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Low Gain Avalanche Detectors (LGADS) have recently been studied for applications in high energy physics. However, radiation hardness remains a problem. Here, we propose new LGAD process flow that allows for the creation of a very steep born profile in the multiapplication region, reducing the fractional acceptor removal and resulting performance degradation.
Artificial illumination identification within images is a useful tool for many applications. Performing such identification allows for an estimation of the illumination source spectrum, which in turn can be used for additional applications ranging from spectral detection and exploitation to statistics about nighttime light usage. Illumination identification has been performed in laboratory settings but not from an unmanned aerial vehicle (UAV) platform. Here, we test the feasibility of using a UAV and commercial off-the-shelf multispectral imaging sensor to perform such artificial illumination identification through linear discriminant analysis using nighttime UAV images. The results are very promising, showing source classification accuracies of 83.3%, 92.3%, 100%, and 100% for the incandescent, light-emitting diode, high pressure sodium, and metal halide illumination sources, respectively. We show that the information gained from the source identification can be further used to inform additional analysis, such as spectral identification. The high resolution of UAV imaging techniques combined with the knowledge of the illumination source can lead to better exploitation of such nighttime data for many applications.
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Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each n close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces green- house gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel. This is especially an issue if deceleration is applied in response to a bad measurement. In this study, a faulty radar was shown to cause a greater than 5% increase in fuel consumption. The mechanism of this fuel consumption increase is investigated and applied to other types of sensor failures to indicate their potential effects on fuel economy. This analysis indicates that poor GPS signals over short time can be largely filtered out, with no real gain or loss of fuel economy. V2V communications were intentionally limited by causing interference, which resulted in dropped communication packets over a small physical area, but not an appreciable impact on fuel economy.
Direct excitation of nickel photocatalysts has gained popularity over the last several years following the successful activation of nickel by Ir and Ru photosensitizers. The ability to pair these photosensitizers with Ni catalysts facilitates the formation of crucial chemical bonds under relatively mild conditions. However, there are still gaps in our understanding of what influences the performance of these Ni photocatalysts. Previous work from our group identified a nickel photocatalyst supported by a tridentate pyridinophane ligand that undergoes wavelength-dependent cross-coupling reactivity. Herein, we report detailed studies of the wavelength dependence of the C–O cross-coupling reactivity, illustrating that the catalytic activity has an optimal wavelength, and excitation on both the blue and red sides of this optimal wavelength leads to a reduction in product yield. We also reveal a crucial catalyst-alcohol preirradiation interaction that drastically alters the catalyst’s optical properties. Furthermore, we investigate both wavelength dependency and alcohol dependency via optical transient absorption spectroscopy and reveal a substrate binding process responsible for the observed wavelength-dependent cross-coupling catalysis.
JLAB has long been a hub of SRF technology with the CEBAF accelerator as one of its first large scale adopters. As SRF technology has advanced, the C50 and C100 programs have allowed for the extension of CEBAF's total energy to 6 GeV and nearly 12 GeV respectively. Along with the increase in energy reach, rates of accelerating gradient degradation have been extracted for these cryomodule designs. A plan to mitigate these losses & maintain robust gradient headroom to deliver the 12 GeV program - the CEBAF Performance Plan - established a multi-year effort of cryomodule refurbishments and replacements. Part of this plan included a cost optimization of the C50 program with more modern processing techniques and the replacement of existing cavities with larger grain boundary cavities produced from ingot Niobium (dubbed C75 for 75 MeV gain). Reports have been made on the prototype pair of C75 cavities installed in a C50 cryomodule and the first full C75 cryomodule installed in 2017 and 2021. This paper reports on the results from the qualification of the cavities for the second C75 module in both a vertical cryostat and the commissioning results of the cryomodule in the CEBAF tunnel. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177.
Hyperbolic neural networks have recently gained significant attention due to their promising results on several graph problems including node classification and link prediction. The primary reason for this success is the effectiveness of hyperbolic space in capturing the inherent hierarchy of graph datasets. However, they are limited in terms of generalization, scalability, and have inferior performance when applied to non-hierarchical datasets. In this paper, we take a completely different perspective for modeling hyperbolic networks and answer the following question: is an Euclidean model able to approximate a function or behavior in the hyperbolic space? Extending the universal approximation theory developed for Euclidean models, We draw an analogy from the hyperbolic components to the Euclidean counterparts and conclude that, in order to capture hierarchical features, it is possible to generalize hyperbolic models to be a special case of Euclidean models with the proposed Pseudo-Poincaré technique. We applied our non-linear hyperbolic normalization to the current state-of-the-art homogeneous and multi-relational graph networks and demonstrate significant improvements in performance compared to both Euclidean and hyperbolic counterparts. The primary impact of this work lies in its ability to capture hierarchical features in the Euclidean space, and thus, can replace hyperbolic networks without any loss in performance metrics while simultaneously leveraging the power of Euclidean networks such as interpretability and efficient execution of various model components.
Nuclear thermal propulsion is an evolving technology that can be utilized for long-distance space travel. This technology yields the advantage of a high thrust and specific impulse, but requires an examination of the potential design adjustments necessary to enhance its feasibility. The development of nuclear thermal propulsion requires a comprehensive understanding of the system-level behavior during transient and steady-state operation. This paper extends our previous research by including the proper handling of turbomachinery with multi-channel thermal hydraulic simulations only for steady-state solutions. The system-level approach presented here enables the treatment of the turbopump components through non-dimensional analysis that eliminates the assumption of constant efficiencies. All the other components within the system (e.g., reflector and core) can be discretized to multiple channels and layers, in which the full thermal hydraulic solution is established. The approach chosen here enables the realistic modeling of the propellant flow within the expander cycle by capturing the pressure losses, mass flow rate splits, and enthalpy gain for various operational conditions. The verification of the package is completed through point comparisons of previous investigations into similar system designs. Furthermore, sensitivity studies are used to benchmark the capabilities of the package and investigate solution variations due to the perturbation of operational conditions and regimes. The sensitivity studies performed here are important to capture variation in flow characteristics (e.g., temperature, pressure, mass flow rates) for different design objectives such as the thrust and specific impulse. This work demonstrates that system-level simulations lacking multi-channel capability and proper turbomachinery treatment may yield higher uncertainties in understanding the engine’s response and characteristics to changing various requirements. This is extremely important when screening the design space of such propulsion systems and when transient simulations are required.
Advanced batteries require advanced characterization techniques, and neutron scattering is one of the most powerful experimental methods available for studying next-generation battery materials. Neutron scattering offers a non-destructive method to probe the complex structural and chemical processes occurring in batteries during operation in truly in situ/in operando measurements with a high sensitivity to battery-relevant elements such as lithium. Neutrons have energies comparable to the energies of excitations in materials and wavelengths comparable to atomic distances in the solid state, thus giving access to study structural and dynamical properties of materials on an atomic scale. In this review, a broad overview of selected neutron scattering techniques is presented to illustrate how neutron scattering can be used to gain invaluable information of solid-state battery materials, with a focus on in situ/in operando methods. These techniques span multiple decades of length and time scales to uncover the complex processes taking place fundamentally on the atomic scale and to determine how these processes impact the macroscale properties and performance of functional battery systems. This review serves the solid-state battery research community by examining how the unique capabilities of neutron scattering can be applied to answer critical and unresolved questions of materials research in this field. A thorough and broad perspective is provided with numerous practical examples showing these techniques in action for battery research.
Bacterial-fungal interactions (BFIs) play an integral role in shaping microbial community composition, biogeochemical functions, spatial dynamics, and microbial dispersal. Mycelial networks created by filamentous fungi or other filamentous microorganisms (e.g., Oomycetes) act as 'fungal highways' that can be utilized by bacteria for transport throughout heterogeneous environments, greatly facilitating their mobility and granting them access to regions that may be challenging or impossible to reach on their own (e.g., due to air pockets within the soil). Several devices and experimental protocols have been created to study these fungal highways, including fungal highway columns. The fungal highway column designed by our group can be used for a variety of in situ or in vitro applications, as well as with diverse environmental and host-associated sample types. Herein, we describe the methods for performing experiments with these columns, including designing, printing, sterilizing, and preparing the devices. The options for analyzing data obtained from the use of these devices are also discussed here, and troubleshooting advice regarding potential pitfalls associated with experiments using fungal highway columns is offered. These devices can be used to gain a more comprehensive understanding of the diversity, mechanisms, and dynamics of fungal highway BFIs to provide valuable insights into the structural and functional dynamics within complex environments (e.g., soils) and across diverse habitats in which bacteria and fungi co-exist.
The overall goal of this two-phase project is to implement performance improvements of the Multiphase Flow with Interphase Exchanges (MFIX) Discrete Element Model (DEM) code that enable a transformative shift for industrial use. Prior to this effort, the largest simulations performed using MFIX are O(10 7 ) particles. This falls short of the O(10 9 ) particle simulations that must be completed on a timescale of days or weeks (vs. months or years) to enable simulations with physically-relevant domain sizes to be incorporated into industrial design cycles within five years. This was accomplished by tailoring best-in-class practices to bear on the unique challenges posed by the MFIX-DEM algorithm and code base. Scientific simulations (e.g., in cosmology, turbulent combustion) routinely use massively parallel computing to update far more particles in short wall clock times. Results from Phase 1 (1.5 years in duration) indicated significant gains in speed were possible for a wide range of benchmark cases. Moreover, a survey sent to >35 companies indicates that the timing is ideal for such an enhanced tool, with >80% of the respondents indicating that DEM is already value-added or will be within the next 5 years, and >70% of the respondents indicating that improved speed is the top computational priority. In Phase 2 (3.5 years in duration), the two major barriers that hinder industry from effectively using multiphase Computational Fluid Dynamics (CFD) to cut costs and improve performance, namely computational overhead and confidence in predictions, continued to be addressed. Regarding the former, the results from Phase 1 to guide the effort, with enhancements focused on an improved time-stepping algorithm and particle sorting. Four target problems of 1 billion particles each and increasing complexity were identified: homogeneous cooling, tumbler with continuous particle size distribution, discharge from a rectangular hopper and a cylindrical riser. Each of these were successfully simulated for relevant time scales (on order of seconds) using less than 24 hours of wall clock time. These represent the first 1-billion particle DEM simulations performed with MFIX, namely using the MFIX-Exa code. This code is currently under development at NETL in collaboration with Lawrence Berkeley National Laboratory. Regarding the second barrier on predictive uncertainty, experiments from Phase 1 (interacting nozzles - hydrodynamics only) and Phase 2 (very small-scale segregation experiments) were used to demonstrate the ability of two simplified approaches to uncertainty quantification (UQ). By limiting the number of particles, UQ based on the simplified treatment was compared to standard UQ, which was shown to have much higher computational demands. Experiments were also performed on a pilot-scale stripper unit to provide validation data for future CFD-DEM simulations and UQ.
Low-Gain Avalanche Diodes (LGADs) are a class of silicon detectors that have been specifically designed for the fast detection of minimum ionizing particles (mips) in High-Energy Physics experiments. While they provide timing resolution on the order of a few tens of picoseconds, they cannot achieve high spatial resolution due to the intrinsic characteristics of their structures. Thus, active R&D is on-going to develop detectors based on LGADs to improve their spatial resolution while maintaining the timing performance of the LGADs. Such devices are, for example, AC-coupled LGADs and Deep-Junction LGADs. Another device option is the Trench-Isolated LGAD (TI LGAD), where trenches etched at the periphery of the pixels isolate them while providing a high fill-factor. Here, in this paper, we present a variation of this latter approach, demonstrating its feasibility by means of 2-dimensional TCAD simulations.
Hydrogen energy as the next-generation clean energy carrier has attracted the attention of both academic and industrial fields. A key limit in the current stage is the operation temperature of hydrogen fuel cells, which lies in the slow development of high-temperature and high-efficiency proton exchange membranes. Currently, much research effort has been devoted to this field, and very innovative material systems have been developed. The authors think it is the right time to make a short summary of the high-temperature proton exchange membranes (HT-PEMs), the fundamentals, and developments, which can help the researchers to clearly and efficiently gain the key information. In this paper, the development of key materials and optimization strategies, the degradation mechanism and possible solutions, and the most common morphology characterization techniques as well as correlations between morphology and overall properties have been systematically summarized.
Abstract Three experiments (EXP) were conducted to determine the effect of feed additives on performance, intestinal integrity, gastrointestinal volatile fatty acids (VFA), and energy and nutrient digestion in nonchallenged nursery pigs. In EXP 1, 480 pigs (6.36-kg body weight, BW) were placed into 96 pens with 5 pigs/pen, and allotted to 1 of 10 dietary treatments: 1) negative control containing no feed additive (NC), 2) NC + 44 mg chlortetracycline and 38.5 mg tiamulin/kg diet (CTsb), 3) NC + 5% resistant potato starch (RSpo), 4) NC + 5% soluble corn fiber (SCF), 5) NC + 5% sugar beet pulp (SBP), 6) NC + 0.30% fatty acid mix (FAM), 7) NC + 0.10% phytogenic blend of essential oils and flavoring compounds (PHY), 8) NC + 50 mg Cu and 1,600 mg zinc oxide/kg diet (CuZn), 9) NC + 5% resistant corn starch (RScn), and 10) NC + 0.05% β-glucan (BG) for 28 d. There was no impact of dietary treatment on BW gain or feed intake (P ≥ 0.22). Pigs fed diets containing SCF, CTsb, and RSpo resulted in microbial community differences compared to pigs fed the NC (P < 0.05). In EXP 2, 48 barrows (12.8 kg BW) were selected at the end of EXP 1 and fed the same dietary treatments they had previously received: 1) NC, 2) NC + 5% RScn, 3) NC + 5% SCF, and 4) NC + FAM for 8 d. There was no effect of feeding diets containing RScn, SCF, or FAM on in vivo intestinal permeability (P ≤ 0.21). Ileal or colon pH, concentrations of VFA did not differ due to dietary treatment (P ≥ 0.36), but pigs fed diets containing FAM resulted in a greater butyric acid concentration in the cecum compared to pigs fed the NC (P ≤ 0.05). In EXP 3, 156 pigs (6.11 kg BW) were placed into 52 pens with 3 pigs/pen and allotted to 1 of 4 dietary treatments arranged in a factorial manner: 1) NC, 2) NC + 5% RSpo, 3) NC + 0.30% FAM, and 4) NC + 5% RSpo + 0.30% FAM for 24 d. Feeding pigs diets containing RSpo did not affect BW gain (P = 0.91) while pigs fed diets containing FAM grew improved BW gain (P = 0.09). Colonic butyric acid concentrations were greater in pigs fed diets containing RSpo (P = 0.03), while pigs fed diets containing FAM exhibited reduced total VFA concentrations (P = 0.11). The results indicate that supplementing diets with digestively resistant but fermentable fibers, short- and medium-chain fatty acids, or antibiotics do not have a consistent effect, positive or negative, on markers of intestinal integrity or barrier function, intestinal VFA patterns, ATTD of energy and nutrients, or on pig performance.
The fatigue strength of additively manufactured metallic parts in their as-built surface condition is mainly dominated by the surface roughness. Post-processing is often inevitable to reduce surface roughness effects even though post-processing diminishes the main advantage of additive manufacturing, which is net-shaped direct-to-service production. This work investigates the underlying mechanisms responsible for fatigue failure of additively manufactured 304L stainless steel parts in as-built and machined/polished surface conditions. Both strain- and force-controlled, fully reversed fatigue tests were conducted to gain a comprehensive understanding of surface roughness effects on fatigue behavior. The sensitivity to surface roughness is shown to be dependent on the control mode, with stress-based fatigue tests showing greater sensitivity than strain-based fatigue tests. Moreover, the fatigue life estimation for as-built specimens was performed based on surface roughness parameters as well as the fatigue properties of the specimens in machined/polished surface condition of the material without using any fatigue data of specimens in as-built surface condition. Accordingly, the effect of surface roughness on the fatigue behavior could be estimated reasonably well in both strain-life and stress-life approaches.
The multi-physics STAR-CCM+ code has been used to simulate the temperature and flow fields in canister gas leakage experiments conducted by using a 1/4.5-scale model storage cask. The simulations were conducted at Argonne National Laboratory’s Laboratory Computing Resource Center, utilizing high-performance computing resources. Development of STAR-CCM+ simulation models for the 1/4.5-scale model cask is described herein, followed by validation of the simulation results against experimental data. Canister depressurization and thermal response during gas leakage are discussed, along with analyses of the leakage path and allowable leakage rate. The insights gained from the STAR-CCM+ simulations and leakage analyses will help guide future experiments and actual industry applications of Argonne’s Remote Area Modular Monitoring system for canister surface temperature measurement (RAMM-TM) to enable effective aging management of spent fuel during extended dry storage, as well as help reduce risks to public safety and health and protect the environment.
The AM 2 Pn 2 (A= Ca, Sr, Ba, Yb, Mg; M = Zn, Cd, Mg; and Pn = N, P, As, Sb, Bi) family of Zintl phases has been known as thermoelectric materials and has recently gained much attention for highly promising materials for solar absorbers in single-junction and tandem solar cells. In this paper, we will, from first principles, explore the entire family of AM 2 Pn 2 compounds in terms of their ground-state structure, thermodynamic stability, and electronic structure. We also perform photoluminescence spectroscopy on bulk powder and thin film samples to verify our results, including the first measurements of the band gaps of SrCd 2 P 2 and CaCd 2 P 2 . The AM 2 Pn 2 compounds exhibit broad stability, are mostly isostructural to CaAl 2 Si 2 (P$\overline{3}$m1), and cover a wide range of band gaps from 0 to beyond 3 eV. This could make them useful for a variety of purposes, for which we propose several candidates, such as CaZn 2 N 2 for tandem top cell solar absorbers and SrCd 2 Sb 2 and CaZn 2 Sb 2 for infrared detectors. By examining the band structures of the AM 2 Pn 2 , we find that Mg 3 Sb 2 has the most promise as a thermoelectric material due to several off-Γ valence band pockets, which are unique to it among the compositions studied here.
Lithium sulfur batteries are one of the most promising next-generation energy storage technologies, because of their impressive theoretical energy density, low materials cost, and relative safety. However, incomplete understanding of their underlying operation mechanisms has hindered their further development and commercialization. In this work, to gain a better understanding of the operation mechanisms in the lithium sulfur battery, three macroscopically different (woven and nonwoven) and microscopically similar (made from the same carbon fibers) free-standing carbon felts are used as the conductive matrices to show how the macrostructure influences the redox reactions and the electrochemical performance. Additionally, an operando radiography and simultaneous in situ electrochemical impedance spectroscopy study are performed to highlight the differences of the carbon hosts and how they compare electrochemically in a lithium sulfur battery. The electrochemical results show that the carbon host with a more open structure results in increased capacity as well as a higher diffusion coefficient. The operando radiography shows that the open structures are more conducive to efficient redox reactivity in forming solid sulfur species, mostly in the form of elongated needlelike structures identified as β-S8, near the end of charge, which is corroborated by the impedance data. The data also reveal a “breathing” mechanism, where the polysulfides are pushed to the edges during discharge and pulled back during charge. Furthermore, it is also revealed that the “breathing” process is a limiting factor in the high rate performance. Overall, this study shows that there is a close relationship between the macrostructure of the carbon matrix and the electrochemical performance of the sulfur cathode.