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Luminescence properties of phosphate phosphor Ba{sub 3}Y(PO{sub 4}){sub 3}:Sm{sup 3+}

A series of reddish orange-emitting phosphate phosphors Ba{sub 3}Y{sub 1−x}(PO{sub 4}){sub 3}:xSm{sup 3+}(0.01≤x≤0.20) were synthesized by solid-state reaction. X-ray diffraction and photoluminescence spectra were utilized to characterize the structure and luminescence properties of as-synthesized phosphors. The optimized phosphors Ba{sub 3}Y{sub 0.95}(PO{sub 4}){sub 3}:0.05Sm{sup 3+} present several excitation bands from 300 to 500 nm, and exhibit intense reddish orange-emitting properties. The energy transfer type between Sm{sup 3+} ions was confirmed as d–d interaction by using Van Uitert model. The chromatic properties of the typical sample Ba{sub 3}Y(PO{sub 4}){sub 3}:0.05Sm{sup 3+} phosphor have been found to have chromaticity coordinates of (0.583, 0.405), which are located in reddish orange region under the excitation of 401 nm. These results indicated that Ba{sub 3}Y(PO{sub 4}){sub 3}:Sm{sup 3+} phosphors have potential applications in the field of lighting and display due to their effective excitation in the near-ultraviolet range. - Graphical abstract: The color coordinates for 5 mol% Sm{sup 3+} doped Ba{sub 3}Y(PO{sub 4}){sub 3} phosphor were calculated to be (0.583, 0.405), which are located in reddish orange region under the excitation of 401 nm. The peaks of Ba{sub 3}Y{sub 0.95}(PO{sub 4}){sub 3}:0.05Sm{sup 3+} phosphor with the highest emission intensity at 600 nm are broader than those of Y{sub 2}O{sub 3}:Eu{sup 3+} and Y{sub 2}O{sub 2}S:Eu{sup 3+} phosphors. All these characteristics suggest that Ba{sub 3}Y(PO{sub 4}){sub 3}:Sm{sup 3+} phosphors are suitable for near-UV (370–410 nm) excitation and can be applicable to near UV-based WLEDs. ▪ - Highlights: • Different concentration Sm{sup 3+}-doped Ba{sub 3}Y(PO{sub 4}){sub 3} phosphors were fabricated by solid state method. • The optimized phosphors present the several excitation bands from 300 to 500 nm. • The Ba{sub 3}Y(PO{sub 4}){sub 3}:Sm{sup 3+} shows bright reddish orange emissions under 401 nm excitation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

TU-PIS-Exhibit Hall-01: CT Dose Optimization Technologies II

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The imaging topic this year is CT scanner dose optimization capabilities. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Siemens‘ Commitment to the Right Dose in Computed Tomography Presentation Time: 11:15 - 11:45 AM Providing sustainable clinical results at highest patient safety: This is the challenge in medical imaging. Especially for Computed Tomography this means applying not simply the lowest, but the right dose for sound diagnostic imaging. Consequently, Siemens is committed to deliver the right dose in CT. In order to reduce radiation to the right dose, the first step is to provide the right dose technology. Through decades of research and development in CT imaging, Siemens CT has constantly introduced new ideas leading to a comprehensive portfolio of unique CARE technologies to deliver the right dose. For example automated kV adjustment based on patient size and the clinical question with CARE kV and three generations of iterative reconstruction. Based on the right dose technology, the next step is to actually scan at the right dose. For this, it is key to know the right dose targets for every examination. Siemens continuously involves CT experts to push developments further and outline how users can best adapt their procedures to the right dose. For users to know whether they met the right dose targets, it is therefore important to understand and monitor the actual absolute dose values. All scanners are delivered with defined default protocols which automatically use the available right dose technologies. Finally, to deliver the right dose not just in singular cases, but ideally to patients everywhere, organizations need then to manage dose across their institutions. Here Siemens offers the right dose management tools for administrations to analyze and document dose, to train their clinical staff and to continuously optimize the application of the right dose in clinical routine. Siemens CT is not only adhering to the ALARA principle, but is inspired by it. With CARE Right Siemens show their commitment to the right dose in CT. Toshiba Aquilion CT Dose Management Tools Presentation Time: 11:45 - 12:15 PM A practical review of Toshiba’s CT dose management technologies from the physicist’s perspective. This presentation will include an overview of Toshiba’s CT dose reduction technologies and dose safeguards. Content will include answers to frequently asked questions relating to dose management, dose tracking, CTDI measurement, ACR accreditation, ACR QC, and protocol review on Toshiba CT systems. Specific topics will include the following: Toshiba’s mechanism of tube current modulation; Exposure record format and interpretation of the values; Dose reporting and dose safeguard tools; Use of AIDR 3D iterative reconstruction; The configuration of task specific image quality target and its integration with AIDR 3D; Tips for scanning the ACR phantom with wide beam geometry; Measuring CTDI for wide beam geometry; Discussion of daily QC procedures and specifications; Tips for annual QC evaluation; Protocol management tools.

60 APPLIED LIFE SCIENCES↗

Dynamic Event Tree advancements and control logic improvements

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the input space characterized by epistemic uncertainties. The consequent Dynamic Event Tree performs the exploration of the aleatory space. In the RAVEN code, a more general approach has been developed, not limiting the exploration of the epistemic space through a Monte Carlo method but using all the forward sampling strategies RAVEN currently employs. The user can combine a Latin Hyper Cube, Grid, Stratified and Monte Carlo sampling in order to explore the epistemic space, without any limitation. From this pre-sampling, the Dynamic Event Tree sampler starts its aleatory space exploration. As reported by the authors, the Dynamic Event Tree is a good fit to develop a goal-oriented sampling strategy. The DET is used to drive a Limit Surface search. The methodology that has been developed by the authors last year, performs a Limit Surface search in the aleatory space only. This report documents how this approach has been extended in order to consider the epistemic space interacting with the Hybrid Dynamic Event Tree methodology.

97 MATHEMATICS AND COMPUTING↗

BENCHMARK TESTS FOR MARKOV CHAIN MONTE CARLO FITTING OF EXOPLANET ECLIPSE OBSERVATIONS

Ground-based observations of exoplanet eclipses provide important clues to the planets' atmospheric physics, yet systematics in light curve analyses are not fully understood. It is unknown if measurements suggesting near-infrared flux densities brighter than models predict are real, or artifacts of the analysis processes. We created a large suite of model light curves, using both synthetic and real noise, and tested the common process of light curve modeling and parameter optimization with a Markov Chain Monte Carlo algorithm. With synthetic white noise models, we find that input eclipse signals are generally recovered within 10% accuracy for eclipse depths greater than the noise amplitude, and to smaller depths for higher sampling rates and longer baselines. Red noise models see greater discrepancies between input and measured eclipse signals, often biased in one direction. Finally, we find that in real data, systematic biases result even with a complex model to account for trends, and significant false eclipse signals may appear in a non-Gaussian distribution. To quantify the bias and validate an eclipse measurement, we compare both the planet-hosting star and several of its neighbors to a separately chosen control sample of field stars. Re-examining the Rogers et al. Ks-band measurement of CoRoT-1b finds an eclipse 3190{sup +370}{sub -440} ppm deep centered at {phi}{sub me} = 0.50418{sup +0.00197}{sub -0.00203}. Finally, we provide and recommend the use of selected data sets we generated as a benchmark test for eclipse modeling and analysis routines, and propose criteria to verify eclipse detections.

79 ASTRONOMY AND ASTROPHYSICS↗

Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework II: Force, vibration, and molecular dynamics calculations

Recently, we have proposed the adaptive local basis set for electronic structure calculations based on Kohn–Sham density functional theory in a pseudopotential framework. The adaptive local basis set is efficient and systematically improvable for total energy calculations. In this paper, we present the calculation of atomic forces, which can be used for a range of applications such as geometry optimization and molecular dynamics simulation. We demonstrate that, under mild assumptions, the computation of atomic forces can scale nearly linearly with the number of atoms in the system using the adaptive local basis set. We quantify the accuracy of the Hellmann–Feynman forces for a range of physical systems, benchmarked against converged planewave calculations, and find that the adaptive local basis set is efficient for both force and energy calculations, requiring at most a few tens of basis functions per atom to attain accuracies required in practice. Since the adaptive local basis set has implicit dependence on atomic positions, Pulay forces are in general nonzero. However, we find that the Pulay force is numerically small and systematically decreasing with increasing basis completeness, so that the Hellmann–Feynman force is sufficient for basis sizes of a few tens of basis functions per atom. We verify the accuracy of the computed forces in static calculations of quasi-1D and 3D disordered Si systems, vibration calculation of a quasi-1D Si system, and molecular dynamics calculations of H{sub 2} and liquid Al–Si alloy systems, where we show systematic convergence to benchmark planewave results and results from the literature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Developing Efficient Benzene Additives for 19.43% Efficiency of Organic Solar Cells by Crossbreeding Effect of Fluorination and Bromination

Employing volatile solid additives have emerged as a promising method to optimize the morphology and improve the performance of organic solar cells (OSCs). However, principles governing the efficient design of solid additives remain elusive. In this work, the programmed fluorination and/or bromination on benzene core to develop efficient additives for OSCs is reported. The programmed fluorination and/or bromination endow the five halogen benzene derivatives, 1,3,5-trifluorobenzene, hexafluorobenzene, 1,3,5-tribromo-2,4,6-trifluorobenzene (TFTB), 1,3,5-tribromobenzene, and hexabromobenzene, with different melting and boiling points, volatility, as well as interactions with the host blend. Studies indicate that the additives with extremely high and low volatility are almost powerless and even detrimental to the morphology evolution. Among them, the combination of fluorine and bromine atoms on TFTB not only enables the more appropriate m.p./b.p. and volatility, but also exerts stronger molecular interactions with the host blend, giving rise to higher ordered molecular packing and more favorable morphology. Importantly, TFTB exhibits good universality to optimize the performances of OSCs with high power conversion efficiencies (PCEs; over 18%) in a group of binary blend systems, and an impressive PCE of 19.43% in the ternary PBTz-F:PM6:L8-BO system.

36 MATERIALS SCIENCE↗

Silicon-On-Silicon Carbide Platform for Integrated Photonics

Silicon carbide (SiC)'s nonlinear optical properties and applications to quantum information have recently brought attention to its potential as an integrated photonics platform. However, despite its many excellent material properties, such as large thermal conductivity, wide transparency window, and strong optical nonlinearities, it is generally a difficult material for microfabrication. Here, it is shown that directly bonded silicon-on-silicon carbide can be a high-performing hybrid photonics platform that does not require the need to form SiC membranes or directly pattern in SiC. The optimized bonding method yields defect-free, uniform films with minimal oxide at the silicon–silicon–carbide interface. Ring resonators are patterned into the silicon layer with standard, complimentary metal–oxide–semiconductor (CMOS) compatible (Si) fabrication and measure room-temperature, near-infrared quality factors exceeding 10 5 . The corresponding propagation loss is 5.7 dB cm -1 . The process offers a wafer-scalable pathway to the integration of SiC photonics into CMOS devices.

36 MATERIALS SCIENCE↗

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗

Bubbling Water–Treating DBD Plasma Device Optimization Using Experimental and Computational Methods

A dry air atmospheric pressure volume dielectric barrier discharge is employed to fix nitrogen in water. Producing nitrate for use as nitrogen fertilizer is the primary motivation. A 0D chemistry model is developed and informed by the electrical, and geometric characteristics of the device and the plasma gas temperature. Modeled ozone and nitrate densities are compared to those measured experimentally in the plasma effluent and treated liquid for a range of gas temperatures. Modeled and measured ozone densities are in good agreement; however, the model lacks the liquid chemistry to properly represent the measured nitrate density. A gas temperature-based shift from ozone to NO x producing regimes is observed in both experiment and model, and the reactions responsible are evaluated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Metal-Free, Oxidative Condensation of Heteroaryl- and Aryl Carbaldehydes towards Functionalized Carbonitriles

Functionalization of pyridines with the carbonitrile group for advanced cyclization reactions routinely incorporates metals, toxic oxidants, and cyanide reagents, often in linear synthetic sequences. Here, motivated by the strategic need of a heteroaryl carbonitrile synthon for further elaboration, we disclose an efficient, metal-free, one-pot reaction sequence that uses ammonium acetate as the nitrogen source and bis-acetoxyiodobenzene as the oxidant with low loadings. The developed method tolerates a diverse substrate scope including heteroaryl, aryl, and alkyl substrates with similar efficiency and expands the practitioner's reaction. Method development, optimization, substrate scope, preliminary mechanistic observations, competition experiments, and a scale up reaction are delineated herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sm 2 Ru 3 Sn 5 : A Noncentrosymmetric Cubic Member of the Ln 2 M 3 X 5 Family

An optimized synthetic method is presented for Sm 2 Ru 3 Sn 5 and investigate its physical properties and electronic structure. Sm 2 Ru 3 Sn 5 is prepared by arc-melting stoichiometric ratios of the elements and is confirmed by single crystal and powder X-ray diffraction. An antiferromagnetic transition is observed at T N = 3.8 K. A modified Curie-Weiss fit to the data in the range 50–150 K yields a Curie-Weiss temperature: θ CW = −36.6 K and an effective magnetic moment: μ eff = 0.83 μ B , in agreement with a Sm 3+ oxidation state. Field-dependent magnetization up to H = 7 T at 2 K shows a maximum response of 0.06 μ B , which is significantly lower than the expected Sm 3+ saturation moment (0.71 μ B ). Resistivity measurements indicate metallic behavior, and analysis of the magnetic entropy from the heat capacity reveals a doublet ground state due to crystal electric field splitting. The electronic structure and density of states are calculated with density function theory and further supported by the local density approximation with dynamical mean-field theory. Finally, the experimental and computational results highlight localized Sm 3+ moments and suggest a possible interplay between Ruddelman–Kitel–Kasuya–Yosida and Kondo interactions, positioning Sm 2 Ru 3 Sn 5 as a promising material for studying topology and complex physical phenomena.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Probing the Higgs boson CP properties in vector-boson fusion production in the H → τ + τ − channel with the ATLAS detector

The CP properties of the Higgs boson are studied in the vector-boson fusion production mode. The analysis exploits the decay mode of the Higgs boson into two τ-leptons using 140 fb −1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider. Results are obtained using the Optimal Observable method. CP-violating interactions between the Higgs boson and electroweak gauge bosons are considered in the effective field theory framework, with the interaction strength described in the HISZ basis by $\tilde{d}$, and in the Warsaw basis by c H$\tilde{W}$ , c H$\tilde{B}$ , and c H$\tilde{W}$B . No deviations relative to the Standard Model are observed, and limits are obtained on the strength parameters. The $\tilde{d}$ parameter is constrained to the interval [−0.012, 0.044] at the 95% confidence level while c H$\tilde{W}$ is constrained to [−0.24, 0.83], when considering both linear and quadratic effects of physics beyond the Standard Model.

Hadron-Hadron Scattering↗

Model Study of Blast Furnace Operation with Central Coke Charging

Blast furnace (BF) remains the dominant ironmaking process worldwide. Central coke charging (CCC) operation is a promising technology for stabilizing BF operations, but it needs reliable and quantified process design and control. In this work, a multi-fluid BF model is further developed for quantitatively investigating flow-thermal-chemical phenomena of a BF under CCC operation. This model features the respective chemical reactions in the respective coke and ore layers, and a specific sub-model of layer profile for the burden structure for the CCC operation. The simulation results confirm that the gas flow patterns and cohesive zone’s shape and location under the CCC operation are quite different from the non-CCC operation. Under the CCC operation, the heat is overloaded at the furnace center while the reduction load is much heavier at the periphery regions; the profiles of top gas temperature and gas utilization show bell-shape and inverse-bell-shape patterns, respectively. More importantly, these differences are characterized quantitatively. In this given case, when the CCC opening radius at the throat is 0.35 m, the cohesive zone top opening radius is around 0.50 m, and the isotherms of CCC operation become much steeper (~ 80 deg) than those of non-CCC operation (~ 60 deg) near BF central regions. In addition, it is confirmed that carbon solution-loss reaction rate can be decreased significantly at BF central regions under CCC operation. The model helps to understand CCC operation and provides a cost-effective method for optimizing BF practice.

36 MATERIALS SCIENCE↗

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗

Revealing meso-structure dynamics in additive manufacturing of energy storage via operando coherent X-ray scattering

3D printing is an emerging technology for the fabrication of energy storage devices, offering advantages over traditional manufacturing methods. However, optimization and design of such devices requires an understanding of the meso-structure formation during the 3D printing process. This study utilizes operando coherent X-ray scattering, X-ray Photon Correlation Spectroscopy (XPCS), to study the spatiotemporally-resolved far-from-equilibrium dynamics during direct ink writing 3D printing. Here, Lithium Titanate (LTO) based ink is prepared and rheologically tested for its shear-thinning properties. Two-time intensity-intensity functions are calculated to be used in subsequent quantitative analysis, which allows for an overall characterization of the dynamics, description of an initial fast decorrelation and identification of sudden rearrangements of subdomains of the sample. The results show the dynamics to be anisotropic, spatiotemporally heterogenous and marked by distinct rearrangement events, all of which impact the electrochemical performance of energy storage devices. The studied 3D printing ink is used to fabricate electrodes which are then electrochemically tested, showing good performance in cycling and retaining structural integrity. This work furthers the understanding of the far-from-equilibrium material dynamics during 3D printing, giving quantitative characterization of this process, and highlights aspects of structure formation relevant to the electrochemical performance of the resultant energy storage device.

36 MATERIALS SCIENCE↗