BIO-ORGANISM DETECTION USING MICROSPHERE RESONATORS IN FLUIDICS
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Thermal barrier coatings (TBCs) are widely used in the hot sections of gas turbine systems as they are remarkably efficient in insulating the underlying superalloys, leading to higher operating temperatures and therefore enhancing the performance of the engines. However, this benefit is only possible if the integrity of the TBC under aggressive thermo-mechanical environments is ensured. Delamination is a common but hard to detect failure mode. We present, in this work, supported by experimentation, the implementation of a modeling approach applying the Kubelka-Munk theory to provide numerical quantification of luminescence contrast and intensity due to top coat delamination in TBCs. The method relies on the drastic change in reflectivity when a delamination forms, exploiting it for high-contrast luminescence mapping. Two distinct TBC configurations containing an erbium-doped yttria-stabilized zirconia (YSZ:Er 3+ ) layer for delamination sensing were used to validate this model. A delamination zone induced by Rockwell indentation was successfully tracked by measuring an increase of the intensity of the erbium emission line at 562 nm. Luminescence-based methods for delamination detection can provide a revolutionary non-invasive technique, with potential for both off-line and on-line engine monitoring.
Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.
We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981–2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory.
The intestinal epithelium is replaced weekly by non-quiescent stem cells with kinetics that rely on a rapid loss of stemness and choice for secretory or absorptive lineage differentiation. To determine how the cellular transcriptome and proteome changes during these transitions, we developed a new cell sorting method to purify stem cells, secretory and absorptive progenitor cells, and mature, differentiated cells. Transcriptome analyses revealed that as stem cells transition to the progenitor stage, alternative mRNA splicing and polyadenylation dominate changes in the transcriptome. In contrast, as progenitors differentiate into mature cell types, alterations in gene expression and mRNA levels drive the changes. RNA processing targets mRNAs encoding regulators of cell cycle, RNA regulators, cell adhesion, SUMOylation, and Wnt and Notch signaling. Additionally, global proteome profiling of sorted cell populations detected >2,800 proteins and revealed RNA:protein patterns of abundance and correlation. Paired together, these data highlight new potentials for autocrine and feedback regulation and provide new insights into cell state transitions in the crypt.
Binary neutron star merger (BNSM) ejecta are considered a primary repository of r-process nucleosynthesis and a source of the observed heavy-element abundances. We implement composition mixing into ray-by-ray radiation-hydrodynamic simulations of BNSM ejecta, coupled with an online nuclear network (NN). We model mixing via a gradient-based mixing approximation that evolves simultaneously with the hydrodynamics. Here, we find that mixing occurs in regions where the electron fraction changes rapidly. While mixing smooths composition gradients in transition regions, it has a negligible impact on the heavy-element yields. This is because the primary r-process site (the equatorial ejecta) is initially homogeneous in free neutrons, leaving no strong gradients for mixing to act upon. In each angular ray, the abundances of the most produced elements are robust under mixing, while the less abundant ones are more affected. The total global abundances change only slightly from mixing, since each angular ray contributes its most abundant elements. Furthermore, the predicted kilonova light curves show only minor reddening, with differences below the detectability of state-of-the-art telescopes. In general, we do not observe significant effects from mixing in the time span of the r-process. Consequently, mixing only leads to minor variations in abundances and light curves in ray-by-ray simulations.
Next-generation nuclear power reactors based on liquid sodium coolant are in development to improve efficiency and safety. Fast breeder reactors could potentially achieve considerably higher fuel burn-up while producing less nuclear waste compared with current commercial technology. Monitoring of impurities in these types of reactors is important in order to prevent blockage of the cooling system or hazardous reactions due to the high reactivity of sodium with water. In this work, laser-induced breakdown spectroscopy is performed on a liquid sodium sample using a custom-built immersion probe system. The probe provides direct line of sight to the sample vessel in argon atmosphere for laser delivery and signal collection. Signatures of sodium, and impurities such as potassium and oxygen have been obtained. The probe height and immersion have a strong influence on the signal level and stability. Argon flow rates of 2 to 5 liters per minute produce no substantial change in signal, suggesting a steady laminar flowing regime. A high signal-to-noise ratio for impurity elements confirms the suitability of this technique for reliable impurity detection and monitoring in the reactor environment.
Improvements in survival rates with gonad-sparing protocols for childhood and adolescence cancer have increased the optimism of survivors to become parents after treatment. Findings in rodents indicate that chromosomal aberrations can be induced in male germ cells by genotoxic exposures and transmitted to offspring and future generations with effects on development, fertility and health. Thus, there is a need for effective technologies to identify human sperm carrying chromosomal aberrations to assess the germ-line risks, especially for cancer survivors who have received genotoxic therapies. The time-dependent changes in the burden of sperm carrying structural chromosomal aberrations were assessed for the first time in a cancer setting, using the AM8 sperm FISH protocol which simultaneously detects abnormalities in chromosomal structure and number in sperm. Nine Hodgkin lymphoma (HL) patients provided 20 semen samples before, during, and after NOVP therapy (Novantrone, Oncovin, Velban and Prednisone) and radiation therapy that produced scattered gonadal doses from <0.05 to 0.6 Gy. Late meiosis was found to be the most sensitive to NOVP treatment for the production of sperm with chromosomal abnormalities, both in structure and number. Earlier stages of spermatogenesis were less sensitive and there was no evidence that therapy-exposed stem cells resulted in increased frequencies of sperm with abnormalities in chromosomal structure or number. This indicates that NOVP therapy may increase the risks for paternal transmission of chromosomal structural aberrations for sperm produced 32 to 45 days after a treatment with these drugs and implies that there are no excess risks for pregnancies conceived more than 6 months after this therapy. This clinical evaluation of the AM8 sperm FISH protocol indicates that it is a promising tool for assessing an individual’s burden of sperm carrying chromosomal structural aberrations as well as aneuploidies after cancer therapy, with broad applications in other clinical and environmental situations that may pose aneugenic or clastogenic risks to human spermatogenesis.
Prompt gamma (PG) analysis is a nondestructive, nuclear, elemental analysis technique that uses charged particle reactions to interrogate a sample, and elements present in the sample matrix are identified through the characteristic gamma-rays emitted from the product nuclei in alpha-p and alpha-n nuclear reactions. This technique has been applied to plutonium oxide packaged in over 4,000 individual 3013 containers, and the concentrations of certain light elements were determined from the integrated peak areas based on a calibration that was published previously. This report provides the results for a new population of 3013 containers packaged with oxide materials produced by the conversion of metal by Advanced Recovery and Integrated Extraction System (ARIES) project using the direct metal oxidation (DMO) process and muffle furnaces. New PG and analytical chemistry data collected since 2015 were added to the existing calibration data sets to refine the calibration parameters. Calibration equations were also developed for determining beryllium and fluorine at low concentrations in high-purity ARIES product oxides. The new fluorine calibration provides an order of magnitude greater sensitivity and results in an additional 1,408 containers in the original population identified as having fluorine as an impurity. Additionally, equations for calculating the lower limits of detection (LLDs) as a function of the actual counting time (live time) were obtained using WLS regression technique for samples in the calibration data set. This resulted in changes to the LLDs published previously.
A cybersecurity distributed ledger is provided herein for managing, tracking, auditing, and securing assets in a power infrastructure. The cybersecurity distributed ledger may include a blockchain, and may combine with smart contract or smart negotiation technology, such as in a permissioned proof of authority blockchain. The cybersecurity distributed ledger may manage the complete life cycle of a grid asset, from asset requirement and specification, through production, testing, deployment, maintenance, and retirement. The cybersecurity distributed ledger may create an immutable record of the grid asset, which may be audited for regulatory compliance or build or development compliance. Further, the cybersecurity distributed ledger may store unique identifying information for a grid asset, which may be used to detect a security breach or other tampering with a grid asset. The cybersecurity distributed ledger may also track control or ownership of the grid asset, as well as changes or updates to the grid asset.
Apparatus comprise control circuitry configured to control a line frequency of a microgrid by directing a first set of one or more electrical devices coupled to the microgrid to produce a change in the line frequency through a change in operation of the first set, such that a second set of one or more electrical devices coupled to the microgrid adjusts operation in response to a detected line frequency changeably produced by the first set. Related methods and additional apparatus and methods are disclosed. Apparatus and methods are disclosed that use line frequency and/or line voltage. Apparatus and methods are also disclosed that use control history.
While regulators, the scientific community, and MSR developers still lack access to literature data on the thermal properties of clean fuel salts, even less information is available on the properties of fuel salts containing impurities. It is essential to understand, benchmark, and predict crucial data on the changes in thermal properties of fuel salt systems due to impurities arising from moisture, corrosion, and reactor operation (i.e., fission products). This research focuses on two actinide fuel salts (1) to investigate a worst-case scenario buildup of actinide fission product in a NaCl-UCl3 eutectic fuel salt and (2) to investigate NaCl-PuCl3 eutectic salt after 1000 hours of operation in a natural circulation flow loop flow to determine if corrosion or atmospheric (moisture/oxygen) products are present. For the first salt, a conservative assumption or worst-case scenario, for fission product buildup in a fuel salt was investigated by adding PuCl3 to eutectic 67 mol% NaCl – 33 mol% UCl3 salt resulting in a ternary salt having a composition of 61 mol% NaCl – 30 mol% UCl3 – 9mol% PuCl3. Addition of PuCl3 to eutectic NaCl-UCl3 resulted in a ternary salt that had a higher melting temperature than either the NaCl-PuCl3 or NaCl-UCl3 binary eutectic mixture. Addition of PuCl3 also resulted in an increase in density which was expected. The second salt was extracted from a micro loop. The composition of the fuel (primary) salt prior to flow loop operations was determined to be 64 mol% NaCl – 36 mol% PuCl3, however, the post-flow loop salt showed increased levels of MgCl2 and NaCl changing the salt composition to 10 mol% MgCl2 – 63mol% NaCl – 26mol% PuCl3) indicating the primary salt interacted with the rinse salt. Analysis of the post flow loop salt detected low concentrations of Al, Ni, Co, Nb, and Zr, most likely corrosion products from the flow loop material of construction. Contamination of the fuel salt (with the rinse salt NaCl-MgCl2) decreased the density by approximately 10% and reduced the onset of melting temperature by 50 °C, from 451 °C to approximately 400 °C. Results from the fission product simulated salt (61 mol% NaCl – 30 mol% UCl3 – 9mol% PuCl3) and the corrosion product salt (10 mol% MgCl2 – 63mol% NaCl – 26mol% PuCl3) will be included in two separate manuscripts for submission to peer-reviewed journals.
Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models’ ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In chest radiographs, the heterogeneity in distributions arises from the diverse conditions in X-ray equipment and their configurations used for generating the images. In the machine learning community, the challenges posed by the heterogeneity in the data generation source is known as domain shift, which is a mode shift in the generative model. In this work, we introduce a domain-shift detection and removal method to overcome this problem. Our experimental results show the proposed method’s effectiveness in deploying a pre-trained DL model for abnormality detection in chest radiographs in a clinical setting.
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100–350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Lignocellulosic materials present the largest source of biomass for biotechnology and green energy. This study aimed at better understanding the cell wall disintegration mechanisms relevant for the biochemical conversion of biomass to carbohydrates. Herein, we examined nanoscale changes in cell wall structure and composition upon industrially relevant chemical and enzymatic treatments to achieve the desired level of breakdown. One treatment involved hydrogen peroxide and acetic acid to remove lignin. Another modification used the cellulase enzyme for cell wall degradation. Band excitation contact resonance atomic force microscopy was used to visualize and mechanically characterize cell wall layers. After cellulase treatments, we detected microcracks across the cell wall. Wet-chemical, Fourier-transform infrared and Raman spectroscopic analyses confirmed the removal of lignin and extractives through acid bleaching, while the enzymatic treatment minimally affected the biopolymer composition. Delignification resulted in cell wall delamination and reduced stiffness. In conclusion, X-ray diffraction revealed changes in cellulose structure and crystallinity.
We propose a novel mechanism for the cosmological production of keV–GeV mass dark matter that interacts with the Standard Model through a small effective magnetic dipole moment. Such an interaction can be radiatively generated if dark matter couples to heavier charged particles. Previous studies have focused on the case where these charged states are much heavier than the reheat temperature, such that freeze-in production of dark matter is sensitive to the ultraviolet details of reheating. Here, we instead consider the possibility that these heavy states have masses comparable to the dark matter mass and are charged under a new kinetically-mixed U (1)$^{′}$. As a result, dark matter production is dominated by the infrared freeze-in of the heavy charged states that subsequently thermalize the rest of the dark sector to a temperature much below that of the visible bath. We delineate regions of parameter space consistent with cosmological and astrophysical constraints and identify benchmark scenarios that can guide the next generation of direct detection experiments searching for spin-dependent scattering of sub-GeV dark matter.
Polymer nanocomposites offer design solutions to control and tune optical, conductive, topological, and thermomechanical properties of advanced and multifunctional materials. Because of their ubiquitous nature, methodologies to diagnose failure or structural changes in the nanocomposites are of significant interest. Herein, we report a nanocomposite system loaded with quantum dots and coumarin-modified carbon nanotubes that transduce mechanical force into fluorescence at a strain, for the first time, as low as 7.5%. Our comprehensive studies detail the optical, morphological, and thermomechanical properties of these nanocomposites to establish the fundamental reason behind the activation of fluorescence. Here our results indicate that bare carbon nanotubes can irreversibly quench the fluorescence from quantum dots and that the coumarin-modified carbon nanotubes mitigate the quenching through Förster Resonance Energy Transfer. Next, the application of force to the sample changes the quantum dot-carbon nanotube spacing as well as the carbon nanotube morphology to activate fluorescence in the nanocomposite. Overall, this force activation of fluorescence can serve as a general strategy for the development of a new class of mechano-responsive nanocomposites that impart polymeric materials with desirable functionalities including damage sensing and mechanical strength.
Composites consisting of periodically arranged scintillating particles within an acrylic (poly methyl methacrylate) matrix are excellent templates for the gamma-insensitive detection of fast neutrons. In order to study the impact of radiation damage on acrylic-based scintillating particle composites, we exposed acrylic samples to different doses of fast neutrons up to 119.7 kGy and investigated the change in optical characteristics using optical absorption spectroscopy in the 200–800 nm wavelength range. The experimental results are compared to coupled MCNP6 and optical ray-tracing (FRED) simulations, and qualitative agreement is found. Neutron-induced radiation damage in the acrylic matrix manifests as a red-shift in the ultra-violet absorption edge of the acrylic and a corresponding decrease in the light propagation efficiency, which leads to detector performance degradation. We show for the first time that the effects of radiation damage can be mitigated by the use of wavelength-shifting coatings. We predict that composites with wavelength-shifting coatings can enable neutron detectors with high tolerance against neutron-induced radiation damage, a property that is particularly desired for neutron detection in high radiation environments.