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The traditional Fermi function (long-distance radiative contribution) ansatz for nuclear beta decay describes enhanced perturbative effects in the limit of large nuclear charge $Z$ and/or small electron velocity $\beta$. We define and compute the quantum field theory object that replaces this ansatz for neutron beta decay, where neither of these limits hold.
Manipulating thermal properties of materials can be interpreted as the control of how vibrations of atoms (known as phonons) scatter in a crystal lattice. Compared to a perfect crystal, crystalline solids with defects are expected to have shorter phonon mean free paths caused by point defect scattering, leading to lower lattice thermal conductivities than those without defects. While this is true in many cases, alloying can increase the phonon mean free path in the Cd-doped AgSnSbSe 3 system to increase the lattice thermal conductivity from 0.65 to 1.05 W m –1 K –1 by replacing 18% of the Sb sites with Cd. It is found that the presence of lone pair electrons leads to the off-centering of cations from the centrosymmetric position of a cubic lattice. X-ray pair distribution function analysis reveals that this structural distortion is relieved when the electronic configuration of the dopant element cannot produce lone pair electrons. Furthermore, a decrease in the Grüneisen parameter with doping is experimentally confirmed, establishing a relationship between the stereochemical activity of lone pair electrons and the lattice anharmonicity. The observed “harmonic” behavior with doping suggests that lone pair electrons must be preserved to effectively suppress phonon transport in these systems.
Large-scale renewable energy must overcome conversion and storage challenges before it can replace fossil fuels due to its intermittent nature. However, current sustainable energy devices still suffer from high cost, low efficiency, and poor service life problems. Recently, porous metal-based materials have been widely used as desirable cross-functional platforms for electrochemical and photochemical energy systems for their unique electrical conductivity, catalytic activity, and chemical stability. To tailor the porosity length scale, ordering, and compositions, 3D printing has been applied as a disruptive manufacturing revolution to create complex architected components by directly joining sequential layers into designed structures. In conclusion, this article intends to summarize cutting-edge advances of metal-based materials for renewable energy devices (e.g., fuel cells, solar cells, supercapacitors, and batteries) over the past decade.
Significance Reaction centers (RCs) are critical to photosynthetic energy conversion. RCs in all characterized photosynthetic organisms contain two symmetrically arranged branches of chromophores and enable light-induced electron transfer with high yield. We fine-tune the properties of a key bacterial RC symmetry-breaking tyrosine via its replacement with noncanonical tyrosine analogs and determine kinetic outcomes. Results are interpreted through energetic characterization made possible by resonance Stark spectroscopy. Analysis indicates this tyrosine modulates the mechanism of the initial light-induced electron transfer, affording an alternative functional pathway that maintains the RC’s robust electron transfer. Modern molecular biology, ultrafast spectroscopy, crystallography, and energetic characterization enable the mechanistic model we describe. Our results deepen understanding of RC function and may have implications for other photocatalysts and enzymes.
Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest surrounding Crested Butte, CO with remotely sensed data, including LiDAR. The flight box design encompassed the watersheds, surveying a total area of 334 km2 across 72 lines. The instrument used was an Optech Gemini, with a pulse density of 2-9 pulses m-2 across the study area (see final report document for detailed information (Goulden and Musinsky 2020). These LiDAR data are the primary data that were provided by NEON, and include both LiDAR source data and derived surface products. The source data are contained in 'crbu_laz.zip' and 'classified_point_cloud.zip. 'classified_point_cloud.zip' represents an additional processing level on 'crbu_laz.zip'. The derived products are 'dsm_mosaic_min_phase_me.tif', 'dtm_mosaic_min_phase_me.tif', and 'tch_mosaic_min_phase_me.tif'. These files are raster mosaics of a digital surface elevation model (DSM), digital terrain elevation model (DTM), and a canopy height model (CHM) at 1 m resolution with a height threshold of > 2 m, respectively, in geotiff (.tif) format. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. [UPDATE] On 06/26/2026 the files 'crbu_laz.zip' and 'classified_point_cloud.zip' were replaced with original output files from NEON to address zip file corruption and naming errors. No changes were made to the underlying data. (See Methods Step 2 for details).
Postpolymerization functionalization of polymers can create new applications for existing materials, while retaining their most favorable, intrinsic properties. Polyvinyl chloride (PVC) is a widely used, commodity polymer that is particularly challenging to modify. We report a copper-catalyzed protocol that replaces a small fraction of the C-Cl bonds in PVC with C-B bonds to boronic esters. The reaction occurs with an inexpensive catalyst comprising copper(II) chloride and an NHC ligand derived from a common ionic liquid that is distinct from the N-heterocyclic carbene (NHC) used for the borylation of small alkyl halides. The resulting materials adhere strongly to common surfaces, such as glass and metals, even more strongly than do commercial glues.
Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance
Decabromodiphenyl ether (decaBDE) has been extensively used as a flame retardant in several applications, including nuclear electrical cable insulation. However, decaBDE has been identified as a persistent, bioaccumulative and toxic (PBT) substance, leading to regulatory scrutiny. The Environmental Protection Agency (EPA) published a regulation on January 6, 2021, aimed at phasing out the manufacturing, processing, and distribution of decaBDE. This rule set a compliance deadline of March 8, 2021, for the manufacture and processing of decaBDE, and an extended deadline of January 6, 2023, for specific applications including wire and cable insulation in nuclear power generation facilities. In response to such regulations, RSCC, a major supplier of safety-related electrical cables and associated products to the U.S. nuclear industry updated the formula of their crosslinked polyethylene (XLPE) insulation to replace the historically used decaBDE flame retardant with an acceptable alternative. This change from the previous decaBDE-containing XLPE prompted interest in comparative performance of the two material formulations, especially with respect to characteristics relevant to safety-related function such as thermal and radiation resistance. RSCC graciously provided samples of wire insulated with the decaBDE-containing XLPE formulation and corresponding wire insulated with XLPE of the new formulation, containing a decaBDE alternative. In this work we compare characteristics of the two formulations and a previously produced commercial version of the RSCC decaBDE-containing XLPE insulation subjected to thermal aging at 150 °C and 165 °C. The comparison was focused on mechanical durability, thermal stability in the oxidative environment, and chemical structures. Briefly, • Tensile elongation at break (EAB) results showed loss of mechanical elasticity with longer aging time, as expected. Aging time dependence of EAB did not differ between the decaBDE-containing and decaBDE-alternative samples. • Subtle differences between the two materials can be detected from Fourier-transform infrared spectroscopy (FTIR) absorbance spectra in the range below 1700 cm -1 , are assumed to be related to decomposition of flame retardant additives during thermal aging. • The oxidation induction time (OIT) data seemed to show that the unaged decaBDE-containing XLPE material is more thermally stable than the unaged decaBDE-alternative material, but the discrepancy in OIT decreased with aging time and the OIT values of the two materials became similar starting with the 4 th day of aging at 165 °C. This thermal aging investigation confirmed that the mechanical durability, a key property monitored for cable qualification, was not significantly affected by the modification of the formulation with a decaBDE alternative flame-retardant system in the investigated thermal aging conditions. Further studies on the same sets of materials exposed to thermal and gamma radiation aging would further inform comparison of the materials safety-related function.
The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.
At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the GEANT4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.
The bridging MeCN ligand in the dicopper(I) complexes [(DPFN)Cu 2 (μ,η 1 : η 1 -MeCN)][X] 2 (X=weakly coordinating anion, NTf 2 (1 a), FAl[OC 6 F 10 (C 6 F 5 )] 3 (1 b), Al[OC(CF 3 ) 3 ] 4 (1 c)) was replaced by white phosphorus (P 4 ) or yellow arsenic (As 4 ) to yield [(DPFN)Cu 2 (μ,η 2 : η 2 -E 4 )][X] 2 (E=P (2 a-c), As (3 a-c)). The molecular structures in the solid state reveal novel coordination modes for E 4 tetrahedra bonded to coinage metal ions. Experimental data and quantum chemical computations provide information concerning perturbations to the bonding in coordinated E 4 tetrahedra. Reactions with N-heterocyclic carbenes (NHCs) led to replacement of the E 4 tetrahedra with release of P 4 or As 4 and formation of [(DPFN)Cu 2 (μ,η 1 : η 1 - Me NHC)][X] 2 (4 a,b) or to an opening of one E-E bond leading to an unusual E 4 butterfly structural motif in [(DPFN)Cu 2 (μ,η 1 : η 1 -E 4 Dipp NHC)][X] 2 (E=P (5 a,b), E=As (6)). With a cyclic alkyl amino carbene ( Et CAAC), cleavage of two As-As bonds was observed to give two isomers of [(DPFN)Cu 2 (μ,η 2 : η 2 -As 4 Et CAAC)][X] 2 (7 a,b) with an unusual As 4 -triangle+1 unit.
The proximal Galerkin finite element method is a high-order, low iteration complexity, nonlinear numerical method that preserves the geometric and algebraic structure of pointwise bound constraints in infinite-dimensional function spaces. This paper introduces the proximal Galerkin method and applies it to solve free boundary problems, enforce discrete maximum principles, and develop a scalable, mesh-independent algorithm for optimal design with pointwise bound constraints. This paper also introduces the latent variable proximal point (LVPP) algorithm, from which the proximal Galerkin method derives. When analyzing the classical obstacle problem, we discover that the underlying variational inequality can be replaced by a sequence of second-order partial differential equations (PDEs) that are readily discretized and solved with, e.g., the proximal Galerkin method. Throughout this work, we arrive at several contributions that may be of independent interest. These include (1) a semilinear PDE we refer to as the entropic Poisson equation; (2) an algebraic/geometric connection between high-order positivity-preserving discretizations and certain infinite-dimensional Lie groups; and (3) a gradient-based, bound-preserving algorithm for two-field, density-based topology optimization. The complete proximal Galerkin methodology combines ideas from nonlinear programming, functional analysis, tropical algebra, and differential geometry and can potentially lead to new synergies among these areas as well as within variational and numerical analysis. Open-source implementations of our methods accompany this work to facilitate reproduction and broader adoption.
The vertical distribution of biomass burning aerosol (BBA) is important in regulating their impacts on weather and climate. The plume-rise process affects the injection height of BBA and interacts with the air parcel lifting and cloud processes. However, these processes are not represented in most global climate models. In this study, we replaced the fixed vertical profiles of monthly BBA emissions in the Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1) with an interactive fire plume-rise model. The vertical distribution of BBA emissions was calculated as a function of ambient thermodynamic conditions from the host E3SMv1, with distributions of fire sizes and sensible heat fluxes derived from the observations. The maximum fire radiative power (FRP) technique was used to determine the fire size. Scaling-FRP technique is used to calculate the wildfire heat release. Daily BBA emission, superimposed with a fire diurnal cycle retrieved from the satellite observation, was included in model simulations. The model shows improved agreement with satellite retrievals and in situ measurement during the National Oceanic and Atmospheric Administration Wildfire Experiment for Cloud chemistry, Aerosol absorption, and Nitrogen campaign. The model-observation comparison demonstrates the importance of the plume-rise model and fire diurnal cycle assumption in determining the BBA fields. We also find that E3SMv1 with new features produces a larger carbonaceous aerosol burden, leading to 0.13 W m –2 warming at the top of atmosphere compared to the default E3SMv1. This highlights the importance of accurately representing the BBA injection height and indicates a no-linear nature in the BBA-induced radiative effect.
Separation of propylene from its analogous hydrocarbons such as propane is of great importance in the petrochemical industry to produce valuable chemical feedstocks with desired purity. However, the well-established method currently used for industrial propane/propylene separation is energy intensive. It involves repeated cycling in the cryogenic and high-pressure distillation process and requires multiple columns and high reflux ratios because of the very similar physical properties of the two species. Thus, it is identified as one of the most capital- and energy-intensive processes. Adsorptive separation based on porous adsorbents is regarded as an alternative energy-efficient technology to replace or supplement the traditional heat-driven cryogenic distillation processes. In this context, metal–organic frameworks (MOFs) have emerged as the most promising candidates for propane/propylene separation, taking into consideration their unique tunability with respect to pore geometry and pore functionality. In this highlight, we summarize the latest advancement in developing MOFs as physisorbents for the separation and purification of propylene from propane, with a focus on those that demonstrate selective molecular exclusion and propane-selective adsorption. We discuss the adsorption preferences related to the material design and separation mechanisms, and review the existing challenges. Lastly, we offer our perspectives on various strategies for the future design of MOFs that hold true potential for propylene purification under industrial settings.
Secondary cell wall holds considerable potential as it has gained immense momentum to replace the lignocellulosic feedstock into fuels. Lignin one of the components of secondary cell wall tightly holds the polysaccharides thereby enhancing the recalcitrance and complexity in the biomass. Laccases (LAC) and peroxidases (PRX) are the major phenyl-oxidases playing key functions during the polymerization of monolignols into lignin. Yet, the functions of laccase and peroxidases gene families remained largely unknown. Hence, the objective of this conducted study is to understand the role of specific LAC and PRX in Populus wood formation and to further investigate how the altered Lac and Prx expression affects biomass recalcitrance and plant growth. This study of heterologous expression of Arabidopsis Lac and Prx genes was conducted in poplar to avoid any otherwise occurring co-suppression mechanism during the homologous overexpression of highly expressed native genes. In the pursuit of optimizing lignocellulosic biomass for biofuel production, the present study focuses on harnessing the enzymatic potential of Arabidopsis thaliana Laccase2, Laccase4, and Peroxidase52 through heterologous expression. We overexpressed selected Arabidopsis laccase2 (AtLac2), laccase4 (AtLac4), and peroxidase52 (AtPrx52) genes, based on their high transcript expression respective to the differentiating xylem tissues in the stem, in hybrid poplar (cv. 717) expressed under the developing xylem tissue-specific promoter, DX15 characterized the transgenic populus for the investigation of growth phenotypes and recalcitrance efficiency. Bioinformatics analyses conducted on AtLac2 and AtLac4 and AtPrx52, revealed the evolutionary relationship between the laccase gene and peroxidase gene homologs, respectively. Transgenic poplar plant lines overexpressing the AtLac2 gene (AtLac2-OE) showed an increase in plant height without a change in biomass yield as compared to the controls; whereas AtLac4-OE and AtPrx52-OE transgenic lines did not show any such observable growth phenotypes compared to their respective controls. The changes in the levels of lignin content and S/G ratios in the transgenic poplar resulted in a significant increase in the saccharification efficiency as compared to the control plants. Overall, saccharification efficiency was increased by 35–50%, 21–42%, and 8–39% in AtLac2-OE, AtLac4-OE, and AtPrx52-OE transgenic poplar lines, respectively, as compared to their controls. Moreover, the bioengineered plants maintained normal growth and development, underscoring the feasibility of this approach for biomass improvement without compromising overall plant fitness. This study also sheds light on the potential of exploiting regulatory elements of DX15 to drive targeted expression of lignin-modifying enzymes, thereby providing a promising avenue for tailoring biomass for improved biofuel production. These findings contribute to the growing body of knowledge in synthetic biology and plant biotechnology, offering a sustainable solution to address the challenges associated with lignocellulosic biomass recalcitrance.
This project was conducted under the Co-operative Agreement No. DE-FE0027584 with the US Department Energy to developed advanced Solid Oxide Fuel Cell (SOFC) Technologies. The overall objective of this project was to advance SOFC technology at the cell and stack level to enhance cell robustness and durability, increase performance, and reduce balance-of-plant (BOP) requirements. By reducing system complexity combined with the increases in power density and efficiency, the ultimate goal of the project was to increased reliability and to reduce capital and operating costs of installed systems. The project was focused on pathways that will reduce the cost of the SOFC cell and stack, including the following areas: Robust, redox tolerant cell technology Lower cost cell manufacturing through advances in cell design, which will reduce the amount of material, energy and time used in the fabrication of SOFCs High performance, low temperature electrolyte based on improvement of established materials Innovative SOFC stack architecture which truly integrates Balance of Plant functionality into the stack level design Thermal management of the fuel cell stack for increased durability and expanded window of operation Novel stack design amenable for use in sub-MW to multi-MW-scale power plants and having low replacement cost The incorporation of balance-of-plant (BOP) equipment into the stack platform increased the economic viability of smaller scale systems. The project objectives were met by a multi-prong approach, including new cell design complemented with modifications to existing cell technology, as well as a new stack design incorporating components typically included in the BOP, such as heat exchangers, oxidizer, fuel reformers, and recycle systems. The project culminated with demonstration of a stack test validating the viability of the cell and stack improvements. A cost model was also developed to estimate costs for the advanced stack technology at high volume manufacturing levels. The net outcome of the project is SOFC cell and stack technology with costs significantly below current DOE targets without compromising and, in some cases, improving on the performance and degradation rate demonstrated with the current state-of-the-art stack design. The results of this project advanced the reliability, robustness, and endurance of low-cost SOFC technology that ultimately are ready to be deployed in coal power systems with greater than 60 percent efficiency (based on higher heating value of fuel) and the capability for ≥97% CO 2 capture at a cost-of-electricity that is approximately 40 percent below presently available Integrated Gasification Combined Cycle systems.
This test report provides the results of radiation tolerance robustness testing that was performed on samples of robotic components and an ultrasonic guided wave air-slot sensor that represent components/sub-systems of the Robotic Air-slot Volumetric Inspection System (RAVIS) that has been engineered for volumetric inspection of Hanford tank bottom plates via under-tank refractory pad air-slots. The specific components tested for 1) functionality during active irradiation and 2) tolerance to cumulative radiation dose (until failure or upon reaching a cumulative dose test limit) were: • four samples each of a printed circuit board (PCB) and direct current (DC) motor, which are robotic components, and • 26 ultrasonic piezoelectric elements (samples) inside an air-slot sensor. The robotic components are part of the RAVIS air-slot inspection crawler drive control system that is responsible for remote communication with and actuation of the air-slot inspection crawler. The failure of either of these components during under-tank deployment would require manual retrieval via the crawler’s tether, which risks damage to the robot/refractory/tank. Preemptive replacement of the components at appropriately conservative dose/time intervals informed by failure dose would reduce the likelihood of under-tank failure. The components were included in radiation tolerance testing to quantify their failure doses to inform replacement intervals. The air-slot sensor is responsible for collecting ultrasonic inspection data (scan images) for the tank bottom plates during under-tank deployment. Compromised signal quality due to elevated noise levels caused by gamma radiation would compromise inspection performance. The air-slot sensor was included in radiation tolerance testing to quantify the impact of active irradiation on sensor signal quality. The irradiation and in-situ functional tests of the PCBs, DC motors and air-slot sensor took place in June and July 2020 at the Pacific Northwest National Laboratory. Testing was performed at a gamma dose rate near 300 rad/hr., which, in the absence of under-tank dose rate data, has been conservatively estimated to be the upper-bound dose rate beneath the primary tanks at Hanford. Irradiation took place at elevated temperatures of 150-200°F to determine failure doses that reflect the compounding effects of gamma radiation and heat. The test results revealed: • The DC motors can tolerate being actively irradiated at the high dose rate at 200°F and can tolerate a cumulative dose of 300,000 rad, that which would be incurred after 5 years of service at the 300 rad/hr dose rate. The component therefore meets minimum and preferred radiation tolerance and lifecycle requirements for robotic components. • The air-slot sensor can tolerate being actively irradiated at the high dose rate at 150°F and can tolerate a cumulative dose of 60,000 rad, that which would be incurred after 1 year of service at the 300 rad/hr dose rate. The sensor therefore meets minimum radiation tolerance and lifecycle requirements. • The PCB can tolerate being actively irradiated at the high dose rate, but can only tolerate a cumulative dose of 19,000 rad at 150-200°F. The PCB does not meet minimum radiation tolerance and lifecycle requirements; however, because the component is considered replaceable, it can be replaced before a cumulative dose of 19,000 rad is reached, determined through either monitoring with a dosimeter or scheduled time intervals that are calculated based on conservative estimates of under-tank dose rates.
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.