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A membrane-based emulsion separator can enable continuous solvent extraction in bioprocessing separations with substantially improved demulsification and mass transfer rates, including where emulsion formation is a challenge.
The rapidly increasing use of electronics in high-radiation environments and the continued evolution in transistor architectures and materials demand improved methods to characterize the potential damaging effects of radiation on device performance. Here, electron-beam-induced current is used to map hot-carrier transport in model metal-oxide semiconductor field-effect transistors irradiated with a 300 KeV focused He + beam as a localized line spanning across the gate and bulk Si. By correlating the damage to the electronic properties and combining these results with simulations, the contribution of spatially localized radiation damage on the device characteristics is obtained. Furthermore, this identified damage, caused by the He + beam, is attributed to localized interfacial P b centers and delocalized positive fixed-charges, as surmised from simulations. Comprehension of the long-term interaction and mobility of radiation-induced damage are key for future design of rad-hard devices.
Beamlines are facilities that produce and deliver highly focused and intense beams of radiation, typically x rays, synchrotron radiation, or neutrons, for scientific research purposes. Millions of dollars are spent annually to maintain and operate these scientific beamlines, oftentimes running continuously between cycles. To reduce human intervention and improve productivity, mechanical sample changers are often commissioned for use. Designing sample changers is difficult because mechanical parts can be bulky, expensive, and challenging to design for instruments with low volume access, high radiation, and cryogenic environments. We present a portable and inexpensive sample changer stick that can hold and manipulate up to four samples, specifically designed for use with cryogenic closed-cycle refrigerators. The sample changer stick enables rapid and efficient exchange of samples without manual intervention, and is compatible with standard sample mounts such as vanadium cans. The sample changer stick includes a motorized rotation and lancing mechanism, which enables the precise positioning of each sample in the neutron beam, while ensuring compatibility with the operating temperatures and vacuum conditions required for closed-cycle refrigerators. The design has been successfully tested at the VISION beamline at the Spallation Neutron Source. The mechanical action and software controls are detailed. Furthermore, the sample changer stick is a valuable tool for scientists working with cryogenic closed-cycle refrigerators.
A novel deep-ridge laser structure with atomic-layer deposition (ALD) sidewall passivation was proposed that enhances the optical characteristics of 8-µm ridge width III-nitride violet lasers on freestanding m -plane GaN substrates. The internal loss was determined using the variable stripe length method, where the laser structure with ALD sidewall passivation showed lower internal loss compared to the conventional shallow-ridge laser design. ALD sidewall passivation plays a critical role in device improvements; compared to the lasers without ALD sidewall passivation, the lasers with ALD sidewall passivation yield improved optoelectrical performance and longer lifetime under continuous-wave operation at high current density. This work demonstrates the importance of ALD sidewall passivation to laser performance, which enables high energy efficiency.
As the demand for more efficient SRF technology continues to rise, so does the need to improve the performance of Nb3Sn, the most promising alternative to niobium. Leveraging recent breakthroughs in Nb3Sn research from Cornell University and Fermilab, namely the electrochemical synthesis-based growth of Nb3Sn and the centrifugal barrel polishing (CBP) technique to smoothen the final Nb3Sn film, our primary goal is to reduce surface roughness while preserving the film’s quality. We present promising RF results from an electroplated cavity that underwent mechanical polishing using the CBP technique, which showed an increase in the maximum accelerating gradient compared to the baseline test. However, contamination-induced Q-slope limited performance. Temperature mapping reveals quench location and heating response before, during and after quench.
The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.
The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through effective allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2023, the OLCF supported 1,676 users and 598 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.52 out of 5 on the annual user survey, and 94% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,619 user tickets submitted in CY 2023, OLCF staff resolved 97% within 3 business days. The facility opened Frontier to full scientific operations this year. Two projects conducted on Frontier received the Association for Computing Machinery (ACM) Gordon Bell Prize and the Gordon Bell Special Prize for Climate Modeling, and a third earned a nomination as a Gordon Bell Prize finalist. The facility’s previous flagship machine, Summit, gained new life and was extended through 2024 in part to help provide resources to the Integrated Research Infrastructure (IRI) projects and the National Artificial Intelligence Research Resource (NAIRR) pilot program. The facility instantiated an Advanced Computing Ecosystem testbed in part to support IRI workflows. OLCF made interactivity easier and more accessible to users than ever through tools like Jupyter notebooks and workflows.
Cavities and cryomodules assembled at Fermilab have demonstrated unprecedented field emission (FE) free gradients. However, consistent FE-free performance is not guaranteed. Many lessons were learned, and continued vigilance is a must. In addition, several improvements have been identified to further push the state-of-the-art low particulate cavity processing and assembly at Fermilab. Those included the optimization of nitrogen flow, robotic-assisted assembly, and low-particulate fasteners. We share our latest results and vision for the future clean assemblies of cavities and cryomodule strings.
The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure to the US Department of Energy (DOE) computing community. As a DOE Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through effective allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world.
Objective performance and economic modeling of solar thermal plants is of keen interest to many EPRI funders. One solar thermal technology that is not currently available to model in any non-vendor, non-proprietary tool is linear Fresnel. This technology has garnered enough interest from EPRI funders to merit investing in a tool to objectively model its performance. Early in 2010, EPRI performed a comparison of modeling solar thermal power plants using the IPSEPRO, CNRS and Solar Advisor (SAM) tools. After completing this effort, EPRI decided to adopt NREL’s Solar Advisor Model as its default modeling tool based in part on user friendliness, flexibility, number of technologies covered, integrated financial model and ease of running sensitivities. Furthermore, it was recognized that NREL continues to invest considerable time and resources into improving capabilities and functionality of the model.
The Activated Materials Laboratory (AML), located in the Long Beamline Building of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL), provides a centralized radiological capability for preparing, handling, and supporting synchrotron experiments on activated materials. As a Nuclear Science User Facilities (NSUF) partner facility, the AML enables the receipt of radioactive shipments, open-form sample handling, encapsulation, transport of specimens to and from APS beamlines, and experimentation with dedicated equipment. The partnership includes the APS 1-ID and 20-ID beamlines, which provide high-energy x-ray scattering, tomography, and diffraction microscopy techniques for ex-situ, in-situ, and grainresolved three-dimensional (3D) characterization. All samples supported through the AML and partner beamlines must meet radiological limits of less than 100 mrem/h at 30 cm. This FY26 progress report summarizes the first full year of AML operations as an NSUF partner facility and highlights progress in both user support and capability development. By the end of FY26, the AML had received a total of 10 NSUF-awarded projects, including 2 Consolidated Innovative Nuclear Research (CINR), 7 Rapid Turnaround Experiment (RTE), and 1 Super RTE projects. Beamtime was fully delivered for 3 RTE projects and partially delivered for 1 CINR project, demonstrating successful workflows for receipt, encapsulation, beamline transfer, and radiological experiment execution. These efforts demonstrated safe radiological experiments at APS beamlines for samples with dose rates above the historical 5 mrem/h threshold and now up to 100 mrem/h at 30 cm, marking an important milestone for neutron-irradiated materials research. During FY26, the AML also expanded its experimental capabilities. A Psylotech xTS load frame with in-grip rotation was deployed for room-temperature mechanical testing with threedimensional x-ray characterization during interrupted loading. A Linkam TS1500V vacuum heater was commissioned for thermally driven studies, and temperature calibration experiments were performed to establish specimen-relevant thermal profiles. In parallel, a customized split-tube furnace for high-temperature mechanical testing entered commissioning, and initial work began on robotic sample handling to reduce worker dose and improve operational efficiency. Data workflow and beamline operations continued to mature. Together, these developments demonstrate that the AML is becoming a unique national resource for safe, efficient, and scientifically advanced characterization of activated materials at the APS.
A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.
As the third most important greenhouse gas (GHG) after carbon dioxide (CO 2 ) and methane (CH 4 ), tropospheric ozone (O 3 ) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O 3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O 3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O 3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O 3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O 3 and its precursor species. The MMM underestimates Arctic surface O 3 by 5% to 15% depending on the location. The vertical distribution of tropospheric O 3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O 3 within a range of ±50% depending on the model and the altitude. The MMM performs best, within ±8% for most locations and seasons. However, nearly all models overestimate O 3 near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O 3 at high latitudes. For example, the MMM is biased high by about 20% at Eureka. Observed and simulated O 3 precursors (CO, NO x , and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NO x but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O 3 matches the observed O 3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O 3 .
A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.
As large national investments in EV charging continue to be made, this project seeks to improve the state-of-the-art in EV infrastructure analysis. New and improved quantitative analysis capabilities will position VTO to support EERE Leadership, assist the DOT/DOE Joint Office through timely analysis, and provide a strategy for the deployment of the national charging network. This project supports on-going development of NREL's EVI-X Suite of charging infrastructure analysis tools, including tools supporting large-scale network planning, local site design decisions, and comprehensive financial analysis. EVI-X development complements and leverages other VTO-funded efforts to ensure inputs to EVI-X and output produced by EVI-X are consistent with and well-integrated into the VTO portfolio (e.g., EV adoption scenarios from TEMPO, charging infrastructure design provided to EVs@Scale, etc.).
MXenes are 2D materials composed of layered transition metal nitrides or carbides. These materials are synthesized by HF exfoliation from MAX phases (Ti{sub 3}AlC{sub 2}). The 2D nanomaterial was synthesized by the removal of the 'A' element, resulting in a Mxene product (Ti{sub 3}C{sub 2}). MXenes have the general formula M{sub n+1}X{sub n}T{sub x}, where M is an early transition metal, X is Carbon and/or Nitrogen, and T accounts for surface terminated functional groups such as Fluoride, hydroxyl, and oxygen. These materials have very unique properties, similar to graphene, that allows them to be applied in a variety of trace detection techniques including surface-enhanced Raman spectroscopy (SERS). MXenes have also been demonstrated to selectively uptake uranyl ion, UO{sub 2}{sup 2+}. If this property can be combined with SERS or fluorescence detection, it may be possible to use MXenes as the basis for an alternative method to kinetic phosphorescence analysis (KPA) for trace uranyl measurements. Objectives: To confirm that MXene Nano materials are suitable substrates for SERS and sensor development by enhancing Raman signaling. To determine if certain MXene preparation methods yield materials that are more suitable for trace sensing methods. To determine uranyl uptake properties of these MXene materials and test them for analytical signals. Sample Preparation: Preparation of Ti{sub 3}C{sub 2}MXene (at FSU). MXenes were prepared by etching Al from Ti{sub 3}AlC{sub 2} (MAX phase)material. Two etching techniques yield different MXene products: LiF/HCl: Milder reaction, larger MXene flakes. HF: Harsher reaction, smaller flakes, larger layer separation. Products washed to remove etchant, vacuum filtered, and dried. Dried MXene flakes are air-stable. Film preparation for sensor testing (at SRNL): Suspend powder in diH{sub 2}O, purge with Ar, sonicate for 30 min. Centrifuge and collect supernate with suspended particles. Observed LiF-etched Mxene yielded a higher density of particles and darker collected solution. Drop-cast (4 ml) supernate onto slides and dried with Ar. For Rhodamine B (RhB) testing, drop-cast 4 ml drops onto Mxene spots and dried with Ar. Scanning Electron Microscopy conditions: 10 kV Beam energy, high vacuum; Working distance of 8 mm; beam penetration depth appx. 4 microns, beam spot size appx. 2 nanometers. Results: Detection of aluminum correlates with bright spots on image. Presence of aluminum shows that LiF/HCl etching was less thorough than HF etching. Trace Cl detection in LiF images suggests incomplete rinsing. HF has smaller feature size, more layer structure, and increased homogeneity, consistent with expectations. Macroscopic Raman spectroscopy measurements: 532 nm excitation, ∼50 mW with a ∼100 micron spot size (InPhotonics RPB probe). Kaiser Optical Holospec f/1.8 spectrometer with cooled (-60 deg.C) Andor iDus OE420 CCD. LiF 1x supernate showed good signal for trace measurements of Rhodamine B. HF and 1/4x LiF supernates showed little Mxene or Rhodamine B signal. Low deposition densities led to excess background signal from glass slides. For LiF film, response is linear with Rhodamine B concentration over range tested. Will retest with Raman microscope (∼1 micron spot size) to characterize SERS of more dilute LiF and HF etched Mxenes. Conclusions: The LiF etched material was more suitable for macroscopic SERS measurements because it was more concentrated, resulting in a thicker film than the HF etched Mxene and diluted LiF sample. However, the other materials may give greater SERS enhancements, which we hope to determine from measurements with the Raman microscope. From characterization with SEM we concluded that the HF etched Mxene is more uniform/homogenous and has smaller particle size than the LiF etched Mxene. There is still aluminum present in both samples indicating that etching wasn't complete, but the removal of the aluminum was more efficient in the HF method. Path Forward: Observe SERS with Raman microscopy, to obtain better signals for the more diluted samples and be able to compare enhancement effects for the different MXenes. Characterize uranyl sorption into MXene films and test Raman and fluorescence signals. Revisit the etching conditions to improve removal of aluminum. FSU and SRNL will continue to collaborate to create and characterize different Mxene materials and test their usefulness for sensor applications.
Post-irradiation examination (PIE) is a continually growing field critical to the development of improved nuclear fuels. To characterize these materials, neutron imaging systems are employed and outfitted with, typically, CMOS or CCD cameras - complementary metal oxide semiconductor sensors and charge-couple devices. Imaging systems are a team-effort between the camera, scintillating materials, and object of interest, as a result of neutrons' inherent need to be converted to a detectable signal. High resolution imaging, flash radiography imaging, and event-mode detection systems are three systems undergoing development, construction, and characterization for improved spatial resolution and time-of-flight detection for PIE efforts.
The Sun provides the only local laboratory to test our understanding of stellar physics. The production, then, of valid dynamical models of the Sun is critical to how we think about other stars. Despite improvements in modeling methodologies and abundance measurements, solar models continue to reproduce errors in convection zone depth and surface helium abundance compared to helioseismic observations. Using the 1D stellar evolution code MESAstar, this work first evaluates the effect of changes to timestep resolution and mixing length theory on model outputs. A standard test case is then constructed with both GS98 and AGSS09 abundances using OP and OPAL opacities before implementing the new Los Alamos OPLIB opacities. It is shown that, when using GS98 abundances, OPLIB opacities improve agreement with helioseismic inference of the convection zone base radius, R czb , by 1.68-σ and 1.19-σ compared to OP and OPAL, respectively, while worsening agreement with the inferred envelope helium mass fraction, Y surf , by 0.63-σ and 0.98-σ for OP and OPAL.