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DOE OSTI2015

JLab cryomodule assembly infrastructure modifications for LCLS-II

The Thomas Jefferson National Accelerator Facility is currently engaged, along with several other DOE national laboratories, in the Linac Coherent Light Source II project (LCLS II). The SRF Institute at Jefferson Lab will be building 1 prototype and 17 production cryomodules based on the TESLA / ILC / XFEL design. Each cryomodule will contain eight nine cell cavities with coaxial power couplers operating at 1.3 GHz. New and modified infrastructure and assembly tooling is required to construct cryomodules in accordance with LCLS-II requirements. The approach for modifying assembly infrastructure included evaluating the existing assembly infrastructure implemented at laboratories world-wide in support of ILC and XFEL production activities and considered compatibility with existing infrastructure at JLab employed for previous cryomodule production projects. These modifications include capabilities to test cavities, construct cavity strings in a class 10 cleanroom environment, assemble cavity strings into cryostats, and prepare cryomodules for cryogenic performance testing. This paper will give a detailed description of these modifications.

Daly, Edward F.↗

DOE OSTI2021

Nonlinear Optical Microscopy with Ultralow Quantum Light

Nonlinear optical (NLO) microscopy relies on multiple light–matter interactions to provide unique contrast mechanisms and imaging capabilities that are inaccessible to traditional linear optical imaging approaches, making them versatile tools to understand a wide range of complex systems. However, the strong excitation fields that are necessary to drive higher-order optical processes efficiently are often responsible for photobleaching, photodegradation, and interruption in many systems of interest. This is especially true for imaging living biological samples over prolonged periods of time or in accessing intrinsic dynamics of electronic excited-state processes in spatially heterogeneous materials. This perspective outlines some of the key limitations of two NLO imaging modalities implemented in our lab and highlights the unique potential afforded by the quantum properties of light, especially entangled two-photon absorption based NLO spectroscopy and microscopy. Here, we further review some of the recent exciting advances in this emerging filed and highlight some major challenges facing the realization of quantum-light-enabled NLO imaging modalities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Quantum Materials at the Nanoscale - Final Report

The central aim of the Quantum Materials at the Nanoscale (QMN) cluster was to understand and control collective behavior involving the interplay of spins, orbitals, and charges, which governs many scientifically interesting and technologically important phenomena in numerous complex materials. Because these phenomena involve various competing interactions, and influence properties on many different length and energy scales in complex materials, tackling this important area of study motivated a collaborative effort that combined the diverse capabilities of QMN cluster experimentalists, the essential theoretical analysis provided by QMN cluster theorists, and the outstanding facilities and staff of the FSMRL. During the funding period 2007-2014, the DOE cluster grant for the Quantum Materials at the Nanoscale (QMN) cluster supported, at various times, 15 different faculty members (14 in Physics and 1 in Materials Science and Engineering), 7 postdoctoral research associates, and 57 physics and materials science PhD students. 41 of these PhD students have since graduated and have gone on to a variety of advanced technical positions at universities, industries, and national labs: 25 obtained postdoctoral positions at universities (14), industrial labs (2 at IBM), DOE national facilities (3 at Argonne National Laboratory, 1 at Brookhaven National Lab, 1 at Lawrence Berkeley National Lab, and 1 at Sandia National Lab), and other federal facilities (2 at NIST); 13 took various industrial positions, including positions at Intel (5), Quantum Design (1), Lasque Industries (1), Amazon (1), Bloomberg (1), and J.P. Morgan (1). Thus, the QMN grant provided the essential support for training a large number of technically advanced personnel who have now entered key national facilities, industries, and institutions. Additionally, during the period 2007-2015, the QMN cluster produced 159 publications (see pages 14-23), including 23 papers published in Physical Review Letters; 16 papers in Nature, Nature Physics, Nature Materials, or Nature Communications; 4 papers in Science, and 8 papers in Applied Physics Letters. In this report, we provide some key highlights of the collaborative projects in which the QMN cluster members have been involved since 2007.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

2018 LDRD annual report

Fiscal Year (FY) 2018 marked the thirteenth year of Savannah River National Laboratory’s (SRNL’s) Laboratory Directed Research and Development (LDRD) program and the largest year to date. When the program began in FY06, just two years after SRNL becoming the DOE’s 17th national lab, it began with a modest authorized collection rate of just under 1.5%. It grew from there each year, reaching 5% in FY17. The authorized collection rate was maintained at 5% on FY18. The increase in collection rates experienced during the last years evidences the value placed on this important program, which provides the discretionary funding needed to develop ideas for future programmatic growth in our key business areas of environmental stewardship, national security, nuclear materials management, and secure energy manufacturing, as well as develop and maintain our core competencies. The LDRD program leverages the unique capabilities of SRNL to yield foundational scientific research and development (R&D) essential to our core business areas, while aligning optimally and continuously with SRNL’s Strategic Plan and providing long-term benefits to DOE and the National Nuclear Security Administration (NNSA), other customers, and stakeholders. As can be seen in the “By the Numbers” summary, this program also plays an important role in sustaining staff excellence with high participation by students, post-docs, and early-career staff. Over 85% of the projects in FY18 included contributions from early-career or post-doctoral staff.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

H2@Scale - Validating an Electrolysis System with High Output Pressure: Cooperative Research and Development Final Report, CRADA Number CRD-18-00741

Electrolysis has been a commercially available product for a while and electrolyzers have been a proven capability to provide additional benefits (e.g. controllable load for grid services) in addition to production of hydrogen. The hydrogen output is typically compressed for storage and dispensing. Compression adds cost and decreases system reliability. Honda’s electrolyzer systems have been developed to include electrochemical compression to leverage the production system itself for at least partial compression. In this project, the team will evaluate Honda’s PEM based electrochemical compression system. The system is capable of compressing hydrogen up to 70 MPa electrochemically. Validation testing is the next step to accelerate this technology into the marketplace, as the validation will provide needed data under a variety of operation conditions and controls. These operating conditions and controls are based on over a decade of NLR research and development with low-temperature electrolysis. The validation testing will include preparing NLR’s site for third party evaluation, benchmark testing of Honda’s stack and system, and simulating operation connected to renewables or in a grid service profile. NLR’s Energy System Integration Lab will be the location for the electrolyzer validation research and integrated into the Hydrogen Infrastructure Test & Research Facility (HITRF). This will build into the existing retail style hydrogen fueling station for a fully integrated experimental setup.

08 HYDROGEN↗

DOE OSTI2025

Unlocking America's Abundant Marine Energy Resources

NREL Marine Energy One-Pager: A resource designed for use during NREL campus visits - especially with high-profile guests - as well as at events in Washington, D.C., when engaging with the new administration, and for displays at WPTO or lab booths. The U.S. holds vast untapped marine energy resources - wave, tidal, river, and ocean currents - that can strengthen grid resilience, support coastal communities, and advance energy independence. NREL leads innovation in this space through advanced modeling tools, patented technologies, and world-class testing facilities. NREL's simulation platforms, like OpenFAST and SAM, help reduce development time and risk. Patented devices such as PKelp and FlexWEC demonstrate flexible, resilient approaches to energy capture. At Flatirons Campus, NREL offers motion simulation, structural testing, wave tanks, and megawatt-scale microgrid emulation via the ARIES platform. These capabilities help developers refine and validate devices before in-water trials. With upcoming open-water testing at PacWave and strong partnerships, NREL is accelerating the path to commercialization - positioning the U.S. as a global leader in marine energy innovation.

17 WIND ENERGY↗

DOE OSTI2021

Perovskite on Silicon Tandem Solar Cells

Since installing solar panels costs more than the panels themselves, it is critically important to improve the efficiency of the panels so that fewer panels need to be installed. More than 90% of all panels being manufactured are made with silicon solar cells. The most promising approach to reach 27% is to upgrade the panels by adding a second set of cells with a larger band gap that can harvest the higher energy photons in the visible spectrum and generate a higher voltage than silicon is capable of generating. Perovskite semiconductors are very promising for low-cost tandems because they have high band gaps, can be rapidly printed from solutions and have been used to make 25% efficient cells by themselves. If we show that it is possible to make stable tandems with 30% efficiency using materials that could be deposited at low cost and that the energy yield will not be compromised, then private companies would be able to raise the capital needed to manufacture this technology. The main metrics for this project are the power conversion efficiency of a lab-scale perovskite/silicon tandem and the ability to maintain a high power conversion efficiency in perovskite solar cells after accelerated operational stability testing under maximum power point at elevated temperature and 1 sun illumination. In short, this project demonstrates improvements to the perovskite absorber layer, contacts for the perovskite solar cell, and transparent oxide layers for window electrodes to achieve tandem efficiencies of 25% after one year and 27% after two years. By the end of the project, we identify strategies to further improve light management in the tandem devices and outline a pathway to reach 30%.In parallel, stability testing is conducted to evaluate said absorber and contact layers in the perovskite top cell, demonstrating 1000 hours of operation with less than 10% degradation at 1 sun. Our work on this project has made a leading impact on the development of perovskite/silicon tandems. The triple halide perovskite that we developed using iodide, bromide, and chloride and published in Science in March 2020 has received significant interest from other experts in the field at conferences. We took a silicon bottom cell with 21% efficiency, a technology that is responsible for a $30 billion market, and improved it by 30% relative. In addition, we have demonstrated impressive stability of our perovskite semitransparent top cells used in the tandems, showing for the first time packaged devices that pass a variety of IEC stability tests including damp heat, temperature cycling, and UV exposure tests.

14 SOLAR ENERGY↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

DOE OSTI2025

LHCspin: a Polarized Gas Target for LHC

The goal of the LHCspin project is to develop innovative solutions for measuring the 3D structure of nucleons in high-energy polarized fixed-target collisions at LHC, exploring new processes and exploiting new probes in a unique, previously unexplored, kinematic regime. A precise multi-dimensional description of the hadron structure has, in fact, the potential to deepen our understanding of the strong interactions and to provide a much more precise framework for measuring both Standard Model and Beyond Standard Model observables. This ambitious task poses its basis on the recent experience with the successful installation and operation of the SMOG2 unpolarized gas target in front of the LHCb spectrometer. Besides allowing for interesting physics studies ranging from astrophysics to heavy-ion physics, SMOG2 provides an ideal benchmark for studying beam-target dynamics at the LHC and demonstrates the feasibility of simultaneous operation with beam-beam collisions. With the installation of the proposed polarized target system, LHCb will become the first experiment to simultaneously collect data from unpolarized beam-beam collisions at $\sqrt{s}$=14 TeV and polarized and unpolarized beam-target collisions at $\sqrt{s_{NN}}\sim$100 GeV. LHCspin has the potential to open new frontiers in physics by exploiting the capabilities of the world's most powerful collider and one of the most advanced spectrometers. This document also highlights the need to perform an R&D campaign and the commissioning of the apparatus at the LHC Interaction Region 4 during the Run 4, before its final installation in LHCb. This opportunity could also allow to undertake preliminary physics measurements with unprecedented conditions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2012

Performance Validation of Refrigeration Recovery for Experimental Hall High Target Loads

The Qweak experiment at Jefferson Lab (JLab) is a 3000 W hydrogen target scheduled to run until the planned shutdown in the spring of 2012 for the 12 GeV installation. As detailed in previous proceedings, support of this target's cryogenic load was made possible by incorporating modifications to the End Station Refrigerator (ESR) to recover the refrigeration supplied by the Central Helium Liquefier (CHL). Testing and commissioning for these modifications was performed in January and February 2010 demonstrating that the performance met or exceeded projected expectations. In this paper, we present the analysis of the test results in regards to the actual loads capable of being supported and the process boundaries encountered, as well as a discussion of the commissioning results for the cryogenic support of the Qweak target.

08 HYDROGEN↗

DOE OSTI2022

ROAM: A Remotely Operated Accelerator Monitor

Monitoring accelerators in operation is a well known challenge due to the radiation environment. However, there are significant benefits in being able to deploy particular sensors in specific locations of accelerator enclosures for monitoring or troubleshooting purposes. Learning from experience at other labs, we used Commercial Off The Shelf (COTS) components and an open source robot control software framework (ROS) to build a remotely controlled robot platform including a standard suite of instruments such as cameras, LIDAR, and ultrasound, with the ability to incorporate other ad-hoc sensors for specific measurements, such as a Gamma radiation monitor. Special attention was given to the robot's ability to maintain safety in the high risk environment of an accelerator, with multiple failure contingencies in place to ensure collision free operation. Testing was performed to prove the platform's viability and showcase its capability for accelerator monitoring.

Thayer, Thomas C.↗

DOE OSTI2025

Hardware-in-the-Loop Using Electromagnetic Transient Simulation

Digital real time simulators have the capability to run electromagnetic transient simulations in real time. This capability allows users to leverage the hardware-software combination to evaluate controller performance, protection device performance, and power device performance. This has helped many field deployment projects to be successful and be cost-effective. In this talk, we will present current state-of-art, and future of real time electromagnetic transient simulation and its impacts on field deployment.

08 HYDROGEN↗

DOE OSTI2015

Development of a Novel Gas Pressurized Stripping Process-Based Technology for CO₂ Capture from Post-Combustion Flue Gases

A novel Gas Pressurized Stripping (GPS) post-combustion carbon capture (PCC) process has been developed by Carbon Capture Scientific, LLC, CONSOL Energy Inc., Nexant Inc., and Western Kentucky University in this bench-scale project. The GPS-based process presents a unique approach that uses a gas pressurized technology for CO₂ stripping at an elevated pressure to overcome the energy use and other disadvantages associated with the benchmark monoethanolamine (MEA) process. The project was aimed at performing laboratory- and bench-scale experiments to prove its technical feasibility and generate process engineering and scale-up data, and conducting a techno-economic analysis (TEA) to demonstrate its energy use and cost competitiveness over the MEA process. To meet project goals and objectives, a combination of experimental work, process simulation, and technical and economic analysis studies were applied. The project conducted individual unit lab-scale tests for major process components, including a first absorption column, a GPS column, a second absorption column, and a flasher. Computer simulations were carried out to study the GPS column behavior under different operating conditions, to optimize the column design and operation, and to optimize the GPS process for an existing and a new power plant. The vapor-liquid equilibrium data under high loading and high temperature for the selected amines were also measured. The thermal and oxidative stability of the selected solvents were also tested experimentally and presented. A bench-scale column-based unit capable of achieving at least 90% CO₂ capture from a nominal 500 SLPM coal-derived flue gas slipstream was designed and built. This integrated, continuous, skid-mounted GPS system was tested using real flue gas from a coal-fired boiler at the National Carbon Capture Center (NCCC). The technical challenges of the GPS technology in stability, corrosion, and foaming of selected solvents, and environmental, health and safety risks have been addressed through experimental tests, consultation with vendors and engineering analysis. Multiple rounds of TEA were performed to improve the GPS-based PCC process design and operation, and to compare the energy use and cost performance of a nominal 550-MWe supercritical pulverized coal (PC) plant among the DOE/NETL report Case 11 (the PC plant without CO₂ capture), the DOE/NETL report Case 12 (the PC plant with benchmark MEA-based PCC), and the PC plant using GPS-based PCC. The results reveal that the net power produced in the PC plant with GPS-based PCC is 647 MWe, greater than that of the Case 12 (550 MWe). The 20-year LCOE for the PC plant with GPS-based PCC is 97.4 mills/kWh, or 152% of that of the Case 11, which is also 23% less than that of the Case 12. These results demonstrate that the GPS-based PCC process is energy-efficient and cost-effective compared with the benchmark MEA process.

Chen, Shiaoguo↗

DOE OSTI2014

Optimization of magnetic switches for single particle and cell transport

The ability to manipulate an ensemble of single particles and cells is a key aim of lab-on-a-chip research; however, the control mechanisms must be optimized for minimal power consumption to enable future large-scale implementation. Recently, we demonstrated a matter transport platform, which uses overlaid patterns of magnetic films and metallic current lines to control magnetic particles and magnetic-nanoparticle-labeled cells; however, we have made no prior attempts to optimize the device geometry and power consumption. Here, we provide an optimization analysis of particle-switching devices based on stochastic variation in the particle's size and magnetic content. These results are immediately applicable to the design of robust, multiplexed platforms capable of transporting, sorting, and storing single cells in large arrays with low power and high efficiency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

DNA Everywhere. A Guide for Simplified Environmental Genomic DNA Extraction Suitable for Use in Remote Areas

Collecting field samples from remote or geographically distant areas can be a financially and logistically challenging. With participation of a local organization where the samples are originated from, gDNA samples can be extracted from the field and shipped to a research institution for further processing and analysis. The ability to set up gDNA extraction capabilities in the field can drastically reduce cost and time when running long-term microbial studies with a large sample set. The method outlined here has developed a compact and affordable method for setting up a “laboratory” and extracting and shipping gDNA samples from anywhere in the world. This white paper explains the process of setting up the “laboratory”, choosing and training individuals with no prior scientific experience how to perform gDNA extractions and safe methods for shipping extracts to any research institution. All methods have been validated by the Andersen group at Lawrence Berkeley National Laboratory using the Berkeley Lab PhyloChip.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

An Observational Understanding of Connected Lighting Systems

As part of the Internet of Things (IoT), connected lighting systems provide additional control capabilities and nonlighting-related services (such as communications and data infrastructure) to reduce energy use while increasing both application efficiency and system value. But the positive or negative energy impact of implementing these systems is still largely unknown due to the variety of human factors associated with installation, maintenance, and use. These complications interfere with adoption and acceptance rates of products that will play a significant role in achieving energy savings long-term. Next Generation Lighting Systems, or NGLS, developed and implemented an observational research methodology for connected lighting systems. The methodology was implemented at an indoor (classroom/office) Living Lab at Parsons School of Design in New York City, and an outdoor (parking lot) Living Lab at Virginia Tech Transportation Institute in Blacksburg, Virginia. Although this report focuses on the approach to indoor installations, observational research in both settings provides insight to the type, scale, and frequency of issues across available products as experienced by installing contractors, end users, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Accomplishments and Year-End Performance Report; Wind Energy Program: Fiscal Year 2021

The National Wind Technology Center (NWTC), located at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) Flatirons Campus, has been a driving force in advancing wind energy technology research worldwide since its designation as a DOE national research center in 1992. Enabled by the Flatirons Campus's world-class facilities, scientists, engineers, analysts, and researchers are pushing the frontiers of science to pursue wind energy innovation. In Fiscal Year (FY) 2021, NREL continued to provide the technical expertise, research capabilities, and industry understanding to support DOE's ambitious climate action and research goals by advancing technology, addressing market and deployment barriers, and driving down costs with more efficient, reliable, and predictable wind energy systems. One of several highlights, NREL received an R&D 100 Special Recognition Award for its Thermoplastic Resin System for Wind Turbine Blades. This breakthrough in the wind turbine manufacturing process will enable the production of recyclable blades that are stronger, longer, and less expensive, while increasing energy capture, decreasing energy and transportation costs, and increasing blade reliability. In a year when the entire U.S. economy struggled to address workforce gaps, an NREL study compared wind industry needs, training programs, and hiring practices with perspectives from students and recent college graduates. Researchers hope that, by pinpointing areas of disconnect, the expectations of employers who have difficulty filling entry-level jobs can better align with the preparation of the potential applicants who find it hard to break into the field. The lab also made numerous new data and modeling resources available in FY 2021. Recent NREL releases include a modeling tool for predicting the power performance and structural loads of wind turbines within a wind farm (FAST.Farm), a computational framework for modeling golden eagle behavior near wind farms, and 20 years of offshore wind data. Updates were also made to the widely used Wind Plant Integrated Systems Design and Engineering Model (WISDEM), which couples engineering and cost models to examine system-level trade-offs. Now, bolstered by a renewed national commitment to tackle climate change and revitalize the U.S. economy through increased investment in clean energy - particularly in offshore wind energy - NREL stands poised to lead the way to a sustainable future that powers the United States with significant levels of reliable, low-cost, accessible wind energy. This report provides an overview of the achievements NREL made on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during FY 2021 (between Oct. 1, 2020, and Sept. 30, 2021).

Flatirons Campus↗

DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗