Smart Self-Assembly Amphiphilic Cyclopeptide-Dye for Near-Infrared Window-II Imaging
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Chemistry↗
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Results for “direct detection”
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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Genomic variation within the maize stiff-stalk heterotic germplasm pool
The stiff-stalk heterotic group in Maize (Zea mays L.) is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and, to a lesser extent, B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four expired Plant Variety Protection (ex-PVP) lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40, which is a Pioneer Hi-Bred International (PHI) early stiff-stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13-2.18 Gbp with N50 scaffold lengths >200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas, whereas transposable element (TE) annotation was conducted using de novo and homology-directed methodologies. Compared with the reference inbred B73, synteny analyses revealed extensive collinearity across the five stiff-stalk genomes, although unique components of the maize pangenome were detected. Comparison of this set of stiff-stalk inbreds with the original Iowa Stiff Stalk Synthetic breeding population revealed that these inbreds represent only a proportion of variation in the original stiff-stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original stiff-stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent stiff-stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pangenome.
59 BASIC BIOLOGICAL SCIENCES↗
Leak detection in a subcritical boiler
Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.
42 ENGINEERING↗
Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains
We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.
54 ENVIRONMENTAL SCIENCES↗
Demonstration of the neutron tracking capability of NEXT array in time-of-flight measurements to improve energy resolution
Precise neutron-energy measurements are required to probe the nuclear structure effects of neutron-rich nuclei, where β-delayed neutron emission becomes a dominant decay mode. The Neutron dEtector with Xn Tracking (NEXT) array has been designed and constructed to measure β-delayed neutrons with better energy resolution. The new design localizes the neutron interaction position by optically segmenting the detector along the direction of the neutron flight path, reducing the associated uncertainties in the neutron time-of-flight measurements. This significantly improves the energy resolution without losing the necessary detection efficiency. The proof-of-principle and efficiency measurements showed promising results. Herein, this article details the implementation of the neutron tracking capability of NEXT array in time-of-flight measurements.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Observation of Radar Echoes from High-Energy Particle Cascades
We report here the observation of radar echoes from the ionization trails of high-energy particle cascades. Data were taken at the SLAC National Accelerator Laboratory, where the full electron beam ( ~ 10 9 e - at ~ 10 GeV / e - ) was directed into a plastic target to simulate an ultrahigh-energy neutrino interaction. The target was interrogated with radio waves, and coherent radio reflections from the cascades were detected with properties consistent with theoretical expectations. This is the first definitive observation of radar echoes from high-energy particle cascades, which could lead to a viable neutrino detection technology for energies ≳ 10 16 eV .
43 PARTICLE ACCELERATORS↗
Live-Cell Quantum Dot-Based Tracking of Plant & Microbial Extracellular Vesicles
All cellular organisms examined to date release various types of small vesicles called extracellular vesicles (EVs). They were originally thought to be unimportant, but research into EVs as carriers of key messages for communication within large, multicellular organisms or between interacting organisms has gained interest in many different scientific areas of study. Recently, it was discovered that both plants and their pathogenic fungi generate EVs and they are important vehicles for sending plant signals to thwart invasion by fungal pathogens, and in response, fungi use EVs to help evade plant defenses. This project aimed to develop new ways to visualize EVs and their potential nucleic acid cargo isolated from the crop plant, sorghum. We adapted a click chemistry-based method for making quantum dot-based RNA sensors, which will be a useful tool for others studying small RNAs. These sensors were not used detect RNA in sorghum EVs because unlike EVs from Arabidopsis, sorghum EVs were not enriched for specific small RNAs. This interesting difference shifted the project in an exciting direction, which focused on comparing EV proteomes. Our study discovered that greater than 60% of Arabidopsis EV proteins were also found in the sorghum EV proteome, revealing a partial conservation between a eudicot and monocot plant. These studies have raised new, fundamental questions on the conserved function of EVs in plants.
59 BASIC BIOLOGICAL SCIENCES↗
Calorimetric Identification of Composite Nuclei in LAr
In neutrino-nucleus interactions, Final State Interactions (FSI) can cause nucleons to scatter or form heavier composite particles like deuterons and tritons. Observing these particles would provide direct evidence of FSI and help improve interaction models, which is essential for precise neutrino energy reconstruction in future experiments like DUNE. Our Goal: Develop an analysis to detect particles heavier than protons in ICARUS data to better constrain FSI models.
McCright, Hannah [Maryland U., College Park]↗
High-Speed and High-Power Ferroelectric Switching Current Measurement Instrument for Materials with Large Coercive Voltage and Remanent Polarization
A high-speed and high-power current measurement instrument is described for measuring rapid switching of ferroelectric samples with large spontaneous polarization and coercive field. Instrument capabilities (±200 V, 200 mA, and 200 ns order response) are validated with a LiTaO 3 single crystal whose switching kinetics are well known. The new instrument described here enables measurements that are not possible using existing commercial measurement systems, including the observation of ferroelectric switching in large coercive field and large spontaneous polarization Al 0.7 Sc 0.3 N thin films.
30 DIRECT ENERGY CONVERSION↗
A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz
We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the $L$-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9' beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake figure of merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is continuously transmitting a narrowband signal with an equivalent isotropic radiated power (EIRP) > 10 13 W. For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 10 16 W) is at most 3 × 10 -4 . Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al.
79 ASTRONOMY AND ASTROPHYSICS↗
AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources
This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.
20 FOSSIL-FUELED POWER PLANTS↗
Direct isotopic analysis of solid uranium particulates on cotton swipes by microextraction-ICP-MS
Direct isotope ratio analysis of solid uranium particulates on cotton swipes was achieved using a solution-based microextraction technique, coupled to a quadrupole inductively coupled plasma - mass spectrometer (ICP-MS). This microextraction-ICP-MS methodology provides rapid isotopic analysis which could be applicable to nuclear safeguards measurements. Particulates of uranyl nitrate hexahydrate (UO 2 (NO 3 ) 2 ·6H 2 O) and uranyl fluoride (UO 2 F 2 ) ranging from 6 μm to 40 μm in length were transferred to cotton swipes with a particle manipulator. The microextraction probe then delivers a 5% nitric acid (HNO3) solvent onto the swipe surface to extract the uranium species. The extracted sample is then delivered to the ICP-MS for isotopic determination. The majority of uranium signal (~99% and ~94% for UO2(NO3)2·6H2O and UO 2 F 2 , respectively) was detected in the first 15 s extraction, while subsequent extractions on the same location had low or no U signal, suggesting near complete removal of the solid uranium compounds from the swipe surface. Ten samples (for each of the uranium compounds), were analyzed for their isotopic composition. For UO 2 (NO 3 ) 2 ·6H 2 O, the determined isotope ratios resulted in a % relative difference (% RD) from the referenced isotope ratios of 0.97, 1.0, and 7.3% for 234 U/ 238 U, 235 U/ 238 U, and 236 U/ 238 U, respectively. The % RD of the UO 2 F 2 isotope ratios were 1.9 and 0.60% for 234 U/ 238 U and 235 U/ 238 U, respectively. The preliminary limits of detection were determined to be 0.002, 0.4, and 60 pg for 234 U, 235 U and 238 U, respectively This work demonstrates that microextraction ICP-MS is a rapid and sensitive method that could directly determine uranium isotope ratios of UO 2 (NO 3 ) 2 ·6H 2 O and UO 2 F 2 particulates on cotton swipes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Process Failure Detection via Recurrence Quantification Analysis in a Slot-Rectangular Spouted Bed
A study was conducted to explore the applicability of recurrence and recurrence quantification analysis (RQA) to the detection of process failures in spouted bed reactor systems. Here, three different potential failure modes were examined in a transparent, cold flow slot-rectangular spouted bed. These being, a simulated air leak from a side wall of the reactor, a simulated gas leak from the top wall of the reactor, and simulated agglomeration of solids via introduction of larger “klinker” particles. Bed pressure drop time history data were collected and analyzed via generation of recurrence plots (RPs) and RQA parameters. In general, the simulated agglomeration case was quite easily detected via ever RQA Parameter examined, whereas the simulated air leaks were detected by only a single RQA parameter.
30 DIRECT ENERGY CONVERSION↗
Estimating the State of Charge in Lithium Primary Batteries: Recent Advances and Critical Insights
Lithium primary batteries (LPBs) remain essential in critical applications such as military, aerospace, medical and emergency devices, and portable electronics. Their superior energy density over lithium-ion batteries offers a significant advantage for long-duration use. Therefore, accurate estimation of the state of charge (SoC) is essential for ensuring the reliable and safe operation of these batteries. While extensive research has been conducted on SoC estimation techniques for lithium-ion secondary batteries, LPBs present unique challenges that complicate accurate SoC estimation. Moreover, research on nondestructive testing techniques for SoC estimation in LPBs is significantly lacking. In this review article, it is aimed to provide a comprehensive overview of recent advancements in SoC estimation for LPBs and generates new insights and directions for future research. Herein, existing methods are discussed and their effectiveness and mechanisms are identified, and areas for further optimization are outlined. More theoretical/experimental efforts to advance SoC detection in LPBs is recommended due to challenges identified with existing techniques.
25 ENERGY STORAGE↗
Machine learning-guided discovery of gas evolving electrode bubble inactivation
The adverse effects of electrochemical bubbles on the performance of gas-evolving electrodes are well known, but studies on the degree of adhered bubble-caused inactivation, and how inactivation changes during bubble evolution are limited. We study electrode inactivation caused by oxygen evolution while using surface engineering to control bubble formation. We find that the inactivation of the entire projected area, as is currently believed, is a poor approximation which leads to non-physical results. Using a machine learning-based image-based bubble detection method to analyze large quantities of experimental data, we show that bubble impacts are small for surface engineered electrodes which promote high bubble projected areas while maintaining low direct bubble contact. We thus propose a simple methodology for more accurately estimating the true extent of bubble inactivation, which is closer to the area which is directly in contact with the bubbles.
Lake, Jack R.↗
The distinctive mechanical and structural signatures of residual force enhancement in myofibers
In muscle, titin proteins connect myofilaments together and are thought to be critical for contraction, especially during residual force enhancement (RFE) when steady-state force is elevated after an active stretch. We investigated titin’s function during contraction using small-angle X-ray diffraction to track structural changes before and after 50% titin cleavage and in the RFE-deficient,mdmtitin mutant. We report that the RFE state is structurally distinct from pure isometric contractions, with increased thick filament strain and decreased lattice spacing, most likely caused by elevated titin-based forces. Furthermore, no RFE structural state was detected inmdmmuscle. We posit that decreased lattice spacing, increased thick filament stiffness, and increased non-cross-bridge forces are the major contributors to RFE. We conclude that titin directly contributes to RFE.
Science & Technology - Other Topics↗
Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool
An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.
47 OTHER INSTRUMENTATION↗
Feasibility of Lead Fast Reactor Heat Exchanger Tube Online Monitoring
Within virtually all reactor systems with heat exchangers, the thinnest barriers between primary and secondary fluids are the heat exchanger tubes. Consistent with light water reactor experience, the advanced reactor heat exchangers are the most susceptible regions for corrosion and leakage (NRC, 2019). Moreover, traditional inspections and repairs using robotics will be expensive or impractical. On-line structural health corrosion monitoring (OLSHM) is therefore desirable to detect tube degradation prior to leaks that may allow mixing of heat exchanger fluids. Rhetorically many molten salt reactor (MSR) designers plan for on-line monitoring of critical reactor components including heat exchangers but there are no proven OLSHM sensors or systems for MSR heat exchangers. Guided wave ultrasound from the tube-end or ID using conventional low-temperature piezoelectric sensors has been shown to be sensitive to pits and cracks less than 50% through-wall in long pipes and tubes (Narayanan M.M., 2019) . Mode and frequency selection are important for practical sensor design decisions and ultimate performance. The L-0 wave-mode is easily excited by L-mode transducers but they have surface normal wave motion that will be sensitive to and more attenuative because of fluid interaction with the tube surface (Rose J.L., 1994). Shear Horizontal (SH) wave modes have material motion parallel to the tube surface therefore are virtually unaffected by fluid interaction. For SH-0 to be nondispersive in steel, the frequency-thickness product (MHz-mm) must be less than approximately 1.5. SH mode transducers must be coupled by a material that can support shear stress; a thin layer of honey works well for temporary sensors and an adhesive or a brazed metallurgical bond for permanent sensors. If sensors are incorporated into the fabrication process, the same kinds of SH-0 waves can be generated from the stagnant flow area of the tube OD without compromising the heat-exchange tube function. Piezoelectric materials are also available that can withstand harsh temperatures of molten salt heat exchangers (Stevenson T., 2015) (Machura et al., 2008). Though they may not have as high-performance characteristics, their performance only needs to be good enough to detect flaws of interest. This project attempts to demonstrate feasibility to develop a sensor that can be sensitive to flaws of interest and perform long-term monitoring of a molten salt reactor heat exchanger. Benefits to the advanced reactor system include: • Minimizing the expense of a reactor shut down for periodic inspection—only shutdown for cause. • Reduce risk of unexpected tube rupture by detecting flaws before they grow to through-wall leaks. • Cost reduction by minimizing need to design for manual/robotic inspectability. Transducer design, characterization of the beam profile, wedges vs. direct application of piezoelectric sensors, and ultrasonic pulse-echo responses from a representative heat exchange tube with fabricated flaw types are discussed.
On-Line Tube Monitor, Guided Wave Ultrasound, SH-0↗