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DOE OSTI2026

Overview of the KSTAR experiments and future plan

The Korea Superconducting Tokamak Advanced Research (KSTAR) device has recently entered a new phase of operation following the installation of a tungsten mono-block divertor, providing a unique platform to investigate high-performance, long-pulse plasma scenarios for future reactors. The 2023–2024 experimental campaigns demonstrated significant progress in commissioning the tungsten divertor, developing plasma operation scenarios, and achieving real-time control. Comparative studies revealed increased core radiation losses and associated performance degradation relative to the carbon divertor environment. Alleviation strategies, such as optimized neutral beam injection timing, boron powder seeding, and impurity control through gas puffing, were shown to reduce tungsten accumulation and extend high-performance phases, including record H-mode operation exceeding 100 s. International collaboration enabled the first demonstration of high poloidal beta scenarios with an internal transport barrier on KSTAR. Advances in control included upgraded real-time resonant magnetic perturbation schemes and disruption forecasting. Furthermore, divertor detachment control using a new surrogate-model-based approach with real-time radiation imaging demonstrated active impurity and heat flux management. Supported by enhanced heating, current drive, and diagnostic systems, these achievements offer valuable insights into tungsten impurity behavior, transport physics, and control strategies for future reactors including ITER. Planned upgrades toward a full-tungsten wall and ITER-aligned real-time plasma control will further establish KSTAR as a leading experimental platform for developing operational scenarios required to achieve steady-state, burning plasma conditions.

KSTAR↗

DOE OSTI2020

PLATFORM for Product Launch

Pecan Street Inc. (PSI) and its partners set out in 2017 to validate the PLATFORM for Product Launch, a new model for improving the effectiveness of investments in clean energy technologies and accelerating market entry of disruptive technologies. In partnership with a newly formed Innovation Advisory Council, PSI recruited fourteen companies with new hardware-based technologies that enable or create clean energy opportunities. Pecan Street carried out product evaluations and conducted market research with stakeholders and target customers to develop recommendations on product optimization opportunities for all companies, and created third party performance evaluation reports that include environmental metrics, such as water and energy savings potential, to assist in creating investment confidence and to unlock additional sources of funding, such as impact investments. Pecan Street set-up the product testing environment, performed small-batch field testing, and provided a verified third-party performance report on the product of twelve companies. PSI Street and its partners leveraged the performance evaluations and insights from the newly formed PRI toolkit to raise over $1.5M across five companies, and garner interest for private sector adoption of the PLATFORM for Product Launch.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Study of an Integration Platform Based on an Adiabatic Active-Layer Waveguide Connection for InP Photonic Device Integration Mirroring That of Heterogeneous Integration on Silicon

In this work, a photonic device integration platform capable of integration of active-passive InP-based photonic devices without the use of material regrowth is introduced. The platform makes use of an adiabatic active-layer waveguide connection (ALWC) to move an optical beam between active and passive devices. The performance of this platform is analyzed using an example made up of four main sections: (1) a fiber coupling section for enabling vertical beam coupling from optical fiber into the photonic chip using a mode-matched surface grating with apodized duty cycles; (2) a transparent waveguide section for realizing passive photonic devices; (3) an adiabatic mode connection structure for moving the optical beam between passive and active device sections; and (4) an active device section for realizing active photonic devices. It is shown that the coupled surface grating, when added with a bottom gold reflector, can achieve a high chip-to-fiber coupling efficiency (CE) of 88.3% at 1550 nm. The adiabatic active-layer mode connection structure has an optical loss of lower than 1% (CE > 99%). The active device section can achieve an optical gain of 20 dB/mm with the use of only 3 quantum wells. The optimized structural parameters of the entire waveguide module are analyzed and discussed.

42 ENGINEERING↗

DOE OSTI2017

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

Here, we present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution (HOD), the conditional luminosity function (CLF), abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos, or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. Here, the package has an optimized toolkit to make mock observations on a synthetic galaxy population, including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others, allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Comparing in planta accumulation with microbial routes to set targets for a cost-competitive bioeconomy

Plants and microbes share common metabolic pathways for producing a range of bioproducts that are potentially foundational to the future bioeconomy. However, in planta accumulation and microbial production of bioproducts have never been systematically compared on an economic basis to identify optimal routes of production. A detailed technoeconomic analysis of four exemplar compounds (4-hydroxybenzoic acid [4-HBA], catechol, muconic acid, and 2-pyrone-4,6-dicarboxylic acid [PDC]) is conducted with the highest reported yields and accumulation rates to identify economically advantaged platforms and breakeven targets for plants and microbes. The results indicate that in planta mass accumulation ranging from 0.1 to 0.3 dry weight % (dwt%) can achieve costs comparable to microbial routes operating at 40 to 55% of maximum theoretical yields. These yields and accumulation rates are sufficient to be cost competitive if the products are sold at market prices consistent with specialty chemicals ($20 to $50/kg). Prices consistent with commodity chemicals will require an order-of-magnitude-greater accumulation rate for plants and/or yields nearing theoretical maxima for microbial production platforms. This comparative analysis revealed that the demonstrated accumulation rates of 4-HBA (3.2 dwt%) and PDC (3.0 dwt%) in engineered plants vastly outperform microbial routes, even if microbial platforms were to reach theoretical maximum yields. Their recovery and sale as part of a lignocellulosic biorefinery could enable biofuel prices to be competitive with petroleum. Muconic acid and catechol, in contrast, are currently more attractive when produced microbially using a sugar feedstock. Ultimately, both platforms can play an important role in replacing fossil-derived products.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Overhead longwave infrared hyperspectral material identification using radiometric models

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Optimizing TinyTPC Test Stand for Low Energy Event Reconstruction

Introducing dopants into liquid argon offers a promising avenue to enhance the sensitivity and performance of LAr detectors such as DUNE. To explore this opportunity, we are optimizing and deploying the TinyTPC test stand, a compact time projection chamber with LArPix readout to study the effects of photosensitive dopants on the charge detected. TinyTPC enables comparative measurements of low-energy responses under various operational configurations and provides a platform to study the behavior of pixel readouts at low energy. We ensured system reliability through continuous current and voltage monitoring, modeling the detector's electrical behavior, and indirectly estimating total resistance under testing conditions. We ran the Blanche cryostat under various thresholds and gain configurations to evaluate their potential impact to low-energy event reconstruction and advancing R&D efforts for dopant integration in neutrino detectors

Van Loenen, Drew [Franciscan U. Steubenville]↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is an extensible secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. Based on the ultra–low power TI MSP430 mixed-signal microcontroller, ACTS is an active device that uses an almost-universal core platform that can be appropriately configured with expansion modules to provide the application-specific data acquisition, data logging, container sealing, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, providing an integration path for current and new technologies. ACTS contains a built-in set of sensors and supports an expansion bus for up to six additional communications, tracking, authentication, encryption, sealing, location, or sensing modules that may enhance the monitoring and tracking of designated containers for specific applications. To date, testing has used an IEEE 802.15.4-2011 ultra-wideband–compliant wireless transceiver module that has additional capabilities for indoor positioning and proximity location. Proximity-based location (i.e., where an ACTS tag knows where it is relative to other ACTS tags) will be explored as a continuity-of-knowledge mechanism to determine unauthorized movement of containers in storage arrays of many items. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory and commercialization partner the Aquila Group.

Younkin, Jim↗

DOE OSTI2020

Density Matrix Quantum Circuit Simulation via the BSP Machine on Modern GPU Clusters

As quantum computers evolve, simulations of quantum programs on classical computers will be essential in validating quantum algorithms, understanding the effect of system noise, and design applications for future quantum computers. In this paper, we propose a novel multi-GPU programming model called MG-BSP that constructs a virtual BSP machine on top of modern multi-GPU platforms, and tweak the programming model to build a multi-GPU density matrix quantum simulator. We propose and evaluated a new formulation minimizing communication and prove that the transformation conserves original semantics when noise is introduced. We build the tool-chain to support quantum assembly open standard, synthesize testing quantum circuit, and enable ultra-deep quantum simulation. We evaluated our design on four state-of-the-art multi-GPU platforms including the latest DGX-1 and DGX-2 systems. We demonstrate simulation of 1 million gates in 94 minutes, far deeper circuits than has been demonstrated in prior work. A roofline-model analysis show that we have reached near-optimal performance under memory bound.

Li, Ang↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

Younkin, James↗

DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗

DOE OSTI2020

Centimeter-Scale Pillared-Layer Metal–Organic Framework Thin Films Mediated by Hydroxy Double Salt Intermediates for CO 2 Sensor Applications

Fabrication of metal–organic framework (MOF) thin films over macroscopic surface areas is a subject of great interest for gas sensor application platforms such as optics and microelectronics. However, a direct synthesis of MOF films at ambient conditions, in particular pillared-layer MOF films due to their anisotropic structures, remains a significant challenge. Herein, we demonstrate for the first time a facile construction of dense and continuous pillared-layer MOF thin films on a centimeter scale via an aluminum-doped zinc oxide template and hydroxy double salt (HDS) intermediates at room temperature. A series of Cu(II)-based pillared MOFs with different 1,4-benzenedicarboxylic acid (bdc) ligands were explored for optimizing MOF film formation for CO 2 sensor applications. Nonpolar ligands with lower water solubility preferentially formed crystalline pillared MOF structures from HDS intermediates. A Cu 2 (ndc) 2 (dabco) (ndc = 1,4-naphthalene-bdc; dabco = 1,4-diazabicyclo[2.2.2]octane) MOF demonstrated the most dense and uniform film growth with micrometer thickness over one square centimeter area. This synthetic approach for growing Cu 2 (ndc) 2 (dabco) MOF thin films was successfully translated toward two sensing platforms: a quartz crystal microbalance and an optical fiber sensor. These Cu 2 (ndc) 2 (dabco) MOF-coated sensors displayed sensitivity toward CO 2 and response/recovery time on the scale of seconds, even at moderate humidity levels. Finally, this work provides a road map for producing continuous and anisotropic crystalline MOF thin films over a centimeter scale area on various substrates, which will greatly facilitate their utilization in MOF-based sensor devices, among other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗

DOE OSTI2022

High-Temperature Gas-Cooled Reactor Research Survey and Overview: Preliminary Data Platform Construction for the Nuclear Energy University Program

Since the U.S. Department of Energy Office of Nuclear Energy initiated the Nuclear Energy University Program (NEUP) in 2009, there are 29 NEUP projects focusing on high-temperature gas-cooled reactor (HTGR) research up to July 2022. The resultant research product, either experimental or computational, were published as final NEUP reports, journal articles and conference proceedings. However, these federally funded products have been scattered and sometimes cannot be easily accessed. To improve access to this valuable HTGR validation data and optimize the return on the significant investment made by the Department of Energy, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects to develop a public-access database specific for HTGRs applications that can be used to retrieve computational fluid dynamics and system code validation data. This effort will help guide future NEUP-funded research, define new state of the ART Phenomena Identification and Ranking Table (PIRT), and promote the usage of this data in the codes validation matrices. This report provides an overview of the NEUP-funded HTGR-related research projects from Fiscal Year (FY) 2009–2021 and identifies validation knowledge gaps still existing in HTGR thermal-fluid research. A preliminary data platform has been developed for the 29 NEUP projects investigating HTGR thermal hydraulics, including their final reports as well as the available scientific publications. As an ultimate goal for this work, the ART-GCR program will create a central database at Idaho National Laboratory to identify, organize, and store these datasets generated by experimental investigations or computational models, experimental facility descriptions, and publicly-available academic products from the HTGR-related NEUP projects and provide future guidance for the storage and transmission of important project documentations for later NEUP projects as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Rapid Aqueous Spray Fabrication of Robust NiO x : A Simple and Scalable Platform for Efficient Perovskite Solar Cells

Organometal halide perovskites have powerful intrinsic potential to drive next-generation solar technology, but their insufficient thermomechanical reliability and unproven large-area manufacturability limit competition with incumbent silicon photovoltaics. This work addresses these limitations by leveraging large-area processing and robust inorganic hole transport layers (HTLs). Inverted perovskite solar cells utilizing NiO x HTLs deposited by rapid aqueous spray-coating that outperform spin-coated NiO x and lead to a 5× improvement in the fracture energy (Gc), a primary metric of thermomechanical stability, are presented. Additionally, the morphology, chemical composition, and optoelectronic properties of the NiO x films are characterized to understand and optimize compatibility with an archetypal double cation perovskite, Cs .17 FA .83 Pb(Br .17 I .83 ) 3 . Perovskite solar cells with sprayed NiO x show higher photovoltaic performance, exhibiting up to 82% fill factor and 17.7% power conversion efficiency (PCE)—the highest PCE reported for inverted cell with scalable charge transport layers—as well as excellent stability under full illumination and after 4000 h aging in inert conditions at room temperature. By utilizing open-air techniques and aqueous precursors, this combination of robust materials and low-cost processing provides a platform for scaling perovskite modules with long-term reliability.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Chapter 12: Gene Editing Technologies for Biofuel Production in Thermophilic Microbes

Thermophilic microbes are an attractive bioproduction platform due to their inherently lower contamination risk and their ability to perform thermostable enzymatic processes which may be required for biomass processing and other industrial applications. The engineering of microbes for industrial scale processes requires a suite of genetic engineering tools to optimize existing biological systems as well as to design and incorporate new metabolic pathways within strains. Yet, such tools are often lacking and/or inadequate for novel microbes, especially thermophiles. This chapter focuses on genetic tool development and engineering strategies, in addition to challenges, for thermophilic microbes. We provide detailed instructions and techniques for tool development for an anaerobic thermophile, Caldanaerobacter subterraneus subsp. tengcongensis, including culturing, plasmid construction, transformation, and selection. This establishes a foundation for advanced genetic tool development necessary for the metabolic engineering of this microbe and potentially other thermophilic organisms.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗