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DOE OSTI2024

Development of Human and Technology Integration Guidance for Work Optimization and Effective Use of Information

Existing nuclear power contributes to roughly 20% of the total electricity generation, and consistently provides the highest capacity factor of any other electricity generating resource in the United States. Despite these advantages, the existing nuclear power plant fleet in the United States has been enduring significant challenges in providing electricity in a cost-competitive manner, which has ultimately threatened the long-term economic viability of these plants. A major contributor to these increased operating and maintenance costs has been the continued reliance of a large workforce who perform their work under an operating model that has largely remained unchanged since the commissioning of these nuclear power plants. Unfortunately, while this operating model has provided safe and reliable electricity, other industries have already began transforming their workforce through the use of advanced digital technology and automation that has reduced their operating and maintenance cost significantly. In order for the United States nuclear industry to remain economically viable, a similar transformation must be considered in which digital technologies and automation capabilities are brought in to support key plant functions across all work functions across plant operation, maintenance, and support. To effectively integrate digital technology and automation in the existing nuclear power plant operating model, a multidisciplinary approach is needed that addresses technological and sociotechnical (i.e., human and technology integration) considerations. This report describes an extension to the human and technology integration methodology, herein referred to as Human Integration and Technology Task Force for Work Management Optimization (HITT), to support the safe, reliable, and efficient use of proposed innovations with the intended users in their intended environment to perform their intended tasks. By effectively incorporating human and technology integration into a plant modernization effort by using HITT, we believe that a utility can significantly improve work performance and overall workforce quality of life. For instance, if HITT is performed to optimize work management, scheduling performance and scope stability can be improved by up to 10%. HITT enables these benefits through developing a rich understanding of the work being performed, the utility’s vision, and the opportunities that provide greatest value to optimize performance through a 10-step process illustrated below as a quick guide. The quick guide contains links in the righthand side of each step that allows for navigation to the detailed section (Section 4) of HITT in this technical report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Enhancing Data Quality Monitoring at CMS with Interactive Visualization Tools and Automated Reference Run Selection

Current data quality monitoring (DQM) tools at CMS offer granularity limited to per-run analysis. Consequently, issues manifesting at the per-lumisection level can go unnoticed or, even if detectable, often lead to the classification of the whole run as bad, resulting in unnecessary data loss. Additionally, shifters have to evaluate a large set of monitoring elements during their long shifts, increasing the probability of human errors or overlooked problems. In this contribution, we present ongoing work on the development of tools that will provide shifters with an accessible, granularity-enhanced view of DQM data through interactive and dynamic visualizations. Furthermore, we introduce a reference run selection tool currently under development, which will automate the selection based on data-taking conditions and will offer a curated set of training data for machine learning models that will be used for the partial automation of the offline data certification process. These endeavors will be integrated into the DIALS website, enabling enhancements in data certification accuracy and improving the accessibility of DQM at CMS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Virtual Refrigerant Charge Sensing Method for Next-Generation Refrigerant in Residential Heat Pumps

The charge level of refrigerant in heat pump systems significantly affects their operational performance. Virtual refrigerant charge (VRC) sensing technology has been well-established for traditional refrigerants (HFCs and HCFCs) for its low cost compared to physical sensors. However, other than traditional refrigerants, HFOs are increasingly used in next-generation heat pumps; whether these conventional VRC sensing methods remain applicable for heat pump systems utilizing next-generation refrigerants requires further investigation. To address these issues, this study develops a low-cost VRC sensing method for next-generation refrigerant heat pumps used in residential buildings. The developed algorithm is evaluated by using simulation models to evaluate the accuracy, considering an R454B heat pump with a nominal heating capacity of 51K Btu/hr (14.95 kW) as an example, and compared with those of the two reference VRC sensing algorithms. Though the developed VRC sensing algorithm and the two reference methods can accurately predict the charge level for the R454B heat pump system (with mean absolute percentage error for various cooling and heating conditions less than 7%), the developed VRC sensing algorithm uses fewer sensors and improves the overall accuracy for heating conditions by 7.1%, and the accuracy for undercharge cooling conditions 14.2%, compared with a mainstream algorithm. This technology will complement physical leakage detectors, and promote the adoption of next-generation heat pump systems, along with reducing wasted energy and maintenance costs.

Liang, Chenjiyu↗

DOE OSTI2025

ComStock Measure Scenario Documentation: Chiller Replacement

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at a subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations. This report describes the modeling methodology for a single end-use savings shape measure - chiller replacement - and briefly introduces key results. The full public dataset can be accessed on the ComStock (TM) data lake or via the Data Viewer at comstock.nrel.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

metagRoot: a comprehensive database of protein families associated with plant root microbiomes

The plant root microbiome is vital in plant health, nutrient uptake, and environmental resilience. To explore and harness this diversity, we present metagRoot, a specialized and enriched database focused on the protein families of the plant root microbiome. MetagRoot integrates metagenomic, metatranscriptomic, and reference genome-derived protein data to characterize 71 091 enriched protein families, each containing at least 100 sequences. These families are annotated with multiple sequence alignments, CRISPR elements, hidden Markov models, taxonomic and functional classifications, ecosystem and geolocation metadata, and predicted 3D structures using AlphaFold2. MetagRoot is a powerful tool for decoding the molecular landscape of root-associated microbial communities and advancing microbiome-informed agricultural practices by enriching protein family information with ecological and structural context. The database is available at https://pavlopoulos-lab.org/metagroot/ or https://www.metagroot.org.

Chasapi, Maria N↗

DOE OSTI2024

Defining Safe and Efficient Interconnection Policies for Energy Storage and Solar + Storage to Improve Integration and Reduce Costs

Energy storage has a unique and pivotal role to play in the transition to a low-carbon economy because it can help the electric grid accommodate more renewable energy. However, a number of barriers currently impede the process of connecting energy storage systems to the distribution grid. The Toolkit and Guidance for the Interconnection of Energy Storage and Solar-Plus-Storage (the “BATRIES Toolkit”), which is the publication subject to this Final Technical / Scientific Report, provides vetted solutions to eight regulatory and technical barriers to the interconnection of standalone storage and solar-plus-storage systems (referred to as “energy storage”) to the distribution grid. These recommendations are based on over a year of research and analysis by utility and industry experts. The BATRIES Toolkit also includes model language that utilities and utility regulators can use to update state interconnection rules to reduce the costs and time to safely interconnect energy storage and solar-plus-storage systems. The solutions are nationally applicable and can be applied in diverse states and markets across the U.S.

14 SOLAR ENERGY↗

DOE OSTI2026

Endogenous Interface Pricing for Consistent Transmission–Distribution Co-Optimization With Discrete Distribution Controls

This paper proposes an endogenous interface pricing model for day-ahead transmission–distribution co-optimization that co-determines the interface locational marginal price (LMP) and the transmission–distribution exchange, ensuring price–dispatch consistency while optimally scheduling discrete distribution controls. The formulation couples a DC optimal power flow (OPF) with a branch-flow AC OPF that schedules distributed energy resources (DERs), tap-changer settings, capacitor banks (CBs), and multi-period energy storage systems (ESSs) under feeder voltage and current limits, and is solved as a mixed-integer second-order cone program (MISOCP). In a T14–D33 system, coordinated device scheduling recovers about 90% of the distribution-to-transmission export achievable in a reference case that ignores distribution network (DN) limits, while satisfying a 1.05 p.u. voltage upper bound. In a T39–D34/D37/D123 system, a sequential decoupled benchmark produces interface LMP distortions up to 12.5% and a 7.28% mismatch in net export energy, whereas the proposed model removes these distortions and the associated settlement mismatches. Second-order cone (SOC) relaxation gaps remain below $10^{-3}$ in all cases.

Noh, Seung-Gil↗

DOE OSTI2024

Impact of Varying Dark Energy on Future Large-Scale Structure Studies

The cosmos withholds multiple mysteries such as dark forms of matter and energy that are yet beyond human comprehension. In the standard cosmological model, $\Lambda$CDM, the cosmological constant $\Lambda$ is thought to be responsible for the late accelerated expansion of the Universe. However, recent results from the Dark Energy Spectroscopic Instrument (DESI) suggest the possibility of evolving dark energy, which warrants further exploration. Future surveys such as the Rubin Observatory Legacy Survey of Space and Time (LSST) will map the large-scale structure (LSS) with unprecedented precision, giving us valuable statistical information about the cosmos. The goal of this research is to investigate the potential impact of an evolving dark energy scenario on cosmological parameters constrained by LSS probes, as will be mapped by the LSST. For this, we examine the power spectra of lens galaxies, source galaxies, and cross-power spectra between lens galaxies and source galaxies. The combination of these statistics is commonly referred to as '3 $\times$ 2 points'. For this investigation, we created a set of simulations resembling LSST data and used them to perform cosmological parameter inference in two scenarios: one in which the simulated data is based on the fiducial model and another on evolving dark energy. We then examined the degeneracy between the cosmological parameters and checked for potential shifts in the parametric space when the data contains dynamical dark energy but the modeling assumes $\Lambda$CDM. Our findings indicate that mismodeling the dark energy equation of state can significantly impact parameter inference, particularly affecting the total matter density, $\Omega_m$, and the growth of structures, as represented by the $S_8$ parameter. These results highlight the importance of further exploring extensions of the $\Lambda$CDM model in future LSS studies.

Yaman Acharya, A.↗

DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

MOOSE–Workbench Integration and MOOSE Meshing Capability Enhancements to Facilitate Inputs and Outputs for Multiphysics Modeling

The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source framework that supports many of the US Department of Energy’s (DOE’s) Nuclear Energy Advanced Modeling and Simulation (NEAMS) technical areas (TA). These TAs develop and use NEAMS physics and coupling modules in multiple ways to enable the research and development of complex physics models. In addition to the MOOSE framework, the NEAMS Workbench user interface provides a common analysis environment with user-interaction accelerators that streamline the tasks of model creation, review, execution, and output inspection. In FY 2024, objectives were realized in the MOOSE framework application development support and user-oriented improvements. Application development improvements support both developers and users with an expanded Reactor Module and Mesh System, stateful material property support for mortar contact, and customizable convergence criteria. Additionally, new user-oriented features were implemented in the MOOSE framework language server, including autocompletion snippets, definition from source and find reference navigations, and syntax overrides. Lastly, improvements were made to the input interpreter necessary to support the MOOSE language server and the NEAMS Workbench so that they can interact with syntactically incomplete user inputs. These improvements and more were intended to address stakeholder feedback and improve developer and user ability to conduct advanced nuclear energy modeling and simulation in support of DOE and industry needs.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Non-linear MHD modelling of transients in tokamaks: a review of recent advances with the JOREK code

Transient magneto-hydrodynamic (MHD) events like edge localized modes (ELMs) or disruptions are a concern for magnetic confinement fusion power plants. Research with the MHD code JOREK towards understanding control of such instabilities is reviewed here in a concise way to provide a complete overview, while we refer to the original publications for details. Experimental validation for unmitigated vertical displacement events progressed. The mechanism of vertical force mitigation by impurity injection was identified. Two-way eddy current coupling to CARIDDI was completed. Shattered pellet injection was simulated in JET, KSTAR, ASDEX Upgrade (AUG) and ITER. Benign runaway electron beam termination in JET and ITER was studied. Coupling of kinetic REs to the MHD is ongoing and a virtual RE synchrotron radiation diagnostic was developed. Regarding pedestal physics, regimes devoid of large ELMs in AUG were simulated and predictive JT60-SA simulations are ongoing. For ELM suppression by resonant magnetic perturbations (RMPs), AUG, ITER and EAST simulations were performed. A free boundary RMP model was validated against experiments. Evidence for penetrated magnetic islands at the pedestal top based on AUG experiments and simulations was found. Simulations of the naturally ELM-free quiescent H-mode in AUG and HL-3 show external kink mode formation prevents pedestal build-up towards an ELM within windows of the edge safety factor. With kinetic neutral particles, high field side high density formation in ITER was simulated and with kinetic impurities, tungsten transport in AUG RMP plasmas was studied. To capture turbulent transport, electro-static full-f particle in cell models for ion temperature gradient and trapped electron modes were established and benchmarked. Application to RMP plasmas shows enhanced turbulence in comparison to unperturbed states. Energetic particle interactions with MHD were studied. Flux pumping that prevents the safety factor on axis from dropping below unity was simulated. First non-linear stellarator applications include current relaxation in $l$ = 2 stellarators, while verification for advanced stellarators progresses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

MOOSE-Workbench integration and MOOSE meshing capability enhancements to facilitate inputs and outputs for multiphysics modeling

The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source framework that supports many of the US Department of Energy’s (DOE’s) Nuclear Energy Advanced Modeling and Simulation (NEAMS) technical areas (TA). These TAs develop and use NEAMS physics and coupling modules in multiple ways to enable the research and development of complex physics models. In addition to the MOOSE framework, the NEAMS Workbench user interface provides a common analysis environment with user-interaction accelerators that streamline the tasks of model creation, review, execution, and out put inspection. In FY 2024, objectives were realized in the MOOSE framework application development support and user-oriented improvements. Application development improvements support both developers and users with an expanded Reactor Module and Mesh System, stateful material property support for mortar contact, and customizable convergence criteria. Additionally, new user-oriented features were implemented in the MOOSE framework language server, including autocompletion snippets, definition from source and find reference navigations, and syntax overrides. Lastly, improvements were made to the input interpreter necessary to support the MOOSE language server and the NEAMS Workbench so that they can interact with syntactically incomplete user inputs. These improvements and more were intended to address stakeholder feedback and improve developer and user ability to conduct advanced nuclear energy modeling and simulation in support of DOE and industry needs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Jet quenching in the glasma phase: Medium-induced radiation

Inspired by the recent considerations of parton momentum broadening in the glasma phase, we study the medium-induced soft gluon radiation of jet partons at early times in heavy-ion collisions. The glasma state is assumed to be comprised of independent color domains with homogenous longitudinal fields that vary event by event, and we further complete this model with an event-averaging procedure accounting for the finite correlation length. Using this description, we evaluate the rate of medium-induced radiation from an energetic parton at midrapidity in the glasma phase. We mainly focus on SU(2) color fields for simplicity, also referring to the U(1) case and comparing with the Baier, Dokshitzer, Mueller, Peigné, Schiff, and Zakharov rate to gain further insight. Our results show that there is an intricate interplay of the synchrotron-like radiation in a single color domain with the destructive interference between different color domains, after the medium averaging is performed. Thus, we find that the emission rate is sensitive to the matter structure, decreasing for a glasma state populated by smaller color domains—i.e., for a glasma with a larger characteristic saturation scale. Our approach can be applied to more realistic backgrounds, and it sets the stage for the modeling of jet evolution in the early stages of heavy-ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Light-induced electron spin qubit coherences in the purple bacteria reaction center protein

Photosynthetic reaction center proteins (RCs) provide ideal model systems for studying quantum entanglement between multiple spins, a quantum mechanical phenomenon wherein the properties of the entangled particles become inherently correlated. Following light-generated sequential electron transfer, RCs generate spin-correlated radical pairs (SCRPs), also referred to as entangled spin qubit (radical) pairs (SQPs). Understanding and controlling coherence mechanisms in SCRP/SQPs is important for realizing practical uses of electron spin qubits in quantum sensing applications. The bacterial RC (bRC) provides an experimental system for exploring quantum effects in the SCRP P 865 + Q A − , where P 865 , a special pair of bacteriochlorophylls, is the primary donor, and Q A is the primary quinone acceptor. In this study, we focus on understanding how local molecular environments and isotopic substitution, particularly deuteration, influence spin coherence times (T M ). Using high-frequency electron paramagnetic resonance (EPR) spectroscopy, we observed that the local environment surrounding P 865 and Q A plays a significant role in determining T M . Our findings show that while deuteration led to a modest increase in T M , particularly at low temperatures, but the effect was substantially smaller than predicted by classical nuclear spin diffusion alone. This result is in contrast to our previous study of the photosystem I (PSI) RC, where no increase in T M was observed upon deuteration. Theoretical modeling identified several methyl groups at key distances from the spin centers of both bRC and PSI, and methyl group tunneling at low temperatures has been previously suggested as a mechanism for enhanced spin decoherence. Additionally, our study revealed a strong dependence of spin coherence on the orientation of the external magnetic field, highlighting the influence of the protein microenvironment on spin dynamics. In conclusion, these results offer new insights for optimizing coherence times in quantum system design for quantum information science and sensing applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Observation of nuclear modification of energy-energy correlators inside jets in heavy ion collisions

Energy-energy correlators are constructed by averaging the number of charged particle pairs within jets, weighted by the product of their transverse momenta, as a function of the angular separation of the particles within a pair. They are sensitive to a multitude of perturbative and nonperturbative quantum chromodynamics phenomena in high-energy particle collisions. Using lead-lead data recorded with the CMS detector, energy-energy correlators inside high transverse momentum jets are measured in heavy ion collisions for the first time. The data are obtained at a nucleon-nucleon center-of-mass energy of 5.02 TeV and correspond to an integrated luminosity of 1.70 nb −1 . A similar analysis is done for proton-proton collisions at the same center-of-mass energy to establish a reference. The ratio of lead-lead to proton-proton energy-energy correlators reveals significant jet substructure modifications in the quark-gluon plasma. The results are compared to different models that incorporate either color coherence or medium response effects, where the two effects predict similar substructure modifications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Towards a RAG-based summarization for the Electron Ion Collider

Abstract The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed athttps://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found inhttps://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]

Instruments & Instrumentation↗

DOE OSTI2025

Engineering the green algae Chlamydomonas incerta for recombinant protein production

Chlamydomonas incerta , a genetically close relative of the model green alga Chlamydomonas reinhardtii , shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii , this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta ’s capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii , C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii . For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii , as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta ’s potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

cell membranes↗