3D SRF cavities as a robust foundation for qudit-based quantum information processing, harnessing the large Hilbert space of cavity modes. I will conclude by outlining strategies to further enhance coherence in both cavities and ancilla qubits, and discuss pathways toward scaling this architecture into a larger quantum computing platform.

Roy, Tanay [Fermilab]↗

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The LSBmax algorithm for boosting resilience of electric grids post (N‐2) contingencies

Abstract A computationally improved algorithm is presented to find the best transmission switching (TS) candidate for boosting resilience of electricity grids subject to ( N ‐2) contingencies. Here, resilience is computed as the reduction in load shed after the above‐mentioned ( N‐ ) contingencies. TS is a planned line outage, and past research shows that changing the transmission system's topology changes the power flow and removes post contingency violations. Finding the best TS candidate in a computationally suitable time for effectively boosting resilience is a challenge. The best TS candidate is found using a novel heuristic method by decreasing the search space based on proximity to the bus with the maximum load shedding (LSB). The LSB algorithm is faster than existing algorithms in the literature; and, it is compatible with both the AC and DC optimal power flow formulations. To validate the authors' claims of speedup and accuracy, two metrics are used to analyze the results from the IEEE 39‐bus and 118‐bus systems. Finally, the inherent parallelism of the LSB algorithm is leveraged on a high‐performance computing platform and applied to the large‐scale Polish 2383‐bus test system to validate scalability in both size and speedup in computation time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Kinetic approach to defect reduction in directed self-assembly

As a potential solution to next-generation nanolithography, directed self-assembly (DSA) of block copolymers (BCPs) is still restrained in high-volume manufacturing primarily due to its defectivity issue. Though defects possess greater free energies than aligned morphologies and are highly energetically unfavorable, they can be kinetically trapped by the energy barriers and persist for a long time during annealing. Therefore, understanding the kinetics of defect annihilation is crucial in revealing the mechanism of defect formation and in further reducing defectivity in DSA. We focus on two types of predominant defects in DSA- dislocation and bridge. Furthermore, a kinetic model of each defect type is developed through statistical analysis of experimental data, providing insight into possible approaches of further defect reduction. We also investigate the impact of annealing temperature and film thickness on annihilation kinetics and discuss the reasons behind the observed results. By simply optimizing annealing conditions and film thickness, we have successfully reduced the total defect density by 1 order of magnitude. Though these findings are based on polystyrene-b-poly(methyl methacrylate) (PS-b-PMMA), we anticipate they could be readily applied to other BCP platforms as well.

42 ENGINEERING↗

SU-F-T-346: Dose Mimicking Inverse Planning Based On Helical Delivery Treatment Plans for Head and Neck Patients

Purpose: We aim to evaluate a new commercial dose mimicking inverse-planning application that was designed to provide cross-platform treatment planning, for its dosimetric quality and efficiency. The clinical benefit of this application allows patients treated on O-shaped linac to receive an equivalent plan on conventional L-shaped linac as needed for workflow or machine downtime. Methods: The dose mimicking optimization process seeks to create a similar DVH of an O-shaped linac-based plans with an alternative treatment technique (IMRT or VMAT), by maintaining target conformity, and penalizing dose falloff outside the target. Ten head and neck (HN) helical delivery plans, including simple and complex cases were selected for re-planning with the dose mimicking application. All plans were generated for a 6 MV beam model, using 7-field/ 9-field IMRT and VMAT techniques. PTV coverage (D1, D99 and homogeneity index [HI]), and OARs avoidance (Dmean / Dmax) were compared. Results: The resulting dose mimicked HN plans achieved acceptable PTV coverage for HI (VMAT 7.0±2.3, 7-fld 7.3±2.4, and 9-fld 7.0±2.4), D99 (98.0%±0.7%, 97.8%±0.7%, and 98.0%±0.7%), as well as D1 (106.4%±2.1%, 106.5%±2.2%, and 106.4%±2.1%), respectively. The OAR dose discrepancy varied: brainstem (2% to 4%), cord (3% to 6%), esophagus (−4% to −8%), larynx (−4% to 2%), and parotid (4% to 14%). Mimicked plans would typically be needed for 1–5 fractions of a treatment course, and we estimate <1% variance would be introduced in target coverage while maintaining comparable low dose to OARs. All mimicked plans were approved by independent physician and passed patient specific QA within our established tolerance. Conclusion: Dose mimicked plans provide a practical alternative for responding to clinical workflow issues, and provide reliability for patient treatment. The quality of dose mimicking for HN patients highly depends on the delivery technique, field numbers and angles, as well as user selection of structures.

60 APPLIED LIFE SCIENCES↗

Roadrunner

SAND2026-17073O Roadrunner software provides a comprehensive platform for simulating the mechanical behavior of crystalline materials under various loading conditions, allowing users to investigate the effects of dislocation slip hardening and damage evolution. Developed as a fork of the Multiphysics Object Oriented Simulation Environment (MOOSE) software from Idaho National Laboratory, Roadrunner is optimized for high-performance computing and can simulate large-scale problems, enabling researchers to explore complex scenarios. Its applications include material design and optimization in aerospace and automotive industries, investigation of failure mechanisms in structural materials, and development of predictive models for crystalline materials under various loading conditions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Lim, Hojun [Sandia National Lab. (SNL-CA), Livermo↗

Accelerating Science Driven System Design With RAMP

Researchers from UC Berkeley, in collaboration with the Lawrence Berkeley National Lab, are engaged in developing an Infrastructure for Synthesis with Integrated Simulation (ISIS). The ISIS Project was a cooperative effort for “application-driven hardware design” that engages application scientists in the early parts of the hardware design process for future generation supercomputing systems. This project served to foster development of computing systems that are better tuned to the application requirements of demanding scientific applications and result in more cost-effective and efficient HPC system designs. In order to overcome long conventional design-cycle times, we leveraged reconfigurable devices to aid in the design of high-efficiency systems, including conventional multi- and many-core systems. The resulting system emulation/prototyping environment, in conjunction with the appropriate intermediate abstractions, provided both a convenient user programming experience and retained flexibility, and thus efficiency, of a reconfigurable platform. We initially targeted the Berkeley RAMP system (Research Accelerator for Multiple Processors) as that hardware emulation environment to facilitate and ultimately accelerate the iterative process of science-driven system design. Our goal was to develop and demonstrate a design methodology for domain-optimized computer system architectures. The tangible outcome is a methodology and tools for rapid prototyping and design-space exploration, leading to highly optimized and efficient HPC systems.

97 MATHEMATICS AND COMPUTING↗

BOPTEST: Initial FRP Test Case Development

To support an emulator model development of Building Optimization Testing (BOPTEST) framework, a team at the US Department of Energy’s Oak Ridge National Laboratory developed a medium-sized office building written in Modelica to serve as a reference test case. The reference test case was modeled based on an actual research platform that represents a typical medium-sized office building. This test case would encourage model-based design and testing of new controllers in which a developer first prototypes and debugs on a model before implementing in the real world. The developed preliminary model and control parameters will be further calibrated and tuned with actual measured data from experiments

42 ENGINEERING↗

Non-Destructive, Three-Dimensional Imaging of Processes in the Rhizosphere Utilizing High Energy Photons

Soil structure, which can be described as the aggregation and distribution of pore spaces, regulates carbon, nutrient, and water cycling across the Earth system. Yet the inability to make quantitative, dynamic, in situ measurements of soil structure and rhizosphere carbon flow has prevented meaningful incorporation of soil structural processes into Earth System Models (ESMs). The key limitations are: (i) Lack of scale integration between micron-scale soil structure and ecosystem-scale models, (ii) Poor functional linkage between soil structural properties and biogeochemical processes, and (iii) Absence of dynamic 3D measurements of rhizosphere structural changes and carbon transformations. To address these challenges, we developed a new integrated positron emission tomography (PET) - microcomputed tomography (CT) imaging platform that enables the first 4D (spatial 3D and time), non-invasive, quantitative imaging of carbon allocation and rhizosphere structural dynamics in living plants and intact soils. The system combines: • Rhizo-PET (R-PET): a high-resolution positron emission tomography scanner optimized for 11 CO 2 tracing in plant roots. • a-Se micro-CT: a high-contrast, high-spatial resolution CT system based on amorphous selenium (a-Se) direct conversion technology, enabling micron-scale visualization of soil structure and root–soil interfaces. Together, these advances allow us to quantify how carbon exudates move, transform, and stabilize within the rhizosphere, directly informing missing processes in BER-relevant carbon cycle models.

42 ENGINEERING↗

OpenACC unified programming environment for GPU and FPGA multi-hybrid acceleration

Attached accelerators have been frequently used in recent High Per- formance Computing (HPC) systems because of their high performance/power ratio. In particular, the Graphics Processing Unit (GPU) is the most popu- lar accelerator owing to its high peak FLOPS performance and high memory bandwidth supported by HBM2, etc. However, the performance of GPU depends highly on a large degree of SIMD parallelism and has difficulty sustaining a high performance on programs with frequent branch operations or a partially low degree of parallelism.By contrast, a Field Programmable Gate Array (FPGA) has received attention as a different type of accelerator than GPU as a fully reconfigurable processor fitting the target applications. The high performance of FPGA is mainly provided by a pipelined operation and optimized circuit suitable for any operation even with frequent conditional branches. We have been focusing on the flexibility of FPGA to compensate for the weakness of GPU. We believe that the coupling of GPU with FPGA can result in one of the most powerful accelerating platforms available.However, the program coding of GPU and FPGA coupling can be quite difficult for application users. Traditionally, CUDA by NVIDIA has been the most popular programming language with the largest share of GPUs used in HPC, whereas a hardware description language such as Verilog HDL has been used in FPGA programming. OpenCL coding has recently become available even on high-end FPGAs. Moreover, several recent studies have also enabled the OpenACC coding for use in FPGA. In this study, we provide a unified programming system based on OpenACC for a platform equipped with both GPU and FPGA aiming at the next-generation accelerated supercomputer framework. Our programming environment is called Multi-Hybrid OpenACC Translator (MHOAT), and in this paper, we describe the basic concept and prototype system of MHOAT based on an evaluation on the amount of coding required and the performance of a hybrid multi-device accelerated system.

Tsunashima, Ryuta↗

Towards Digital and Performance-Based Supervisory HVAC Control Delivery

Upgrading supervisory HVAC control in commercial buildings is one of the most attractive decarbonization tools at our disposal. Modern controls are software programs and can in theory be deployed at scale and with a low up-front carbon "pulse". In practice, however, control delivery is a disjointed and inefficient process, dominated by manual handoffs of imprecise English language documents. A particularly high barrier exists between control implementation and building energy modeling (BEM) which results in control sequences typically not being tested for correctness or performance before implementation. Together with industry partners, DOE and the national labs are developing an ecosystem of tools and standards that can support fully digital performance-based control delivery workflows. This paper describes this ecosystem, which consists of three mutually supportive efforts. Semantic models of buildings and their systems enable automatic configuration and installation of control software. Platform-neutral control descriptions separate control algorithms from control platforms and enable the creation of libraries of reference control implementations. Dynamic whole-building energy-control simulation that can execute physically realistic control sequences makes it possible to test and evaluate the performance of control sequences and then directly compile them for installation and execution in control systems. In addition to digitizing and streamlining project-level control delivery, these standards and related software support benchmarking of control algorithms, both rule-based and optimization-based, and help to both advance the state of the art and to implement ratings and programs that encourage the adoption of high-performance control.

building controls↗

Robust Ceramic Coaxial Cable Down-Hole Sensors for Long-Term In Situ Monitoring of Geologic CO 2 Injection and Storage

Energy generation by use of fossil fuels produces large volumes of CO 2 and other greenhouse gases, whose accumulation in the atmosphere is widely seen as undesirable. CO 2 Capture followed by sequestration has been identified as the solution. Subsurface geologic formations offer a potential location for long-term storage of CO 2 because of their requisite size. Unfortunately, the inaccessibility and complexity of the subsurface, the wide range of scales of variability, and the coupled nonlinear processes, impose tremendous challenges to determine the transport and predict the fate of the stored CO 2 . Among the various monitoring approaches, in situ down-hole monitoring of the various state parameters provides critical and direct data points that can be used to validate the models, optimize the injection, detect leakage and track the CO 2 plume. However, down-hole sensors that can withstand the harsh conditions and operate over decades of the project lifecycle remain unavailable. Given that the widespread of carbon capture and storage will be the necessity and reality in the future, fundamental and applied research is required to address the significant challenges and technological gaps in lack of long-term reliable down-hole sensors This project focused on the development and demonstration of a novel, low-cost, distributed, robust ceramic coaxial cable sensor platform for in situ down-hole monitoring of geologic CO 2 injection and storage with high spatial and temporal resolutions. The coaxial cable Fabry-Perot interferometer (CCFPI) has been studied as a general sensor platform for in situ, long-term, measurement of temperature, pressure and strain, which are critical to CO 2 injection and storage. A novel signal processing scheme has been developed and demonstrated for dense multiplexing of the sensors for low-cost distributed sensing with high spatial resolution. The developed temperature, pressure and strain sensors have been extensively tested under laboratory conditions that are similar to the downhole CO 2 storage environment, showing excellent capability for in situ monitoring the various parameters that are important to model, optimize the injection, detect leakage and track the CO 2 plume. In addition, the interactions between the sensor datum and the geological models have been investigated in details for the purposes of model validation, guiding sensor installation/placement, enhancement of model prediction capability and optimization of the injection processes. This project has resulted in the successful development of new ceramic coaxial cable based sensor systems that can monitor directly the changes in pressure, temperature, and strain caused by increased reservoir pressure and reduced reservoir temperature due to the supercritical CO 2 injection. Integrated with geological models, the sensors and measurement data can improve the possibility to identify plume movement and leakage in the cap rock and wells with higher precision and more accuracy. The low cost, ease of deployment, small size and dense multiplexing features of the new sensing technology will allow a large number of sensors to be deployed to address the objective to demonstrate that 99% of the CO 2 remains in the injection zone.

01 COAL, LIGNITE, AND PEAT↗

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

R&D Roadmap for Enhanced Resilient Plant Systems, Metrics, Scenarios, Risk Analysis, and Modeling and Simulation

The nuclear industry and the U.S. Department of Energy are jointly developing accident tolerant fuel (ATF) which has the potential to greatly improve the safety and economic performance of the current fleet of NPPs. The combination of ATF with the optimal use of FLEX equipment (deployed as an industry response to the Fukushima Daiichi Nuclear Power Plant (NPP) accident in 2012), enhancements on plant components and systems, incorporation of augmented cooling systems or new passive cooling systems, and improvements in fuel cycle efficiency, have the potential to render the NPPs more efficient and resilient to events (both internally and externally initiated) that have the potential to challenge the integrity of the fuel or plant containment. In this report, combinations of these plant improvements are called Enhanced Resilient Plant Systems. In order to accelerate the development and deployment of the components that comprise Enhanced Resilient Plant Systems, a well-integrated toolkit that is fuel/clad-, fuel cycle-, systems-, and scenario-centric is required to provide a ready platform for the analysis of the advanced ATF fuel and cladding materials that are being developed, as well as plant systems modifications to achieve enhanced resiliency. Since the deployment of these enhancements will comprise a risk/benefit tradeoff of their impacts on plant safety and economics, the envisioned toolkit will need to be capable to characterizing the multidimensional aspects associated with these evaluations and provide decision-makers with information that can optimize the technological, economic, and safety benefits that are to be obtained. The Risk-Informed Systems Analysis (RISA) Pathway of the DOE Light Water Reactor Sustainability (LWRS) Program initiated several research tasks to develop analytical capabilities to support the industry in the transition to the ATF and Enhanced Resilient Plant Systems. The general idea behind the initiative is the development of an integrated evaluation approach which combines the plant probabilistic risk assessment (PRA) models with Multi-Physics Best Estimate Plus Uncertainty (MP-BEPU) analyses in a seamless fashion. The integrated evaluation framework aims at enabling plant system configuration variations to be studied with speed and precision, including detailed risk and benefit assessments of introducing ATF and Enhanced Resilient Plant Systems into current LWR plants to achieve both safety and operational performance enhancements. The focus of this report is to present an integrated research and development (R&D) roadmap to identify and perform high value evaluations of proposed ATF and Enhanced Resilient Plant Systems concepts to identify both the technical (e.g. benefits to risk, safety, and operational margins) and the economic (i.e. business and cost) elements associated with industry adoption of the technology. The integrated evaluation approach will be developed to support the development, testing, qualification, licensing, and deployment of ATF and Enhanced Resilient Plant Systems technology that is capable of achieving substantial safety and economic improvements as well as timely widespread adoption by the US nuclear industry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗