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At least 73 records · Page 4

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗

Mitigation of Boiling-Induced Thermal Degradation Using Microporous Nickel Inverse Opals Structures

Engineered microporous structures have received much attention in high-heat-flux electronics cooling due to their high thermal conductivity and permeability, and large surface area for heat transfer, but are susceptible to boiling-induced thermal degradation. Here, this study investigates the efficacy of nickel inverse opals (NiIOs) in mitigating structural degradation caused by corrosion-assisted erosion during pool boiling with water as the working fluid. First, we compared the reliability of NiIOs to copper inverse opals (CuIOs) for a 3-day pool boiling test at constant heat flux. The NiIOs demonstrated superior resistance to thermal degradation due to their inherent corrosion resistance and mechanical strength. Subsequently, we conducted a more controlled experiment to show the effect of heat flux on the degradation of the NiIOs while excluding the effect of temperature variations. Pool boiling tests of 20-μm-thickness NiIOs covering an area of ∼11 × 11 mm 2 with a 2.5 × 2.5 mm 2 heater at the center were conducted at heat flux levels of 20%, 40%, and 60% of the critical heat flux (CHF) for 3 days. The NiIOs subjected to heat flux levels of 20% and 40% CHF showed minimal degradation, while the sample subjected to 60% CHF showed erosion on the top surface due to higher bubble formation and departure rate. These results show the potential of NiIOs as a promising solution for long-term thermal management in high-power electronic devices, although design considerations for maximum allowable heat flux are necessary for reliable operation.

36 MATERIALS SCIENCE↗

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

54 ENVIRONMENTAL SCIENCES↗

Ultrafast optical modulation of vibrational strong coupling in ReCl(CO) 3 (2,2-bipyridine)

Polaritons – hybrid light-matter states formed from the strong coupling of a bright molecular transition with a confined photonic mode – may offer new opportunities for optical control of molecular behavior. Vibrational strong coupling (VSC) has been reported to impact ground-state chemical reactivity, but its influence on electronic excited-state dynamics remains unexplored. Here, we take a first step towards excited-state VSC by demonstrating optical modulation of the ReCl(CO) 3 (bpy), (bpy = 2,2-bipyridine) complex under VSC using femtosecond ultraviolet (UV)-pump/infrared (IR)-probe spectroscopy. We establish ground-state VSC of ReCl(CO) 3 (bpy) in a microfluidic Fabry-Pérot cavity equipped with indium tin oxide (ITO)-coated mirrors. ITO is effectively dichroic as it is reflective in the IR and transmissive in the UV-visible and therefore minimizes optical interference. Excitation with UV pump light drives ReCl(CO) 3 (bpy) into a manifold of electronic excited states that subsequently undergo non-radiative relaxation dynamics. We probe the transient response of the strongly-coupled system in the mid-IR, observing both Rabi contraction and cavity-filtered excited-state absorption signatures. We reconstruct the intrinsic response of intracavity molecules from the transient cavity transmission spectra to enable quantitative comparison with extracavity control experiments. We report no changes in the excited-state dynamics of ReCl(CO) 3 (bpy) under ground-state VSC. However, we do observe significant amplification of transient vibrational signals due to classical cavity-enhanced optical effects. This effort lays the groundwork to pursue direct excited-state VSC aimed at modulating photochemical reactivity.

cavity-enhanced spectroscopy↗

COMPASS-FME Synoptic Site Tree Greenhouse Gas Concentrations

These data are tree stem greenhouse gas concentrations collected from tree gas wells at some of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales; see https://compass.pnnl.gov/) 'synoptic' sites in the Chesapeake Bay region: Moneystump (MSM), Goodwin Islands (GWI), and GCReW (GCW). The sap flow monitoring trees at these sites in the Upland (UP) and Transition (TR) zones were cored and had gas wells installed at breast height. There were also some dead standing trees cored, gas well installed, and sampled at MSM and GWI. The GCW UP samples overlap with the TEMPEST experiment control plot, so the GCW UP data was pulled from the TEMPEST page and included here. These data provide crucial information about possible pathways for the greenhouse gas (carbon dioxide and methane, CO2 and CH4 respectively) production and emission (or in the case of CH4, perhaps taken up from) the atmosphere.All data are plain text CSV (comma separated value) files and require no special software to read.Updated 2025-10-09 to fix two missing dates (lines 77 and 78 in the data file).

54 ENVIRONMENTAL SCIENCES↗

Getting to the bottom of it: do laser-driven coils really work? (Final Technical Report)

Since claims of ultra-high (up to ~ 1.5 kiloTesla) magnetic fields appeared in the scientific literature, it became necessary to conduct controlled experiments to determine the validity of these claims and assess the real capability of laser-driven coils (LDCs) to produce high B-field to be used in HEDP (high energy density physics) experiments. This project assessed the viability of generating magnetic fields using LDCs. This was accomplished with a thorough analysis of several focused experiments on the OMEGA EP laser at LLE designed to measure the magnetic field inside LDCs. We demonstrated how problems with prior diagnostic analyses have led to overestimations of the magnetic fields generated from LDCs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗

Quench Propagation On Rare-Earth Barium Copper Oxide (REBCO) Tape

Rare-earth barium copper oxide (REBCO) high-temperature superconductors can carry high currents with zero electrical resistance, enabling applications in high-field magnets, energy systems, and fusion devices. However, localized losses of superconductivity, known as quenches, pose a severe risk due to REBCO’s mechanical brittleness and slow quench propagation, complicating early detection. This work investigates quench propagation and thermal response through controlled experiments. A segmented REBCO tape soldered onto a printed circuit board (PCB) with multiple voltage taps and temperature sensors was submerged in liquid nitrogen at 77 K. In the first test, current ramping produced a quench near 360 A, consistent with theoretical current limits, but resulted in local burnout. In the second test, a heater-induced quench at 300 A propagated bidirectionally with velocities ranging from 164–226 mm/s. These results provide critical parameters for improving REBCO quench detection and protection system design while accounting for REBCO’s limited bendability.

Ju, Han [Northern Illinois U.]↗

Quench Propagation on Rare-Earth Barium Copper Oxide (REBCO) Tape

Rare-earth barium copper oxide (REBCO) high-temperature superconductors can carry high currents with zero electrical resistance, enabling applications in high-field magnets, energy systems, and fusion devices. However, the localized losses of superconductivity called a quench pose a severe risk due to REBCO’s mechanical brittleness and slow quench propagation, hence complicating early detection. This work investigates quench propagation and thermal response through controlled experiments. A segmented REBCO tape soldered on a PCB with multiple voltage taps and temperature sensors was submerged in liquid nitrogen at 77 K. In the first test, current ramping produced a quench near 360 A, consistent with theoretical current limits but resulted in a local burn. In the second test, a heater-induced quench at 300 A propagated bidirectionally with measured velocities ranging from 164–226 mm/s. These measurements provide critical parameters for improving REBCO quench detection and protection system design with accounts for REBCO's limited bendability.

Ju, Han [NIU, DeKalb]↗

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗

Comparing Control Performance Between Simulation and Experiment using the Microreactor Automated Control System Testbed

In the advanced reactor domain, a flexible and scalable software/hardware infrastructure is crucial for integrating and validating various control technologies. This study used the Microreactor Automated Control System (MACS) hardware platform as a testbed. MACS was originally designed to mirror Idaho National Laboratory (INL)'s Microreactor Applications Research Validation and Evaluation (MARVEL), a 85-kW thermal fission microreactor. It features control drums for simulated reactivity control; lights that function as a surrogate reactor core, with the brightness being proportional to the reactor power; and light sensors that emulate neutron detectors. To transform MACS into a physical twin of MARVEL for evaluating control methods, the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software was employed. This software integrated the hardware with two models of the MARVEL core, based on Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The study aimed to demonstrate the gap between control theory and actual practice—a gap that often necessitates empirical adjustments such as control gain retuning, filters, time discretization, and integrator anti-windup measures. Controllers were developed based on increasingly complex simulations without hardware, starting from the base MARVEL model and then introducing actuator saturation constraints and sensor noise. The final control strategy was then tested using MACS, and a comparative performance analysis was conducted.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

Assessment of Consensus and Wake Steering Wind Farm Control for the American WAKE ExperimeNt (AWAKEN)

As part of the AmericanWAKE ExperimeNt (AWAKEN), a wind farm control experiment is being conducted at the King Plains wind plant in northern Oklahoma from May 2024 to summer 2025. Two types of wind farm control are being evaluated: 1) wake steering, in which upstream wind turbines are misaligned relative to the wind direction to deflect their wakes away from downstream turbines and increase total wind plant power, and 2) consensus yaw control, whereby each turbine's yaw position is controlled to track a "consensus" weighted average of the wind directions measured at neighboring turbines rather than the turbine's own nacelle wind direction measurement. By replacing the noisy wind direction measured by an individual turbine with the smoother, more slowly varying consensus wind direction, consensus yaw control is intended to reduce yaw activity and increase power capture by improving yaw alignment. To help balance the potential increase in yaw activity for the turbines implementing wake steering, they are also operated using consensus yaw control. In this presentation we highlight the impacts of consensus yaw control and wake steering on both energy production and yaw travel at the wind plant. Results show that the change in energy from wake steering is minor overall, but significant increases in energy are observed for closely spaced turbines. Further, larger increases in energy occur during low turbulence periods. The impact of consensus yaw control on energy production is currently inconclusive, with some energy gains measured for some turbines and losses measured for others. Lastly, consensus yaw control was found to reduce yaw travel significantly, even when combined with wake steering.

17 WIND ENERGY↗

Control Mechanisms for Self‐Sealing in Activated Clay‐Rich Faults Through Controlled Hydraulic Injection Experiment

Abstract In a high‐pressure injection fault activation experiment conducted at the Mont Terri underground research laboratory in Switzerland, the transmissivity of the Opalinus Clay fault significantly increased due to opening and shearing. The fluid injection, spanning a few hours, generated a 10 m radius fault activation patch. Subsequent pressure pulse tests conducted bi‐weekly for a year revealed the gradual return of fault transmissivity to its initial state. The study utilized fluid pressure decay analysis, optical fiber monitoring, continuous active source seismic measurements and borehole displacement sensors for measuring fault displacements. The fault zone exhibited a dilation of approximately 1.4 mm, associated with both normal and tangential movements during activation, resulting in a sudden transmissivity increase from 1 × 10 −12 to 3.2 × 10 −7 m 2 /s. Early post‐activation, transient compaction and the subsequent slow compaction were observed, transitioning to an extension regime. The pressure pulse tests demonstrated a rapid transmissivity drop by more than two orders of magnitude within the first 10 days, followed by a gradual and less pronounced decrease. Plastic shear and compaction dominated the transmissivity evolution until 70 days after injection ended, followed by a period where additional factors, such as clay mineral swelling, influenced the behavior. Extrapolation suggested a sealing process taking at least 50 years after the initial activation. Plain Language Summary A field‐scale fault activation experiment offers valuable insights into the elasto‐plastic processes governing the sealing of shale faults. The experiment reveals a rapid increase in the fault's transmissivity by approximately five orders of magnitude during activation. Subsequent observations show a gradual transmissivity decrease by about three orders of magnitude post‐activation, with slow long‐term plastic shear and compaction of the fault competing against secondary processes, notably clay mineral swelling. All conceptual models employed to interpret these field data converge on the estimation that the fault's return to its initial low transmissivity state would require a minimum of 50 years. Key Points High‐pressure injection fault activation experiment at the Mont Terri underground research laboratory Continuous transmissivity measurements record self‐sealing inside a clay‐rich fault zone Transmissivity undergoes a phase of domination by slow plastic compaction and shearing during the initial post‐activation period, with mineral swelling exerting its influence over the long term

Guglielmi, Yves↗

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗