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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Detectability prediction for a thermoacoustic sensor in the breazeale nuclear reactor pool

Laboratory experiments have suggested that thermoacoustic engines can be in- corporated within nuclear fuel rods. Such engines would radiate sounds that could be used to measure and acoustically-telemeter information about the op- eration of the nuclear reactor (e.g., coolant temperature or uxes of neutrons or other energetic particles) or the physical condition of the nuclear fuel itself (e.g., changes in temperature, evolved gases) that are encoded as the frequency and/or amplitude of the radiated sound [IEEE Measurement and Instrumen- tation 16(3), 18-25 (2013)]. For such acoustic information to be detectable, it is important to characterize the vibroacoustical environments within reactors. Measurements will be presented of the background noise spectra (with and with- out coolant pumps) and reverberation times within the 70,000 gallon pool that cools and shields the fuel in the 1 MW research reactor on Penn State's campus using two hydrophones, a piezoelectric projector, and an accelerometer. Sev- eral signal-processing techniques will be demonstrated to enhance the measured results. Background vibrational measurement were also taken at the 250 MW Advanced Test Reactor, located at the Idaho National Laboratory, using ac- celerometers mounted outside the reactor's pressure vessel and on plumbing will also be presented. The detectability predictions made in the thesis were validated in September 2015 using a nuclear ssion-heated thermoacoustic sensor that was placed in the core of the Breazeale Nuclear Reactor on Penn State's campus. Some features of the thermoacoustic device used in that experiment will also be revealed. [Work supported by the U.S. Department of Energy.]

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) – Performance Characterization

Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗
DOE OSTI2017

Qualified display system architecture - 246

There is a need in the nuclear industry for a Qualified Display System (QDS) architecture as described in this paper. The function of the QDS is to process and display Regulatory Guide 1.97 post accident monitoring parameters. The QDS is implemented on the Lockheed Martin Field-Programmable Gate Array (FPGA) based Nuclear Protection and Control (NuPAC) digital platform, coupled with a Qualified Display Controller (QDC) and a Safety Display Unit (SDU). The QDS is designed to meet the requirements of IEEE 603-1991 (endorsed by Regulatory Guide 1.153, Rev. 1), IEEE 497-2002 (endorsed by Regulatory Guide 1.97, Rev. 4), and IEEE 7-4.3.2- 2003 (endorsed by Regulatory Guide 1.152, Rev. 3). The QDS is designed with flexibility such that it can be implemented in an advanced reactor design, Small Modular Reactor (SMR) with any cooling medium, or digital upgrade project for an operating nuclear plant. The QDS has four independent and redundant divisions. Each division has five layers in the architecture: Process variable input signals; Process Protection Logic (PPL); Algorithm Logic (AL), Interface Panel (IP); and Safety Display Unit (SDU). The QDS meets the following design criteria: redundancy, independence, predictability and repeatability, and diversity capability. The QDS architecture also meets the simplicity design principle. The QDS architecture design provides flexibility in the implementation in a plant. The QDS can be implemented in the design of a new advanced plant or as a digital upgrade in an operating plant. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2014

Note: On-line weak signal detection via adaptive stochastic resonance

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Constraining microphysical processes of warm rain formulation using advanced spectral separations, an ensemble retrieval framework and machine learning techniques

Drizzle, a common feature of marine boundary layer clouds formed through collision coalescence, plays a key role in cloud microphysics and evolution. Yet, simultaneously retrieving cloud and drizzle properties from remote-sensing observations remains challenging because drizzle droplets often dominate radar signals, masking cloud contributions. The goal of the proposed research is to provide constraints for the process of autoconversion and accretion using ARM cloud measurements. Specifically, we provide concurrent retrievals of cloud and drizzle that allows users to derive corresponding autoconversion and accretion rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Acoustic sensors for fission gas characterization: R and D skills devoted to innovative instrumentation in MTR, non-destructive devices in hot lab facilities and specific transducers for measurements of LWR rods in nuclear plants

First of all, we will present the main principle of the method. A piezoelectric transducer, driven by a pulse generator, generates the acoustic waves in a cavity that may be the fuel rod or a chamber connected to an instrumented rod. The composition determination consists in measuring the time of flight of the acoustic signal emitted. The pressure can be estimated by a calibration process, above the measurement of the amplitude of the signal. Two projects will then be detailed. The first project consists in the development of advanced instrumentation for in-pile experiments in Material Testing Reactor. It constitutes a main goal for the improvement of the nuclear fuel behavior knowledge. This acoustic method was tested with success during a first experiment called REMORA 3, and the results were used to differentiate helium and fission gas release kinetics under transient operating conditions. This experiment was lead at OSIRIS reactor (CEA Saclay, France). As a first step of the development program, we performed in-pile tests on the most sensitive component, i.e., the piezoelectric transducer. For this purpose, the active part of this sensor has been qualified on gamma and neutron radiations and at high temperature. Various industrial piezo-ceramics were exposed to a high activity Cobalt source for few days. The cumulated dose was ranged from 50 kGy up to 2 MGy. Next, these devices were placed inside a Material Test Reactor to investigate their reliability towards neutron fluence. The final fluence after 150 days of irradiation was up to 1.6.10{sup 21}n/cm{sup 2} (for thermal neutron). Irreversible variations have been measured. Next, a specific sensor has been implemented on an instrumented fuel rod and tested in the frame of a REMORA 3 Irradiation test. It was the first experiment under high mixed, temperature neutron and gamma flux. A first irradiation phase took place in March 2010 in the OSIRIS reactor and in November 2010 for the second step of the irradiation. The instrumented fuel rod incorporating the ultrasonic gas composition sensor was finally irradiated during 2 weeks in nominal conditions. Neutronics calculation will be performed in order to calculate the thermal and fast neutron fluence and the gamma dose absorbed by acoustic sensor. A first evaluation gives a thermal fluence about 4,5.10{sup 19} n/cm{sup 2}, a fast neutrons fluence about 4,5.1018 n/cm{sup 2} and a total gamma dose up to 0,25 MGy The maximal temperature during the irradiation test was about 150 C. Although the ultrasonic sensor appears to be damaged, the optimization of the electrical attack parameters and the development of a new signal processing maintain the measurement feasibility up the end of the irradiation campaign. It was the first time that the composition of fission gas has been monitored all along an irradiation experiment in a MTR, giving access to the gas release kinetics. New researches involve thick film transducers produced by screen-printing process in order to propose piezoelectric structures for harsh temperature and irradiation measurements. The second project consists in the development of a non-destructive device that can be directly applied on a LWR fuel rod. The problem to be solved relates to the measurement of the fission gas pressure and composition in a fuel rod using a non-destructive method. Fuel rod internal pressure is one of the safety criteria applied in nuclear power analyses. This criterion must be verified in order to avoid any fuel-cladding gap reopening risk and therefore any local clad ballooning. Apart from the safety implications, this parameter is also a fuel behaviour indicator and reflects the overall fuel performance in operation, but also during shipping and long-term storage. Rod internal pressure is one criterion amongst others, like cladding corrosion, against which the acceptable fuel burn-up limit is set. A sensor has been achieved in 2007. A full-scale hot cell test of the internal gas pressure and composition measurement by an acoustic sensor was conducted successfully between 2008 and 2010 on 5 high burn-up MOX fuel rods and 2 very high burn-up UO{sub 2} fuel rods in LECA Facility at Cadarache Centre. An improvement of this sensor has been proposed, allowing us to divide by two the uncertainty on the pressure measurement. In the case of hot-cell measurements, viscous liquid can be used to couple the sensor with the rod. For gas content with a pressure exceeding 15 bars and a 10% Xe/Kr ratio, such coupling may reduce relative acoustic method accuracy by ±7% for pressure measurement result and ±0.25 % for the assessment of gas composition. These results make it possible to demonstrate the feasibility of the technique on LWR fuel rods. The transducer and the associated methodology are now operational for non-destructive measurements in hot lab facilities and allow characterising the fission gas without puncturing the fuel rods. Up to now, any other non-destructive method can be proposed. A next step will be the development of an industrial application in a fuel storage pool in order to perform a large number of measurements on a fuel assembly in nuclear plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Reconstruction and Analysis for the DUNE 35-ton Liquid Argon Prototype

Neutrino physics is approaching the precision era, with current and future experiments aiming to perform highly accurate measurements of the parameters which govern the phenomenon of neutrino oscillations. The ultimate ambition with these results is to search for evidence of CP-violation in the lepton sector, currently hinted at in the world-leading analyses from present experiments, which may explain the dominance of matter over antimatter in the Universe. The Deep Underground Neutrino Experiment (DUNE) is a future long-baseline experiment based at Fermi National Accelerator Laboratory (FNAL), with a far detector at the Sanford Underground Research Facility (SURF) and a baseline of 1300 km. In order to make the required precision measurements, the far detector will consist of 40 kton liquid argon and an embedded time projection chamber. This promising technology is still in development and, since each detector module is around a factor 15 larger than any previous experiment employing this design, prototyping the detector and design choices is critical to the success of the experiment. The 35-ton experiment was constructed for this purpose and will be described in detail in this thesis. The outcomes of the 35-ton prototype are already influencing DUNE and, following the successes and lessons learned from the experiment, confidence can be taken forward to the next stage of the DUNE programme. The main oscillation signal at DUNE will be electron neutrino appearance from the muon neutrino beam. High-precision studies of these νe interactions requires advanced processing and event reconstruction techniques, particularly in the handling of showering particles such as electrons and photons. Novel methods developed for the purposes of shower reconstruction in liquid argon are presented with an aim to successfully develop a selection to use in a νe charged-current analysis, and a first-generation selection using the new techniques is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Quantitative proteomic characterization of redox-dependent post-translational modifications on protein cysteines

Protein cysteine thiols play a crucial role in redox signaling, regulation of enzymatic activity and protein function, and maintaining redox homeostasis in living systems. The unique chemical reactivity of thiol groups makes cysteine susceptible to oxidative modifications by reactive oxygen and nitrogen species to form a broad array of reversible and irreversible protein post-translational modifications (PTMs). The reversible modifications in particular are one of the major components of redox signaling and are involved in regulation of various cellular processes under physiological and pathological conditions. The biological significance of these redox PTMs in health and diseases has been increasingly recognized. Herein, we review the recent advances of quantitative proteomic approaches for investigating redox PTMs in complex biological systems, including the general considerations of sample processing, various chemical or affinity enrichment strategies, and quantitative approaches. We also highlight a number of redox proteomic approaches that enable effective profiling of redox PTMs for addressing specific biological questions. Although some technological limitations remain, redox proteomics is paving the way towards a better understanding of redox signaling and regulation in human health and diseases.

Cysteine↗
DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2024

Dynamic Modeling and Simulation of a Subcritical Coal-Fired Power Plant under Load-Following Conditions

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long-time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Interplay between Ultrafast Electronic and Librational Dynamics in Liquid Nitrobenzene Probed with Two-Color Four-Wave Mixing

Here, we present an experimental and theoretical study of the interplay between ultrafast electron dynamics and librational dynamics in liquid nitrobenzene. A femtosecond ultraviolet pulse and two femtosecond near-infrared pulses interact with nitrobenzene molecules, generating a four-wave mixing nonlinear signal measured in the Optical Kerr Effect geometry. The signal is measured to be nonzero only at negative time delays, corresponding to the near-infrared pulses arriving before the ultraviolet pulse. We perform time-dependent Quantum Master Equation calculations with classical libration to simulate the experiment. The simulations support the conclusion that the near-infrared pulses launch librational motion while creating electronic coherences resulting in a libration-modulated electronic nonlinear response. The analysis of the phase-matched four-wave mixing signals suggests a nonparametric process leaving the molecules in an excited electronic state, providing new insight into ultrafast nonlinear optical interactions in liquids and advancing toward probing ultrafast electronic coherences in complex molecular liquids.

Shivaram, Niranjan [Lawrence Berkeley National Lab↗
DOE OSTI2018

Sensitivity of Mountain Hydroclimate Simulations in Variable-Resolution CESM to Microphysics and Horizontal Resolution

Mountains are natural dams that impede atmospheric moisture transport and water towers that cool, condense, and store precipitation. They are essential in the western United States where precipitation is seasonal, and snowpack is needed to meet water demand. With anthropogenic climate change increasingly threatening mountain snowpack, there is a pressing need to better understand the driving climatological processes. However, the coarse resolution typical of modern global climate models renders them largely insufficient for this task, and signals a need for an advanced strategy. This paper continues the assessment of variable-resolution in the Community Earth System Model (VR-CESM) in modeling mountain hydroclimatology to understand the role of grid-spacing at 55, 28, 14, and 7 km and microphysics, specifically the Morrison and Gettelman (2008, MG1, https://doi.org/10.1175/2008JCLI2105.1) scheme versus the Gettelman and Morrison (2015, MG2, https://doi.org/10.1175/JCLI-D-14-00102.1) scheme. Eight VR-CESM simulations were performed from 1999 to 2015 with the F_AMIP_CAM5 component set, which couples the atmosphere-land models and prescribes ocean data. Refining horizontal grid-spacing from 28 to 7 km with the MG1 scheme did not improve the simulated mountain hydroclimatology. Substantial improvements occurred with the use of MG2 at grid-spacings ≤28 km compared to MG1 as shown with subsequent statistics. Average SWE bias diminished by 9.4X, 4.9X, and 3.5X from 55 to 7 km. The range in minimum (maximum) DJF spatial correlations increased by 0.1–0.2 in both precipitation and SWE. Mountain windward/leeward distributions and elevation profiles improved across hydroclimate variables, however not always with model resolution alone. Disconcertingly, all VR-CESM simulations exhibited a systemic mountain cold bias that worsened with elevation and will require further examination.

58 GEOSCIENCES↗
DOE OSTI2024

Scintillation and cherenkov photon counting detectors with analog silicon photomultipliers for TOF-PET

Objective. Standard signal processing approaches for scintillation detectors in positron emission tomography (PET) derive accurate estimates for 511 keV photon time of interaction and energy imparted to the detection media from aggregate characteristics of electronic pulse shapes. The ultimate realization of a scintillation detector for PET is one that provides a unique timestamp and position for each detected scintillation photon. Detectors with these capabilities enable advanced concepts for three-dimensional (3D) position and time of interaction estimation with methods that exploit the spatiotemporal arrival time kinetics of individual scintillation photons. Approach. In this work, we show that taking into consideration the temporal photon emission density of a scintillator, the channel density of an analog silicon photomultiplier (SiPM) array, and employing fast electronic readout with digital signal processing, a detector that counts and timestamps scintillation photons can be realized. To demonstrate this approach, a prototype detector was constructed, comprising multichannel electronic readout for a bismuth germanate (BGO) scintillator coupled to an SiPM array. Main Results. In proof-of-concept measurements with this detector, we were able to count and provide unique timestamps for 66% of all optical photons, where the remaining 34% (two-or-more-photon pulses) are also independently counted, but each photon bunch shares a common timestamp. We show this detector concept can implement 3D positioning of 511 keV photon interactions and thereby enable corrections for time of interaction estimators. The detector achieved 17.6% energy resolution at 511 keV and 237 ± 10 ps full-width-at-half-maximum coincidence time resolution (CTR) (fast spectral component) versus a reference detector. We outline the methodology, readout, and approach for achieving this detector capability in first-ever, proof-of-concept measurements for scintillation photon counting detector with analog silicon photomultipliers. Significance. The presented detector concept is a promising design for large area, high sensitivity TOF-PET detector modules that can implement advanced event positioning and time of interaction estimators, which could push state-of-the-art performance.

62 RADIOLOGY AND NUCLEAR MEDICINE↗