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Transparent Nanocomposites for Gamma Spectroscopy, Neutron-Gamma Pulse Shape Discrimination, and Thermal Neutron Detection
No full textChapter 1 is dedicated to an overview of radiation sources, interactions, and detection; the different types of scintillators with a focus on nanocomposite scintillators, which shall be the subject of this dissertation, along with the scintillation mechanism in organic scintillators; and the working mechanism of photomultiplier tubes, which convert low energy scintillation photons into electrical signals for signal processing.Chapter 2 presents nanocomposite scintillators loaded with hafnium oxide nanoparticles and a phosphorescent host and guest system for gamma spectroscopy. Utilizing 1,3-di(9H-carbazol-9- yl)benzene (mCP) and 9,9-dimethyl-9H-fluorene (MF) as triplet hosts to facilitate Dexter energy transfer to bis[2-(4,6-difluorophenyl)pyridinato-C2,N](picolinato)iridium(III) (FIrPic), a blue light emitting phosphorescent dye, utilizes previously lost triplet excitons and increases the gamma light yield compared to a system that only uses fluorescent emission. A plastic scintillator containing 20 wt % MF, 10 wt % mCP, and 2 wt % FIrPic has a high gamma light yield of 14 800 Ph/MeV. Incorporating 20–35 wt % hafnium oxide nanoparticles into this organic matrix results in nanocomposites that demonstrate a gamma photopeak energy resolution of 6.4–9.7% at 662 keV while still retaining a gamma light yield between 8800–10 800 Ph/MeV.Chapter 3 explores transparent nanocomposites that can be used in gamma scintillation, radiation shielding, neutron-gamma pulse shape discrimination (PSD), and thermal neutron detection. All of these applications require high loading for high performance, but high loading of nanomaterials generally causes these materials to become opaque due to increased scattering with increased loading. Highly transparent nanocomposites are fabricated with ultra high nanoparticle loading, which may be adapted to a variety of applications by tuning the nanoparticle composition, nanoparticle loading, organic molecule composition, and organic molecule loading. A sample containing 20 wt % hafnium oxide nanoparticles and 20 wt % MF has a gamma light yield of 9782 Ph/MeV. The energy resolution of the hafnium Kα escape peak is 9.4% at 607 keV and the energy resolution of the deconvoluted photopeak is 4.9% at 662 keV. Furthermore, a 5.4 cm thick nanocomposite loaded with 80 wt % hafnium oxide nanoparticles offers the same stopping power as 4.9 cm of leaded glass at an incident photon energy of 10 MeV, while offering the benefits of being transparent and nontoxic. A nanocomposite containing 40 wt % hafnium oxide nanoparticles, 1 wt % gadolinium oxide nanoparticles, 2-(4-tert-butylphenyl)-5-(4-biphenylyl)-1,3,4-oxadiazole (PBD), and 1,4-bis(5-phenyl-2- oxazolyl)benzene (POPOP) is able to detect high energy gamma rays, fast neutrons, and thermal neutrons in a single material.Chapter 4 investigates nanocomposite scintillators loaded with 10 to 20 wt % hafnium oxide nanoparticles for gamma ray attenuation, up to 30 wt % 2,5-diphenyloxazole (PPO) for enhanced triplet-triplet annihilation to enable PSD, and 7.5 wt % 9,9-bis(4-vinylbenzyl)-9H-fluorene (SF) crosslinker to afford mechanically hard monoliths. A 10 mm diameter, 10 mm thick sample containing 0.2 wt % 9,10-diphenylanthracene (DPA) as the wavelength shifter has a gamma light yield of 8963 Ph/MeV and an energy resolution of 14.3% at 662 keV, while a 27 mm diameter, 30 mm thick sample of the same composition has a gamma light yield of 9202 Ph/MeV and an energy resolution of 17.5% at 662 keV. A 10 mm diameter, 10 mm thick sample containing 10 wt % nanoparticles, 30 wt % PPO, and 2 wt % Exalite 417 has a gamma light yield of 9246 Ph/MeV, a hafnium Kα escape peak energy resolution of 11.5% at 607 keV, and a deconvoluted photopeak energy resolution of 6.4% at 662 keV. This sample demonstrates a figure of merit of 3.47, on par with unloaded plastic scintillators used for neutron-gamma PSD.Chapter 5 summarizes the work and presents an outlook for future research. The introduction of MF and mCP as triplet hosts to facilitate Dexter energy transfer to FIrPic takes advantage of phosphorescent emission, which utilizes the previously lost triplet excitons and increases the gamma light yield compared to a system that only uses fluorescent emission. Nanocomposites are fabricated with an extremely high nanoparticle loading while maintaining high transparency, enabling their use in gamma scintillation, radiation shielding, neutron-gamma PSD, and thermal neutron detection. Mechanically hard scintillators are loaded with hafnium oxide nanoparticles, PPO, and SF despite high loadings of PPO and nanoparticles being known to act as plasticizing agents that typically result in soft samples that are prone to deformation and surface hazing, and the nanocomposite synthesis can be scaled up to greater than 1 in3 (16.4 cm3) with no loss in gamma light yield or mechanical hardness. Advances have been made to boost light yield by using a phosphorescent host and guest system as well as by exploring different dyes and crosslinkers for the polymer matrix, but the current nanocomposite system still suffers from decreased light yield with increased nanoparticle loading. Future work may explore luminescent nanoparticles or perovskite quantum dots to replace the non luminescent hafnium oxide nanoparticles
First-Principles Statistical Mechanics Study of Magnetic Fluctuations and Order–Disorder in the Spinel LiNi0.5Mn1.5O4 Cathode
Get PDFWhile significant magnetic interactions exist in lithium transition metal oxides, commonly used as Li-ion cathodes, the interplay between magnetic couplings, disorder, and redox processes remains poorly understood. In this work, we focus on the high-voltage spinel LiNi0.5Mn1.5O4 (LNMO) cathode as a model system on which to apply a computational framework that uses first principles-based statistical mechanics methods to predict the finite temperature magnetic properties of materials and provide insights into the complex interplay between magnetic and chemical degrees of freedom. Density functional theory calculations on multiple distinct Ni-Mn orderings within the LNMO system, including the ordered ground-state structure (space group P4332), reveal a preference for a ferrimagnetic arrangement of the Ni and Mn sublattices due to strong antiferromagnetic superexchange interactions between neighboring Mn4+ and Ni2+ ions and ferromagnetic Mn-Mn and Ni-Ni couplings, as revealed by magnetic cluster expansions. These results are consistent with qualitative predictions using the Goodenough-Kanamori-Anderson rules. Simulations of the finite temperature magnetic properties of LNMO are conducted using Metropolis Monte Carlo. We find that a "semiclassical" Monte Carlo sampling method based on the Heisenberg Hamiltonian accurately predicts experimental magnetic transition temperatures observed in magnetometry measurements. This study highlights the importance of a robust computational toolkit that accurately captures the complex chemomagnetic interactions and predicts finite temperature magnetic behavior to help analyze experimental magnetic and magnetic resonance spectroscopy data acquired ex situ and operando
Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip
Get PDFIn this work, we developed a dual-mode portable device that integrated a 3D-printed microfluidic chip for detection of dipicolinic acid (DPA) on chip. The system uses a ratiometric fluorescence nanoprobe formed by embedding carbon dots (CDs) into an Eu3⁺ metal–organic framework (Eu-MOF). Upon reaction with DPA in the microchannel, red fluorescence was enhanced and blue fluorescence suppressed, enabling sensitive ratiometric detection of DPA on chip with a detection limit (LOD) of 0.04 µM. Interestingly, the composite EuMOF/CDs/DPA also exhibits peroxidase-like activity, catalyzing the oxidation of TMB into a blue-colored product (oxTMB), which allows for colorimetric detection with an LOD of 10.14 µM. To improve usability and reduce environmental or instrumental variability, incorporating a microfluidic chip into a semi-portable device and utilizing a smartphone, making the system portable and miniaturized for easy operation. In the smartphone-assisted mode, the LODs were 0.33 µM (ratiometric fluorescence) and 12.27 µM (colorimetry), determined by RGB signal analysis, respectively. Moreover, satisfactory recoveries (85–104.6%) were achieved in the spiked real samples. Overall, this platform offers a straightforward, cost-effective, and versatile approach for DPA detection, with promising applications in food safety, environmental monitoring, and clinical diagnostics
Decadal recovery of fungal but not termite deadwood decay in tropical rainforest
Get PDFDeadwood represents ~11% of carbon stocks in tropical rainforest ecosystems and its decay is driven largely by fungi and termites, which contribute to the cycling of carbon and nutrients. Due to land use change, such as forest clearing, secondary growth tropical rainforests are increasingly prevalent around the globe. In secondary growth rainforest, studies found lower decay rates of leaf litter; however, little is known about how deadwood decays in these forests. Here, we tested whether termite and fungal species richness, composition and functions in decaying deadwood were similar in secondary and old-growth tropical rainforests. We assessed termite ability to discover and consume deadwood, as well as fungi community composition and contributions to wood decay. We placed non-native pine blocks, half of which were accessible to termites, in an old-growth rainforest site as a reference and two secondary growth rainforest sites that were restored 4 and 8 years before the start of the experiment. Blocks were harvested every 6 months for 4 years (eight harvests). Using fungal ITS amplicon sequencing of sawdust samples from the decaying deadwood blocks at the seventh harvest, we determined wood-dwelling fungal community composition. We found that termites discovered similar proportions of deadwood across the secondary and old growth rainforest sites, although the decay rates of the discovered deadwood were lower in the secondary growth rainforest. Further, fungal decay was similar to old growth rainforest levels in the older but not younger secondary growth rainforest, where it was slower; although differences among sites were small. Wood-dwelling fungal communities were similar between secondary and old growth rainforests. Synthesis and applications. Contrary to common assumptions, fungal communities and their wood decay functions were resilient and recovered relatively quickly within secondary growth rainforests; however, those of termites did not, which could reduce carbon and nutrient cycling in secondary growth rainforests. Active management methods such as the local transplant of termite- and fungi-occupied logs could accelerate the recovery of these ecosystems
Inferring free surface disturbance properties from Kelvin wakes using convolutional neural network
Get PDFAbstract:
Kelvin wakes are fluid motions generated by a moving disturbance at a free surface. We present a machine learning-based framework for inferring the properties of such moving disturbances from the Kelvin-wake patterns. We perform phase-resolved simulations to establish a dataset of nearly half a million Kelvin wakes generated by disturbances of varying propagating speed, length scale and geometry. Trained with the augmented data, the neural network achieves accuracies of 99.7% and 92.4% in predicting the velocity and the length scale of the disturbance, respectively, even if a random noise has been added to the training data. The explainability of the neural network is demonstrated by quantifying the contribution of the input data to the prediction, which shows a strong connection with the diverging and transverse waves. The accuracy of the neural network in predicting the disturbance length scale is sensitive to wave nonlinearity
Defining the decline: a glossary relevant to insect decline
No full textInsects are declining in abundance and species richness, globally. This has broad implications for the ecology of our planet, many of which we are only beginning to understand. Comprehensive, large-scale efforts are urgently needed to quantify and mitigate insect biodiversity loss. Because there is broad interest in this topic from a range of scientists, policymakers, and the general public, we posit that such endeavors will be most effective with precise and standardized terms. The Entomological Society of America is the world’s largest association of professional entomologists and is ideally positioned to lead the way on this front. We provide here a glossary of definitions for biodiversity loss terminology. This can be used to enhance and clarify communication among entomologists and others with an interest in addressing the multiple overlapping research, policy, and outreach challenges surrounding this urgent issue
Chromosome-Level Genome Assembly and Annotation of Corallium rubrum: A Mediterranean Coral Threatened by Overharvesting and Climate Change
Get PDFReference genomes are key resources in biodiversity conservation. Yet, sequencing efforts are not evenly distributed across the tree of life raising concerns over our ability to enlighten conservation with genomic data. Good-quality reference genomes remain scarce in octocorals while these species are highly relevant targets for conservation. Here, we present the first annotated reference genome in the red coral, Corallium rubrum (Linnaeus, 1758), a habitat-forming octocoral from the Mediterranean and neighboring Atlantic, impacted by overharvesting and anthropogenic warming-induced mass mortality events. Combining long reads from Oxford Nanopore Technologies (ONT), Illumina paired-end reads for improving the base accuracy of the ONT-based genome assembly, and Arima Hi-C contact data to place the sequences into chromosomes, we assembled a genome of 532 Mb (20 chromosomes, 309 scaffolds) with contig and scaffold N50 of 1.6 and 18.5 Mb, respectively. Fifty percent of the sequence (L50) was contained in seven superscaffolds. The consensus quality value of the final assembly was 42, and the single and duplicated gene completeness reported by BUSCO was 86.4% and 1%, respectively (metazoa_odb10 database). We annotated 26,348 protein-coding genes and 34,548 noncoding transcripts. This annotated chromosome-level genome assembly, one of the first in octocorals and the first in Scleralcyonacea order, is currently used in a project based on whole-genome resequencing dedicated to the conservation and management of C. rubrum
Sonographic Visualization of a Tortuous Optic Nerve: Case Report of a Novel Finding on Point-of-Care Ultrasound
Get PDFIntroduction: Idiopathic intracranial hypertension is a disorder typically affecting females with common complaints of headaches and visual disturbances. Diagnostic criteria have been described with clinical findings, high opening pressures in lumbar punctures, and magnetic resonance imaging (MRI) findings.
Case Report: A 36-year-old female presented with double vision and headaches. Point-of-care ultrasound demonstrated tortuosity of the optic nerve, a finding previously described in MRI studies, which may serve as an additional marker for ideopathic intracranial hypertension.
Conclusion:This case highlights the potential of point-of-care ultrasound to detect tortuous optic nerves, which may help in the early diagnosis of ideopathic intracranial hypertension, facilitating more timely and effective management
Scaling Across Multiple Dimensions to Accelerate Subgrid Machine Learning Parameterization Development
Get PDFSubgrid processes in climate models are a dominant source of uncertainty in long-term climate projections. Due to the prohibitive computational expense of direct simulation, these processes are instead parameterized using hand-tuned approximations and assumptions that lead to systematic biases and uncertainty. In theory, Machine-Learning (ML) parameterizations offer a few attractive advantages, namely being able to emulate higher resolution simulations at a fraction of the cost and scaling in performance with more data. However, they also introduce substantial challenges of their own, ranging from the highly empirical nature of their design and evaluation to the emergent behavior they can exhibit when coupled to the dynamical core of a climate model (i.e. tested online). Left unsolved, these challenges threaten the prospect of breaking ``deadlock" in convective parameterization development on a timescale in which humanity has not already experienced the worst effects of climate change.
The goal of this dissertation is to seed a fundamental step change in the progress of ML subgrid parameterization development by embracing scale on two seemingly orthogonal dimensions: online sampling and people. While these may seem like two unrelated directions, they confront the same central, existential problem: the solution space of possible ML parameterizations is too vast, too chaotic, and too poorly understood for any one graduate student or research lab to efficiently explore alone. However, with large scale O(100)$ online sampling, signal can be detected from emergent noise with statistical confidence. And by opening up the ML parameterization problem to seasoned machine learning researchers and data scientists around the world with a standardized dataset, benchmark, software suite, and competition, identifying the neural network architectures and paradigms with the best chance of solving this problem suddenly becomes plausible.
Our results conclusively show that offline error, online error, and online stability do not necessarily improve together. We present reproducible and statistically robust cases in which online stability and online error trade off against each other (i.e. in the cases of changing the loss function or removing dropout). More encouragingly, we also identify design decisions that improve ML parameterizations on all fronts (e.g. when training on multiple climates or using batch normalization). After being exposed to multiple state-of-the-art architectures through a three-month competition open to the world, we achieve state-of-the-art online zonal mean biases and identify offline biases that transcend the choice of architecture, helping narrow down the root cause of online biases that have persisted through multiple independent research papers. Altogether, these results present necessary but not sufficient steps towards operational reliable hybrid physics-ML climate models. We hope that our methods can also serve as a blueprint for continued rapid progress