Publications

Ge-Sb-Te (GST) alloys are leading phase-change materials for data storage due to the fast phase transition between amorphous and crystalline states. Ongoing research aims at improving the stability of the amorphous phase to improve retention. This can be accomplished by the introduction of carbon as a dopant to Ge2Sb2Te5, which is known to alter the short- and mid-range structure of the amorphous phase and form covalently bonded C clusters, both of which hinder crystallization. The relative importance of these processes as a function of C concentration is not known. We used molecular dynamics simulation based on density functional theory to study how carbon doping affects the atomic structure of GST-C. Carbon doping results in an increase in tetrahedral coordination, especially of Ge atoms, and this is known to stabilize the amorphous phase. We observe an unexpected, non-monotonous trend in the number of tetrahedral bonded Ge with the amount of carbon doping. Our simulations show an increase in the number of tetrahedral bonded Ge up to 5 at.% C, after which the number saturates and begins to decrease above 14 at.% C. The carbon atoms aggregate into clusters, mostly in the form of chains and graphene flakes, leaving less carbon to disrupt the GST matrix at higher carbon concentrations. Different degrees of carbon clustering can explain divergent experimental results for recrystallization temperature for carbon doped GST.

High-nickel-content layered oxides are among the most promising electric vehicle battery cathode materials. However, their interfacial reactivity with electrolytes and tendency toward oxygen release (possibly yielding reactive 1O2) remain degradation concerns. Elucidating the most relevant (i.e., fastest) interfacial degradation mechanism will facilitate future mitigation strategies. We apply screened hybrid density functional (HSE06) calculations to compare the reaction kinetics of LixNiO2 surfaces with ethylene carbonate (EC) with those of O2 release. On both the (001) and (104) facets, EC oxidative decomposition exhibits lower activation energies than O2 release. Our calculations, coupled with previously computed liquid-phase reaction rates of 1O2 with EC, strongly question the role of “reactive 1O2” species in electrolyte oxidative degradation. The possible role of other oxygen species is discussed. To deal with the challenges of modeling LixNiO2 surface reactivity, we emphasize a “local structure” approach instead of pursuing the global energy minimum.

This document is intended to help users program the new mid-circuit measurement (MCM) and classical branching capabilities of the Quantum Scientific Computing Open User Testbed (QSCOUT). Here, we present and explain an exemplar “ping-pong teleportation” program that makes repeated MCM and branching calls. The program is written in Jaqal, the quantum assembly language used by QSCOUT. This document is intended to accompany a companion Jupyter notebook Exemplar_one_bit_teleportation_pingpong.ipynb.

All freely available plane-of-array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib-python and pvpltools-python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853-1 and 61853-2 testing, and the input data for each model were sourced from these system-specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system-specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system-specific data.

Understanding how science and technology advance has long been of interest to diverse scholarly communities. Thus far, however, such understanding has not been easy to map to, and thus to improve, the operational practice of research and development. Indeed, one might argue that the operational practice of research and development, particularly its exploratory research half, has become less effective in recent decades. In this paper, we describe a rethinking of how science and technology advance, one that is consistent with many (though not all) of the perspectives of the scholarly communities just mentioned, and one that helps bridge the divide between theory and practice. The result is an architecture we call “Bell's Dodecants,” to reflect its six mechanisms and two flavors, and their balanced nurturing at Bell Labs, the iconic 20th century industrial research and development laboratory.

It is vital that avionic packages used for testing and certifying the reliability and safety of U.S. nuclear weapons with platform aircraft survive exposure to shock environments during transportation and delivery. The objective of this research was to characterize the response to these transportation shock environments delivering accurate shock test specifications in order to set laboratory programming material and device certification rigor. Responses to shock events were analyzed in the frequ

Quantitative risk assessment (QRA) is highly dependent on data, leading to more robust models as new and updated data is acquired. The Hydrogen Plus Other Alternative Fuels Risk Assessment (HyRAM+) QRA capabilities include calculations of individual risk from leaks in a gaseous hydrogen facility due to the potential effects of jet fires and explosions. Leak frequencies are acquired through statistical analysis of published data from a variety of sources and industries. The filter leak frequencies in previous versions of the HyRAM+ software are substantially greater than the leak frequencies of other components, leading to QRA results for gaseous hydrogen in which filters consistently dominate the overall risk. Data that were previously used to derive the filter leak frequencies were reevaluated for applicability and additional data points were added to update the filter leak frequencies. The new frequencies are more comparable to leak frequencies for other components.

Monopulse is a technique for determining the Direction of Arrival (DOA) of a radar echo by comparing the simultaneous signal responses from two or more antenna beams or apertures. Two principal architectures are employed: 1) amplitude-comparison monopulse, and 2) phase-comparison monopulse. For a constrained-size fully and uniformly illuminated aperture, there is no meaningful difference between the DOA angle precision achievable by an amplitude monopulse architecture versus a phase monopulse

We report the results of the second charged-particle transport coefficient code comparison workshop, which was held in Livermore, California on 24-27 July 2023. This workshop gathered theoretical, computational, and experimental scientists to assess the state of computational and experimental techniques for understanding charged-particle transport coefficients relevant to high-energy-density plasma science. Data for electronic and ionic transport coefficients, namely, the direct current electrical conductivity, electron thermal conductivity, ion shear viscosity, and ion thermal conductivity were computed and compared for multiple plasma conditions. Additional comparisons were carried out for electron-ion properties such as the electron-ion equilibration time and alpha particle stopping power. Overall, 39 participants submitted calculated results from 18 independent approaches, spanning methods from parameterized semi-empirical models to time-dependent density functional theory. In the cases studied here, we find significant differences—several orders of magnitude—between approaches, particularly at lower temperatures, and smaller differences—roughly a factor of five—among first-principles models. We investigate the origins of these differences through comparisons of underlying predictions of ionic and electronic structure. The results of this workshop help to identify plasma conditions where computationally inexpensive approaches are accurate, where computationally expensive models are required, and where experimental measurements will have high impact.

Abstract not provided.

The Example Problems Manual supplements the User's Manual and the Theory Manual. The goal of the Example Problems Manual is to reduce learning time for complex end to end analyses. These documents are intended to be used together. See the User's Manual for a complete list of the options for a solution case. All the examples are part of the salinas test suite. Each runs as is.

During February 2023, a total of 32 individual DAS systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats in order to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the USGS and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, dark fiber). Monitored cable lengths vary between 152 and 120129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and is available for free download. Organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

High-fidelity simulations are performed to characterize the turbulence-induced wall pressure fluctuations on a sharp cone at a 5.5-degree angle-of-attack in a Mach 8 flow. Wall-resolved large-eddy simulation (LES) and wall-modeled large-eddy simulation (WMLES) results are compared to measurements at several locations on the cone body. Simulations are also compared to each other, and WMLES show good comparison in the autospectra, but modest comparison in the coherence.

It is commonly assumed that cleaning photovoltaic (PV) modules is unnecessary when the inverter is undersized because clipping will sufficiently mask the soiling losses. Clipping occurs when the inverter's AC size is smaller than the overall modules' DC capacity and leads to the conversion of only part of the PV-generated DC energy into AC. This study evaluates the validity of this assumption, theoretically investigating the current magnitude of clipping and its effect on soiling over the contiguous United States. This is done by modelling energy yield, clipping and soiling across a grid of locations. The results show that in reality, under the current deployment trends, inverter undersizing minimally affects soiling, as it reduces these losses by no more than 1%absolute. Indeed, clipping masks soiling in areas where losses are already low, whereas it has a negligible effect where soiling is most significant. However, the mitigation effects might increase under conditions of lower performance losses or more pronounced inverter undersizing. In any case, one should take into account that degradation makes clipping less frequent as systems age, also decreasing its masking effect on soiling. Therefore, even if soiling was initially mitigated by the inverter undersizing, its effect would become more visible with time.

This report provides technical guidance for the calibration of laboratory glassware to help the practitioner achieve traceability to the International System of Units and meet customer quality requirements. The discussion of traceability uses the National Institute of Standards and Technology’s seven essential elements of traceability as a framework. The guidance also includes how to determine when calibration is necessary, practical tips, and helpful references.

Abstract not provided.

Photonic crystal nanobeam cavities are valued for their small mode volume, CMOS compatibility, and high coupling efficiency-crucial features for various low-power photonic applications and quantum information processing. However, despite their potential, nanobeam cavities often suffer from low quality factors due to fabrication imperfections that create surface states and optical absorption. In this work, we demonstrate InP nanobeam cavities with up to 140% higher quality factors by applying a coating of Al₂O₃ via atomic layer deposition to terminate dangling bonds and reduce surface absorption. Additionally, changing the deposition thickness allows precise tuning of the cavity mode wavelength without compromising the quality factor. This Al₂O₃ atomic layer deposition approach holds great promise for optimizing nanobeam cavities that are well-suited for integration with a wide range of photonic applications.

NasGen provides a path for migration of structural models from NASTRAN bulk data format (BDF) into both an Exodus mesh file and an ASCII input file for Sierra Structural Dynamics (Salinas) and Solid Mechanics (Presto).

The cis- form of diaminodibenzocyclooctane (DADBCO, C16H18N2) is of interest as a negative coefficient of thermal expansion (CTE) material. The crystal structure was determined through single-crystal X-ray diffraction at 100 K and is presented herein.

Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

This report documents analysis to determine whether a hydrogen jet flame impinging on a tunnel ceiling structure could result in permanent damage to the Callahan tunnel in Boston, Massachusetts. This tunnel ceiling structure consists of a passive fire protective board supported by stainless steel hangers anchored to the tunnel ceiling with epoxy. Three types of fire protective boards were considered to determine whether heat from the flame could reach the stainless-steel hangers and the epoxy and cause the ceiling structure to collapse. Heat transfer analyses performed showed that the temperature remains constant where the steel hangers are attached to the passive fire protective board. According to these results, the passive fire protective board should provide adequate protection to the tunnel structure in this release scenario. Tunnel structures with similar suspended fire-resistant liner board materials should protect the integrity of the structure against the extremely low probability of an impinging hydrogen jet flame.

This paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.

Abstract not provided.

The ShakeAlert Earthquake Early Warning (EEW) system aims to issue an advance warning to residents on the West Coast of the United States seconds before the ground shaking arrives, if the expected ground shaking exceeds a certain threshold. However, residents in tall buildings may experience much greater motion due to the dynamic response of the buildings. Therefore, there is an ongoing effort to extend ShakeAlert to include the contribution of building response to provide a more accurate estimation of the expected shaking intensity for tall buildings. Currently, the supposedly ideal solution of analyzing detailed finite element models of buildings under predicted ground-motion time histories is not theoretically or practically feasible. The authors have recently investigated existing simple methods to estimate peak floor acceleration (PFA) and determined these simple formulas are not practically suitable. Instead, this article explores another approach by extending the Pacific Earthquake Engineering Research Center (PEER) performance-based earthquake engineering (PBEE) to EEW, considering that every component involved in building response prediction is uncertain in the EEW scenario. While this idea is not new and has been proposed by other researchers, it has two shortcomings: (1) the simple beam model used for response prediction is prone to modeling uncertainty, which has not been quantified, and (2) the ground motions used for probabilistic demand models are not suitable for EEW applications. In this article, we address these two issues by incorporating modeling errors into the parameters of the beam model and using a new set of ground motions, respectively. We demonstrate how this approach could practically work using data from a 52-story building in downtown Los Angeles. Using the criteria and thresholds employed by previous researchers, we show that if peak ground acceleration (PGA) is accurately estimated, this approach can predict the expected level of human comfort in tall buildings.

Abstract not provided.

Abstract not provided.

Recent experimental findings have shown that tantalum single crystals display strong anisotropy during Taylor impact testing in stark contrast to isotropic deformation in polycrystalline counterparts. In this study, a coupled dislocation dynamics and finite element model was developed to simulate the complex stress field under dynamic loading of a Taylor impact test and track the intricate evolution of the dislocation microstructure. Our model allowed us to investigate detailed motion of dislocations and their mutual interactions and the effect of varying simulation parameters, such as sample size, initial dislocation density, crystallographic orientation, and temperature. Simulation results show good agreement with experimental observations and shed light on the mechanical response at small-scale under extreme loading conditions. In addition, resolved shear stress analysis incorporating the effect of shear stress from impact was performed to quantitatively support and provide a means to understand the model predictions of the impact foot shape.

​Protocols play an essential role in Advance Reactor systems. A diverse set of protocols are available to these reactors. Advanced Reactors benefit from technologies that can minimize their resource utilization and costs. Evaluation frameworks are often used when assessing protocols and processes related to cryptographic security systems. The following report discusses the various characteristics associated with these protocol evaluation frameworks, and derives a novel evaluative framework.

Metal-organic frameworks (MOFs) have shown promise for adsorptive separations of metal ions. Herein, MOFs based on highly stable Zr(iv) building units were systematically functionalized with targeted metal binding groups. Through competitive adsorption studies, it was shown that the selectivity for different metal ions was directly tunable through functional group chemistry.

The frequency response function (FRF) is an essential means by which dynamic systems are qualified. In recent years, local modeling approaches have been extensively researched and shown to significantly outperform traditional FRF estimators. However, the standard local modeling approach assumes a perfectly-known system input, which results in biased FRF estimates in the presence of input noise. This paper derives a simple adjustment that can be used to improve FRF estimation for systems subjected to random excitation with noisy input data. This improvement can be implemented with little modification to standard local modeling algorithms and with little additional computational burden. The adjustment is coupled with a model selection procedure to avoid underfitting and overfitting. The methods presented in this paper are validated on a simulation, and they are shown to reduce bias due to input noise.

K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μ m of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μ m length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.

Risks associated with carbonation are a key limitation to greater replacement levels of ordinary portland cement (OPC) by supplementary cementitious materials (SCMs). The addition of pozzolanic SCMs in OPC alters the hydrate assemblage by forming phases like calcium-(alumina)-silicate-hydrate (C-(A)-S-H). The objective of the present study was to elucidate how such changes in hydrate assemblage influence the chemical mechanisms of carbonation in a realistic OPC system. Here, we show that synthetic zeolite Y (faujasite) is a highly reactive pozzolan in OPC that reduces the calcium content of hydration products via prompt consumption of calcium hydroxide from the evolving phase assemblage prior to CO2 exposure. Suppression of portlandite at moderate to high zeolite Y content led to a more damaging mechanism of carbonation by disrupting the formation of a passivating carbonate layer. Without this layer, carbonation depth and CO2 uptake are increased. Binders containing 12–18% zeolite Y by volume consumed all the calcium hydroxide from OPC during hydration and reduced the Ca/(Si+Al) ratio of the amorphous products to near 0.67. In these cases, higher carbonation depths were observed after exposure to ambient air with decalcification of C-(A)-S-H as the main source of CO2 buffering. Binders with either 0% or 4% zeolite Y contained calcium hydroxide in the hydrated microstructure, had higher Ca/(Si+Al) ratios, and formed a calcite-rich passivation layer that halted deep carbonation. Although the carbonated layer in the samples with 12% and 18% zeolite Y contained 70% and 76% less calcite than the OPC respectively, their higher carbonation depths resulted in total CO2 uptakes that were 12x greater than the OPC sample. Passivation layer formation in samples with calcium hydroxide explains this finding and was further supported by thermodynamic modeling. High Si/Al zeolite additives to OPC should be balanced with the calcium content for optimal carbonation resistance.

Z-pinch platforms constitute a promising pathway to fusion energy research. Here, we present a one-dimensional numerical study of the staged Z-pinch (SZP) concept using the FLASH and MACH2 codes. We discuss the verification of the codes using two analytical benchmarks that include Z-pinch-relevant physics, building confidence on the codes’ ability to model such experiments. Then, FLASH is used to simulate two different SZP configurations: a xenon gas-puff liner (SZP1*) and a silver solid liner (SZP2). The SZP2 results are compared against previously published MACH2 results, and a new code-to-code comparison on SZP1* is presented. Using an ideal equation of state and analytical transport coefficients, FLASH yields a fuel convergence ratio (CR) of approximately 39 and a mass-averaged fuel ion temperature slightly below 1 keV for the SZP2 scheme, significantly lower than the full-physics MACH2 prediction. For the new SZP1* configuration, full-physics FLASH simulations furnish large and inherently unstable CRs (> 300), but achieve fuel ion temperatures of many keV. While MACH2 also predicts high temperatures, the fuel stagnates at a smaller CR. The integrated code-to-code comparison reveals how magnetic insulation, heat conduction, and radiation transport affect platform performance and the feasibility of the SZP concept.

Since the earliest investigations of olefin metathesis catalysis, light has been the choice for controlling the catalyst activity on demand. From the perspective of energy efficiency, temporal and spatial control, and selectivity, photochemistry is not only an attractive alternative to traditional thermal manufacturing techniques but also arguably a superior manifold for advanced applications like additive manufacturing (AM). In the last three decades, pioneering work in the field of ring-opening metathesis polymerization (ROMP) has broadened the scope of material properties achievable through AM, particularly using light as both an activating and deactivating stimulus. In this Perspective, we explore trends in photocontrolled ROMP systems with an emphasis on approaches to photoinduced activation and deactivation of metathesis catalysts. Recent work has yielded a myriad of commercial and synthetically accessible photosensitive catalyst systems, although comparatively little attention has been paid to achieving precise control over polymer morphology using light. Metal-free, photophysical, and living ROMP systems have also been relatively underexplored. To take fuller advantage of both the thermomechanical properties of ROMP polymers and the operational simplicity of photocontrol, clear directions for the field are to improve the reversibility of activation and deactivation strategies as well as to further develop photocontrolled approaches to tuning cross-link density and polymer tacticity.

We present optical Thomson scattering measurements of electron density and temperature in high Mach number laser-driven blast waves in homogeneous gases. Taylor–Sedov blast waves are launched in nitrogen (N₂) or helium (He) at pressures between 0.4 mTorr and 10 Torr by ablating a solid plastic target with a high energy laser pulse (10 J, 10¹² W/cm²). Experiments are performed at high repetition rate (1 Hz), which allows one-dimensional and two-dimensional Thomson scattering measurements over an area of several cm² by automatically translating the scattering volume between shots. Electron temperature and density in the blast wave fronts were seen to increase with increasing background gas pressure. Measured electron density and temperature gradients were used to calculate $\partial$B/$\partial$t ∝ ∇T_e $\times$ ∇n_e⁠. The experimentally measured $\partial$B/$\partial$t showed agreement with the magnetic field probe (B-dot) measurements, revealing that magnetic fields are generated in the observed blast waves via the Biermann battery effect. The results are compared to numerical three-dimensional collisional magnetohydrodynamic simulations performed with FLASH, and are discussed in the context of spontaneous magnetic field generation via the Biermann battery effect.

Liquid crystal elastomers (LCEs) exhibit unique mechanical properties of soft elasticity and enhanced energy dissipation with rate dependency. They are potentially transformative materials for applications in mechanical impact mitigation and vibration isolation. However, previous studies have primarily focused on the mechanics of LCEs under equilibrium and quasistatic loading conditions. Critical knowledge gaps exist in understanding their rate-dependent behaviors, which are a complex mixture of traditional network viscoelasticity and the soft elastic behaviors with changes in the mesogen orientation and order parameter. Together, these inelastic mechanisms lead to unusual rate-dependent energy absorption responses of LCEs. In this work, we developed a viscoelastic constitutive theory for monodomain nematic LCEs to investigate how multiple underlying sources of inelasticity manifest in the rate-dependent and dissipative behaviors of monodomain LCEs. The theoretical modeling framework combines the neo-classical network theory with evolution rules for the mesogen orientation and order parameter with conventional viscoelasticity. The model is calibrated with uniaxial tension and compression data spanning six decades of strain rates. The established 3D constitutive model enables general loading predictions taking the initial mesogen orientation and order parameter as inputs. Additionally, parametric studies were performed to further understand the rate dependence of monodomain LCEs in relation to their energy absorption characteristics. Based on the parametric studies, particularly loading scenarios are identified as conditions where LCEs outperform conventional elastomers regarding energy absorption.

Filamentous fungi are known to secrete biochemicals that drive the synthesis of nanoparticles (NPs) that vary in composition, size, and shape; a process deemed mycosynthesis. Following the introduction of precursor salts directly to the fungal mycelia or their exudates, mycosynthesis proceeds at ambient temperature and pressure, and near neutral pH, presenting significant energy and cost savings over traditional chemical or physical approaches. The mycosynthesis of zinc oxide (ZnO) NPs by various fungi exhibited a species dependent morphological preference for the resulting NPs, suggesting that key differences in the biochemical makeup of their individual exudates may regulate the controlled nucleation and growth of these different morphologies. Metabolomics and proteomics of the various fungal exudates suggest that metal chelators, such as hexamethylenetetramine, present in high concentrations in exudates of Aspergillus versicolor are critical for the production dense, well-formed, spheroid nanoparticles. Further, the results also corroborate that the proteinaceous material in the production of ZnO NPs serves as a surface modifier, or protein corona, preventing excessive coagulation of the NPs. Collectively, these findings suggest that NP morphology is regulated by the small molecule metabolites, and not proteins, present in fungal exudates, establishing a deeper understanding of the factors and mechanism underlying mycosynthesis of NPs.

The association of ionizable polymers strongly affects their motion in solutions, where the constraints arising from clustering of the ionizable groups alter the macroscopic dynamics. The interrelation between the motion on multiple length and time scales is fundamental to a broad range of complex fluids including physical networks, gels, and polymer-nanoparticle complexes where long-lived associations control their structure and dynamics. Using neutron spin echo and fully atomistic, multimillion atom molecular dynamics (MD) simulations carried out to times comparable to that of chain segmental motion, the current study resolves the dynamics of networks formed by suflonated polystryene solutions for sulfonation fractions 0 ≤ f ≤ 0.09 across time and length scales. The experimental dynamic structure factors were measured and compared with computational ones, calculated from MD simulations, and analyzed in terms of a sum of two exponential functions, providing two distinctive time scales. These time constants capture confined motion of the network and fast dynamics of the highly solvated segments. A unique relationship between the polymer dynamics and the size and distribution of the ionic clusters was established and correlated with the number of polymer chains that participate in each cluster. The correlation of dynamics in associative complex fluids across time and length scales, enabled by combining the understanding attained from reciprocal space through neutron spin echo and real space, through large scale MD studies, addresses a fundamental long-standing challenge that underline the behavior of soft materials and affect their potential uses.

In this article we present a quantitative analysis of the second positive system of molecular nitrogen and the first negative system of the molecular nitrogen cation excited in the presence of ionizing radiation. Optical emission spectra of atmospheric air and nitrogen surrounding 210Po sources were measured from 250 to 400 nm. Multi-Boltzmann and non-Boltzmann vibrational distribution spectral models were used to determine the vibrational temperature and vibrational distribution function of the emitting N2(C3Πu) and N2+(B2Σ+u) states. A zero-dimensional kinetic model, based on the electron energy distribution function (EEDF) and steady-state excitation and de-excitation of N2(X1Σ+g), N2+(B2Σ+u), N2+(X2Σ+g), N4+, O2+, and N2(C3Πu, v), was developed for the prediction of the relative spectral intensity of both the N2+(B2Σ+u → X2Σ+g) emission band and the vibrational bands of N2(C3Πu → B3Πg) for comparison with the experimental data.

Design strategies for improving terahertz (THz) quantum cascade lasers (QCLs) in the 5-6THz range are investigated numerically and experimentally, with the goal of overcoming the degradation in performance that occurs as the laser frequency approaches the Reststrahlen band. Two designs aimed at 5.4THz were selected: one optimized for lower power dissipation and one optimized for better temperature performance. The active regions exhibited broadband gain, with the strongest modes lasing in the 5.3-5.6THz range, but with other various modes observed ranging from 4.76 to 6.03THz. Pulsed and continuous-wave (cw) operation is observed up to temperatures of 117K and 68K, respectively. In cw mode, the ridge laser has modes up to 5.71THz - the highest reported frequency for a THz QCL in cw mode. The waveguide loss associated with the doped contact layers and metallization is identified as a critical limitation to performance above 5THz.

In this work, we introduce a family of novel activation functions for deep neural networks that approximate n-ary, or n-argument, probabilistic logic. Logic has long been used to encode complex relationships between claims that are either true or false. Thus, these activation functions provide a step towards models that can efficiently encode information. Unfortunately, typical feedforward networks with elementwise activation functions cannot capture certain relationships succinctly, such as the exclusive disjunction (p xor q) and conditioned disjunction (if c then p else q). Our n-ary activation functions address this challenge by approximating belief functions (probabilistic Boolean logic) with logit representations of probability and experiments demonstrate the ability to learn arbitrary logical ground truths in a single layer. Further, by representing belief tables using a basis that associates the number of nonzero parameters with the effective arity of each belief function, we forge a concrete relationship between logical complexity and sparsity, thus opening new optimization approaches to suppress logical complexity during training. We provide a computationally efficient PyTorch implementation and test our activation functions against other logic-approximating activation functions on both traditional machine learning tasks as well as reproducing known logical relationships.

The report summarizes the work and accomplishments of DOE SETO funded project 36533 “Adaptive Protection and Control for High Penetration PV and Grid Resilience”. In order to increase the amount of distributed solar power that can be integrated into the distribution system, new methods for optimal adaptive protection, artificial intelligence or machine learning based protection, and time domain traveling wave protection are developed and demonstrated in hardware-in-the-loop and a field demonstration.

This report presents analyses of the AB5 and AB6 ABCOVE sodium spray fire experiments with the MELCOR code. This code simulates the progression of accident events for analysis and auditing purposes of nuclear facilities during accident conditions. Historically, the ABCOVE experiments have contributed to the validation of aerosol physics and related phenomena. Given advancements in sodium-cooled reactor designs, characterization of the sodium spray combustion may further the review and...

A previous SAND report, SAND2020-11353 described the basic metallurgical and surface roughness properties of additively manufactured Ti-64 material made using a powder bed fusion process. As part of that work, material was post-processed using a hot isostatic press (HIP) to densify and heat treat the material. This report is meant as an addendum to the original report and to provide specific data on material processed with HIP. The main focus of this report is to show the effects of HIP on the m

This report summarizes a gap analysis resulting from a literature review and expert interviews conducted by subject matter experts from Sandia National Laboratory, Siemens, and the Electric Power Research Institute (EPRI) in Spring 2023. The gap analysis consists of two main parts: The fault-ride through (FRT) behavior of grid-forming (GFM) inverter-based resources (IBR) and the response of state-of-the-art protection relays to the fault currents and voltages from GFM IBRs.

The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seis-moacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Using the recorded data set, we built 1373 events with local magnitude (ML) of −2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both ML and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of ML −MC, low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for ML −MC, low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. Results of joint discriminant analyses suggest that even the arguably inef-fective single classifier, like Pg/Sg in this case, can provide some value when used in combi-nation with others.

In superconducting systems in which inversion and time-reversal symmetry are simultaneously broken the critical current for positive and negative current bias can be different. For superconducting systems formed by Josephson junctions (JJs) this effect is termed Josephson diode effect. In this paper, we study the Josephson diode effect for a superconducting quantum interference device (SQUID) formed by a topological JJ with a 4π-periodic current-phase relationship and a topologically trivial JJ. We show how the fractional Josephson effect manifests in the Josephson diode effect with the application of a magnetic field and how tuning properties of the trivial SQUID arm can lead to diode polarity switching. We then investigate the ac response and show that the polarity of the diode effect can be tuned by varying the ac power and discuss differences between the ac diode effect of asymmetric SQUIDs with no topological JJ and SQUIDs in which one JJ is topological.

One of the most iconic of radar waveforms is the Linear FM chirp. It is well-behaved and well-understood, and has become the gold standard against which other radar waveforms are measured. It has a number of desirable attributes, but is not without some issues. It may be processed by a number of techniques with many variations. Details of the Linear FM chirp are presented and discussed in this report.

Abstract: The effect of moisture on the photo-oxidative degradation of polyamide-6 (PA-6) was studied by analyzing the mechanical response after two different accelerated aging procedures. In the first aging procedure, the PA-6 was only exposed to ultra-violet (UV) radiation at 60 ∘C. In the second procedure, the same duration of UV radiation was periodically interrupted while the relative humidity was raised to 100%. Diffusion-limited and nominally homogeneous degradation conditions were investigated using bulk and film specimens, respectively. Accelerated UV aging reduced the ductility of PA-6, but the additional hygrothermal exposure had no effect on the ductility or strength, indicating that humidity did not influence the photo-oxidation of PA-6. This finding contrasts with previous studies that found thermo-oxidation of PA-6 was accelerated by moisture. Graphical abstract: (Figure presented.)

This article describes the implementation of a new numerical model of the power take-off system installed in the Monterey Bay Aquarium Research Institute wave energy converter, a device developed to provide power to various oceanic research missions. The simultaneous presence of hydraulic, pneumatic, and electrical subsystems in the power take-off system represents a significant challenge in forging an accurate model able to replicate the main dynamic characteristics of the system. The validation of the new numerical model is addressed by comparing simulations with the measurements obtained during a series of bench tests. Data from the bench tests show good agreement with the numerical model. The validated model provides deeper insights into the complex nonlinear dynamics of the power take-off system and will support further performance improvements in the future.

Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.

We characterize the performance of two pixelated neutron detectors: a PMT-based array that utilizes Anger logic for pixel identification and a SiPM-based array that employs individual pixel readout. The SiPM-based array offers improved performance over the previously developed PMT-based detector both in terms of uniformity and neutron detection efficiency. Each detector array uses PSD-capable plastic scintillator as a detection medium. We describe the calibration and neutron efficiency measurement of both detectors using a 137Cs source for energy calibration and a 252Cf source for calibration of the neutron response. We find that the intrinsic neutron detection efficiency of the SiPM-based array is (30.2 ± 0.9)%, which is almost twice that of the PMT-based array, which we measure to be (16.9 ± 0.1)%.

Hydrogen continues to show promise as a viable contributor to achieving energy storage goals such as energy security and decarbonization in the United States. However, many new and expanded hydrogen use applications will require identifying methods of larger-scale storage than the solutions that currently exist for smaller storage applications. One possibility is to store large quantities of gaseous hydrogen below ground level. Underground storage of other fuels such as natural gas is already currently utilized, so much of the infrastructure and basic technologies can be used as a basis for underground hydrogen storage (UHS). A few commercial UHS facilities currently exist in the United States, including salt caverns owned and operated by Air Liquide, Linde, and Conoco Philips, but UHS is still a relatively new concept that has not been widely deployed. It is necessary to understand the safety risks and hazards associated with UHS before its use can be expanded and accepted more broadly. Many of these risks are addressed through regulations, codes, and standards (RCS) issued by governing bodies and organizations with expertise in certain hazards. This report is a review of RCS documents relevant to UHS, with a particular lens on potential technical gaps in existing guidance. These gaps may be specific to the physical properties of hydrogen or due to the different technologies relevant for hydrogen vs. natural gas storage. This is meant to be a high-level review to identify relevant documents and potential gaps. Formally addressing the individual gaps identified here within the codes and standards themselves would involve a more intensive analysis and differ based on the code or standard revision processes of the various publishing organizations. Therefore, presenting specific recommendations for revising the verbiage of the documents for UHS applications is left for future work and other publications.

Time resolved liquid and vapor fields of dodecane and oxymethylene ethers are measured from Spray A-3 and Spray D using high speed Rayleigh scattering and diffuse back illumination at the Engine Combustion Network (ECN) Spray A condition of 900 K and 22.8 kg/m3. Global quantities including mixture fraction, vapor and liquid penetration, as well as spreading angle are measured. The mixture fraction fields and vapor penetration profiles are well predicted by the 1-D Musculus-Kattke model. The mixture fraction field and vapor penetration from Spray A-3 are similar to those measured from Spray A in previous works. Spray D exhibits higher mixture fraction fields and vapor penetration due to its larger nozzle diameter. The quasi-steady mixture fraction fields from these injectors scale well when distance from the injector is normalized by the nozzle diameter. The turbulent dissipation structures were also analyzed based on the orientation, thickness, and magnitude of the mixing layers. The orientation and thickness are similar to other measurements in atmospheric gas jets, while the magnitude is lower. The thickness and magnitude are subject to uncertainties due to limitations in the imaging resolution of the system but still provide an order of magnitude as a useful reference for comparison against computational fluid dynamic simulations.

The hydrodynamics of the dense confining fuel shell is of great importance in defining the behavior of the burning plasma and burn propagation regimes of inertial confinement fusion experiments. However, it is difficult to probe due to its low emissivity in comparison with the central fusion core. In this work, we utilize the backscattered neutron spectroscopy technique to directly measure the hydrodynamic conditions of the dense fuel during fusion burn. Experimental data are fit to obtain dense fuel velocities and apparent ion temperatures. Trends of these inferred parameters with yield and velocity of the burning plasma are used to investigate their dependence on alpha heating and low mode drive asymmetry. It is shown that the dense fuel layer has an increased outward radial velocity as yield increases, showing that burn has continued into re-expansion, a key signature of hotspot ignition. A comparison with analytic and simulation models shows that the observed dense fuel parameters are displaying signatures of burn propagation into the dense fuel layer, including a rapid increase in dense fuel apparent ion temperature with neutron yield.

We present a mathematical framework for constructing the most general neutrino mass matrices that yield the observed spectrum of light active neutrino masses in conjunction with arbitrarily many heavy sterile neutrinos, without the need to assume a hierarchy between Dirac and Majorana mass terms. The seesaw mechanism is a byproduct of the formalism, along with many other possibilities for generating tiny neutrino masses. We comment on phenomenological applications of this approach, in particular deriving a mechanism to address the long-standing (g-2)μ anomaly in the context of the left-right symmetric model.

Despite their noted potential in polynomial-system solving, there are few concrete examples of Newton-Okounkov bodies arising from applications. Accordingly, in this paper, we introduce a new application of Newton-Okounkov body theory to the study of chemical reaction networks and compute several examples. An important invariant of a chemical reaction network is its maximum number of positive steady states Here, we introduce a new upper bound on this number, namely the ‘Newton-Okounkov body bound’ of a chemical reaction network. Through explicit examples, we show that the Newton-Okounkov body bound of a network gives a good upper bound on its maximum number of positive steady states. We also compare this Newton-Okounkov body bound to a related upper bound, namely the mixed volume of a chemical reaction network, and find that it often achieves better bounds.

Abstract not provided.

This abstract presents a comprehensive analysis of total ionizing dose (TID) response in GlobalFoundries' (GFs) 12LP 12 nm bulk Fin-based field effect transistor (FinFET) technology using 10 keV X-rays. Devices with higher threshold voltages (VTs) demonstrated lower increases in OFF-state leakage current (I_ DS, OFF ) post-irradiation, highlighting the mitigating role of high VT in TID response. Our data show that transistors with fewer fins exhibit superior TID resistance, implying lower susceptibility to radiation effects. Our study also probed two bias conditions, 'Gate-On' and 'Pass-Gate,' with the former displaying more severe TID degradation. Interestingly, p-type devices displayed negligible degradation, underscoring their inherent resilience to TID effects. Additionally, medium thick n-type devices echoed the fin-count-dependent TID response observed in other transistor types, further strengthening our findings. These results underscore the importance of strategic transistor selection and design for enhancing the TID resilience of future complementary metal-oxide semiconductor (CMOS) FinFET architectures, particularly critical in radiation-intense environments.

We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers-Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.

As modern gasoline direct injection (GDI) engines utilize sophisticated injection strategies, a detailed understanding of the air-fuel mixing process is crucial to further improvements in engine emission and fuel economy. In this study, a comprehensive evaluation of the spray process of single-component iso-octane (IC8) and multi-component gasoline surrogate E00 (36 % n-pentane, 46 % iso-octane, and 18 % n-undecane, by volume) fuels was conducted using an Engine Combustion Network (ECN) Spray G injector. High-speed extinction, schlieren, and microscopy imaging campaigns were carried out under engine-like ambient conditions in a spray vessel. Experimental results including liquid/vapor penetration, local liquid volume fraction, droplet size, and projected liquid film on the nozzle tip were compared under ECN G1 (573 K, 3.5 kg/m3), G2 (333 K, 0.5 kg/m3), and G3 (333 K, 1.01 kg/m3) conditions. In addition to the experiments, preferential evaporation process of the E00 fuel was elucidated by Large–Eddy Simulations (LES). The three-dimensional liquid volume fraction measurement enabled by the computed tomographic reconstruction showed substantial plume collapse for E00 under the G2 and G3 conditions having wider plume growth and plume-to-plume interaction due to the fuel high vapor pressure. The CFD simulation of E00 showed an inhomogeneity in the way fuel components vaporized, with more volatile components carried downstream in the spray after the end of injection. The high vapor pressure of E00 also results in ∼4 μm smaller average droplet diameter than IC8, reflecting a higher rate of initial vaporization even though the final boiling point temperature is higher. Consistent with high vapor pressure, E00 had a wider plume cone angle and enhanced interaction with the wall to cover the entire surface of the nozzle tip in a film. However, the liquid fuel underwent faster evaporation, so the final projected tip wetting area was smaller than the IC8 under the flash-boiling condition.

We compare the suitability of various magnesium-based liquid metal alloy ion sources (LMAISs) for scalable focused-ion-beam (FIB) implantation doping of GaN. We consider GaMg, MgSO4●7H2O, MgZn, AlMg, and AuMgSi alloys. Although issues of oxidation (GaMg), decomposition (MgSO4●7H2O), and excessive vapor pressure (MgZn and AlMg) were encountered, the AuMgSi alloy LMAIS operating in a Wien-filtered FIB column emits all Mg isotopes in singly and doubly charged ionization states. We discuss the operating conditions to achieve <20 nm spot size Mg FIB implantation and present Mg depth profile data from time-of-flight secondary ion mass spectrometry. We also provide insight into implantation damage and recovery based on cathodoluminescence spectroscopy before and after rapid thermal processing. Prospects for incorporating the Mg LMAIS into high-power electronic device fabrication are also discussed.

Abstract not provided.

Lymphocytic choriomeningitis virus (LCMV) and Lassa virus (LASV) share many genetic and biological features including subtle differences between pathogenic and apathogenic strains. Despite remarkable genetic similarity, the viscerotropic WE strain of LCMV causes a fatal LASV fever-like hepatitis in non-human primates (NHPs) while the mouse-adapted Armstrong (ARM) strain of LCMV is deeply attenuated in NHPs and can vaccinate against LCMV-WE challenge. Here, we demonstrate that internalization of WE is more sensitive to the depletion of membrane cholesterol than ARM infection while ARM infection is more reliant on endosomal acidification. LCMV-ARM induces robust NF-κB and interferon response factor (IRF) activation while LCMV-WE seems to avoid early innate sensing and failed to induce strong NF-κB and IRF responses in dual-reporter monocyte and epithelial cells. Toll-like receptor 2 (TLR-2) signaling appears to play a critical role in NF-κB activation and the silencing of TLR-2 shuts down IL-6 production in ARM but not in WE-infected cells. Pathogenic LCMV-WE infection is poorly recognized in early endosomes and failed to induce TLR-2/Mal-dependent pro-inflammatory cytokines. Following infection, Interleukin-1 receptor-associated kinase 1 (IRAK-1) expression is diminished in LCMV-ARM- but not LCMV-WE-infected cells, which indicates it is likely involved in the LCMV-ARM NF-κB activation. By confocal microscopy, ARM and WE strains have similar intracellular trafficking although LCMV-ARM infection appears to coincide with greater co-localization of early endosome marker EEA1 with TLR-2. Both strains co-localize with Rab-7, a late endosome marker, but the interaction with LCMV-WE seems to be more prolonged. These findings suggest that LCMV-ARM’s intracellular trafficking pathway may facilitate interaction with innate immune sensors, which promotes the induction of effective innate and adaptive immune responses.

The overall goal of this investigation was to develop an innovative high-temperature chloride molten salt flow control valve capable of operation up to 750 °C. The team developed an integrated active and passive thermal management system to ensure robust design for freeze-thaw cycles, with either a bellows-sealed configuration, a high-temperature stuffing box, or combination of the two. The STM system is unique in the industry.

Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

Additive manufacturing (AM) maintains a wide process window that enables complex designs otherwise unattainable via conventional production technologies. However, the lack of confidence in qualifying AM parts that leverage AM process–structure–property–performance (PSPP) relationships stymies design optimization and adoption of AM. While continuing efforts to map fundamental PSPP relationships that cover the potential design space, we first need pragmatic and then long-term solutions that overcome challenges associated with qualifying AM-designed parts. Two pragmatic solutions include: (1) AM material specifications to substantiate process reproducibility, and (2) component risk categorization to associate system risk relative to part performance and required part quality. A novel qualification paradigm under development involves efficient prediction of part performance over wide-ranging PSPP relationships through targeted testing and computational simulation. This paper describes projects at Sandia National Laboratories on PSPP relationship discovery, these pragmatic approaches, and the novel qualification approach.

High Energy Arcing Faults (HEAFs) are hazardous events in which an electrical arc leads to the rapid release of energy in the form of heat, vaporized metal, and mechanical force. In Nuclear Power Plants (NPPs), these events are often accompanied by loss of essential power and complicated shutdowns. To confirm the probabilistic risk analysis (PRA) methodology in NUREG/CR-6850, which was formulated based on limited observational data, the NRC led an international experimental campaign from 2014 to

In the rapidly evolving field of solar energy, Photovoltaic (PV) manufacturers are constantly challenged by the degradation of PV modules due to localized overheating, commonly known as hotspots. This issue not only reduce the efficiency of solar panels but, in severe cases, can lead to irreversible damage, malfunctioning, and even fire hazards. Addressing this critical challenge, our research introduces an innovative electronic device designed to effectively mitigate PV hotspots. This pioneering solution consists of a novel combination of a current comparator and a current mirror circuit. These components are uniquely integrated with an automatic switching mechanism, notably eliminating the need for traditional bypass diodes. We rigorously tested and validated this device on PV modules exhibiting both adjacent and non-adjacent hotspots. Our findings are groundbreaking: the hotspot temperatures were significantly reduced from a dangerous 55 °C to a safer 35 °C. Moreover, this intervention remarkably enhanced the output power of the modules by up to 5.3%. This research not only contributes a practical solution to a longstanding problem in solar panel efficiency but also opens new pathways for enhancing the safety and longevity of solar PV systems.

The Rock Valley fault zone (RVFZ), an intraplate strike-slip fault zone in the southern Nevada National Security Site (NNSS), hosted a series of very shallow (<3 km) earthquakes in 1993. The RVFZ may also have hydrological significance within the NNSS, potentially playing a role in regional groundwater flow, but there is a lack of local hydrological data. In the Spring of 2021, we collected active-source accelerated weight drop seismic data over part of the RVFZ to better characterize the shallow subsurface. We manually picked ∼17,000 P-wave travel times and over 14,000 S-wave travel times, which were inverted for P-wave velocity (VP), S-wave velocity (VS), and VP = VS ratio in a 3D joint tomographic inversion scheme. Seismic velocities are imaged as deep as ∼700 m in areas and generally align with geologic and structural expectations. VP and VS are relatively reduced near mapped and inferred faults, with the most prominent lower VP and VS zone around the densest collection of faults. We image VP = VS ratios ranging from ∼1.5 to ∼2.4, the extremes of which occur at a depth of ∼100 m and are juxtaposed across a fault. One possible interpretation of the imaged seismic velocities is enhanced fault damage near the densest collection of faults with relatively higher porosity and/or crack density at ∼100 m depth, with patches of semiperched groundwater present in the sedimentary rock in higher VP = VS areas and drier rock in lower VP = VS areas. A relatively higher VP = VS area beneath the densest faults persists at depth, which suggests percolation of groundwater via the fault damage zone to the regionally connected lower carbonate aquifer. Potentially, the presence and movement of groundwater may have played a role in the 1993 earthquake aftershocks.

Due to its tunable bandgap, anisotropic behavior, and superior thermoelectric properties, device applications using layered tellurene (Te) are becoming more attractive. Here, we report a thinning technique for exfoliated tellurene nanosheets using thermal annealing in an oxygen environment. We characterize different thinning parameters, including temperature and annealing time. Based on our measurements, we show that controlled layer thinning occurs in the narrow temperature range of 325-350 °C. We also show a reliable method to form β-tellurene oxide (β-TeO2), which is an emerging wide bandgap semiconductor with promising electronic and optoelectronic properties. This wide bandgap semiconductor exhibits a broad photoluminescence (PL) spectrum with multiple peaks covering the range of 1.76-2.08 eV. This PL emission, coupled with Raman spectra, is strong evidence of the formation of 2D β-TeO2. We discuss the results obtained and the mechanisms of Te thinning and β-TeO2 formation at different temperature regimes. We also discuss the optical bandgap of β-TeO2 and show the existence of pronounced excitonic effects evident by the large exciton binding energy in this 2D β-TeO2 system that reach 1.54-1.62 eV for bulk and monolayer, respectively. Our work can be utilized to have better control over the Te nanosheet thickness. It also sheds light on the formation of well-controlled β-TeO2 layered semiconductors for electronic and optoelectronic applications.

Sandia’s Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) is a computer code that solves the linear Boltzmann transport equation, particularly targeting coupled photon-electron problems. It uses unstructured finite element meshes in space, multigroup in energy, and discrete ordinates (Sn) or other methods in angle. SCEPTRE uses an xml-based input file to specify the problem. This report documents the options and syntax of that input file.

Abstract not provided.

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. We have implemented CSP in a C++ open-source library CSPlib1 using the Kokkos2 parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS. Program summary: Program Title: CSPlib CPC Library link to program files: https://doi.org/10.17632/p9gb7z54sp.1 Developer's repository link: https://github.com/sandialabs/csplib Licensing provisions: BSD 2-clause Programming language: C++ Nature of problem: Dynamical systems can involve coupled processes with a wide range of time scales. The computational singular perturbation (CSP) method offers a reformulation of these systems which enables the use of dynamically-based diagnostic tools to better comprehend the dynamics by decoupling fast and slow processes. CSPlib is an open-source software library for analyzing general ordinary differential equation (ODE) and differential algebraic equation (DAE) systems, with specialized implementations for detailed chemical kinetic ODE/DAE systems. It relies on CSP for the analysis of these systems. CSPlib has been used in gas kinetic and heterogeneous catalytic kinetic models. Solution method: CSP analysis seeks a set of basis vectors to linearly decompose the right-hand side (RHS) of a dynamical system in a manner that decouples fast and slow processes. The CSP basis vectors are often well approximated with the right eigenvectors of the RHS Jacobian. And the left basis vectors are found by the inversion of the matrix, whose columns are the CSP basis vectors. Accordingly, the right and left CSP basis vectors are orthonormal. CSP defines mode amplitudes as the projections of the left basis vectors on the RHS; the time scales as the reciprocals of the RHS Jacobian eigenvalue magnitudes; and the CSP pointers, which are the element-wise multiplication of the transpose of the right CSP basis vectors with the left CSP basis vectors. For kinetic models that can be cast as the product of a generalized stoichiometric matrix and a rate of progress vector, CSP defines the participation index, which represents the contribution of a chemical reaction to each mode. Further, it defines the slow and fast importance indices, which describe the contribution of a chemical reaction to the slow and fast dynamics of a state variable, respectively. These indices are useful in diagnostic studies of dynamical systems and the construction of simplified models. Additional comments including restrictions and unusual features: CSPlib is a portable library that carries out many CSP analyses in parallel and can be used in modern high-performance platforms.

This report introduces the radiative transfer equation, mean opacities, and why we need them. It also derives the Planck and Rosseland mean opacities, which are the most common mean opacities used in various applications.

We present an expression for the spectral gap, opening up new possibilities for performing and accelerating spectral calculations of quantum many-body systems. We develop and demonstrate one such possibility in the context of tensor network simulations. Our approach requires only minor modifications of the widely used simple update method and is computationally lightweight relative to other approaches. We validate it by computing spectral gaps of the 2D and 3D transverse-field Ising models and find strong agreement with previously reported perturbation theory results.

The properties of defects in n-p-n Si bipolar junction transistors (BJTs) caused by 17-MeV Si ions are investigated via current-voltage, low-frequency (LF) noise, and deep level transient spectroscopy (DLTS) measurements. Four prominent radiation-induced defects in the base-collector junction of these transistors are identified via DLTS. At least two defect levels are observed in temperature-dependent LF 1/f noise measurements, one that is similar to a prominent defect in DLTS and another that is not. Defect microstructures are discussed. Our results show that DLTS and 1/f noise measurements can provide complementary information about defects in linear bipolar devices.

We present a new optimization-based property-preserving algorithm for passive tracer transport. The algorithm utilizes a semi-Lagrangian approach based on incremental remapping of the mass and the total tracer. However, unlike traditional semi-Lagrangian schemes, which remap the density and the tracer mixing ratio through monotone reconstruction or flux correction, we utilize an optimization-based remapping that enforces conservation and local bounds as optimization constraints. In so doing we separate accuracy considerations from preservation of physical properties to obtain a conservative, second-order accurate transport scheme that also has a notion of optimality. Moreover, we prove that the optimization-based algorithm preserves linear relationships between tracer mixing ratios. We illustrate the properties of the new algorithm using a series of standard tracer transport test problems in a plane and on a sphere.

Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.

Long-duration energy storage (LDES) is critical to a stable, resilient, and decarbonized electric grid. While batteries are emerging as important LDES devices, extended, high-power discharges necessary for cost-competitive LDES present new materials challenges. Focusing on a new generation of low-temperature molten sodium batteries, we explore here unique phenomena related to long-duration discharge through a well-known solid electrolyte, NaSICON. Specifically, molten sodium symmetric cells at 110 °C were cycled at 0.1 A cm−2 for 1-23 h discharges. Longer discharges led to unstable overpotentials, reduced resistances, and decreased electrolyte strength, caused by massive sodium penetration not observed in shorter duration discharges. Scanning electron microscopy informed mechanisms of sodium penetration and even “healing” during shorter-duration cycling. Importantly, these findings show that traditional, low-capacity, shorter-duration tests may not sufficiently inform fundamental materials phenomena that will impact LDES battery performance. This case highlights the importance that candidate LDES batteries be tested under pertinent long-duration conditions.

Motivated by increasing interest in electrochemical devices that include highly alkaline electrolytes, we investigated two force fields for potassium hydroxide (KOH) at high concentrations in water. The “FNB” model uses the SPC/E water model, while the “FHM” model uses the TIP4P/2005 water model. Here, we also developed parameters to describe zincate ions in these solutions. The density and viscosity of KOH using the FHM model are in better agreement with experiment than the values from the FNB model. Comparing the properties of the zincate solutions to the available experimental data, we find that both force fields agree reasonably well, although the FHM parameters give a better prediction of the viscosity. The developed force field parameters can be used in future simulations of zincate/KOH solutions in combination with other species of interest.

Material design is increasingly used to realize desired functional properties, and the perovskite structure family is one of the richest and most diverse: perovskites are employed in many applications due to their structural flexibility and compositional diversity. Hexagonal, layered perovskite structures with chains of face-sharing transition metal oxide octahedra have attracted great interest as quantum materials due to their magnetic and electronic properties. Ba4MMn3O12, a member of the “12R” class of hexagonal, layered perovskites, contains trimers of face-sharing MnO6 octahedra that are linked by a corner-sharing, bridging MO6 octahedron. Here, we investigate cluster magnetism in the Mn3O12 trimers and the role of this bridging octahedron on the magnetic properties of two isostructural 12R materials by systematically changing the M4+ cation from nonmagnetic Ce4+ (f0) to magnetic Pr4+ (f1). We synthesized 12R-Ba4MMn3O12 (M= Ce, Pr) with high phase purity and characterized their low-temperature crystal structures and magnetic properties. Using substantially higher purity samples than previously reported, we confirm the frustrated antiferromagnetic ground state of 12R-Ba4PrMn3O12 below TN ≈ 7.75 K and explore the cluster magnetism of its Mn3O12 trimers. Despite being atomically isostructural with 12R-Ba4CeMn3O12, the f1 electron associated with Pr4+ causes much more complex magnetic properties in 12R-Ba4PrMn3O12. In 12R-Ba4PrMn3O12, we observe a sharp, likely antiferromagnetic transition at T2 ≈ 12.15 K and an additional transition at T1 ≈ 200 K, likely in canted antiferromagnetic order. These results suggest that careful variation of composition within the family of hexagonal, layered perovskites can be used to tune material properties using the complex role of the Pr4+ ion in magnetism.

The most complex challenges facing the world today comprise the work of [Department of Energy’s] (DOE’s) 17 National Laboratories: [...] From furthering U.S. energy independence and leadership in clean technologies; to promoting innovation that advances U.S. economic competitiveness; to conducting research of the highest caliber in the physical, chemical, biological, materials, computational, and information sciences to advance understanding of the world around us-the Laboratories’ purview is expansive and further their contributions are indispensable.

Custom-form factor batteries fabricated in non-conventional shapes can maximize the overall energy density of the systems they power, particularly when used in conjunction with energy dense materials (e.g., Li metal anodes and conversion cathodes). Additive manufacturing (AM), and specifically material extrusion (ME), have been shown as effective methods for producing custom-form cell components, particularly electrodes. However, the AM of several promising energy dense materials (conversion electrodes such as iron trifluoride) have yet to be demonstrated or optimized. Furthermore, the integration of multiple AM produced cell components, such as electrodes and separators, along with a custom package remains largely unexplored. In this work, iron trifluoride (FeF3) and ionogel (IG) separators are conformally printed using ME onto non-planar surfaces to enable the fabrication of custom-form Li-FeF3 batteries. To demonstrate printing on non-planar surfaces, cathodes and separators were deposited onto cylindrical rods using a 5-axis ME printer. ME printed FeF3 was shown to have performance commensurate with FeF3 cast using conventional means, both in coin cell and cylindrical rod formats, with capacities exceeding 700 mAh/g on the first cycle and ranging between 600 and 400 mAh/g over the next 50 cycles. Additionally, a ME process for printing polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) based IGs directly onto FeF3 is developed and enabled using an electrolyte exchange process. In coin cells, this process is shown to produce cells with similar capacity to cells built with Celgard separators out to 50 cycles, with the exception that cycling instabilities are observed during cycles 8–20. When using printed and exchanged IGs in a custom cylindrical cell package, 6 stable high-capacity cycles are achieved. Overall, this work demonstrates approaches for producing high-energy-density Li-FeF3 cells in coin and cylindrical rod formats, which are translatable to customized, arbitrary geometries compatible with ME printing and electrolyte exchange.

Manganese dioxide is a promising cathode material for energy storage applications because of its high redox potential, large theoretical energy density, abundance, and low cost. It has been shown that the performance of MnO₂ electrodes in rechargeable alkaline Zn/MnO₂ batteries could be improved by nanostructuring and by increasing the concentration of defects in MnO₂. However, the underlying mechanism of this improvement is not completely clear. We used an ab initio density functional computational approach to investigate the influence of nanostructuring and crystal defects on the electrochemical properties of the MnO₂ cathode material. The mechanism of electrochemical discharge of MnO₂ in Zn/MnO₂ batteries was studied by modeling the process of H ion insertion into the structures of pyrolusite, ramsdellite, and nsutite polymorphs containing oxygen vacancies, cation vacancies, and open surfaces. Our calculations showed that the binding energies of H ions inserted into the structures of MnO₂ polymorphs were strongly affected by the presence of surfaces and bulk defects. In particular, we found that the energies of H ions inserted under the surfaces and attached to the surfaces of MnO₂ crystals were significantly lower than those for bulk MnO₂. Furthermore, the results of our study provide an explanation for the influence of crystal defects and nanostructuring on the electrochemical reactivity of MnO₂ cathodes in rechargeable alkaline Zn/MnO₂ batteries.

Molecular dynamics simulations are used to test when the particle-in-cell (PIC) method applies to atmospheric pressure plasmas. It is found that PIC applies only when the plasma density and macroparticle weight are sufficiently small because of two effects associated with correlation heating. The first is the physical effect of disorder-induced heating (DIH). This occurs if the plasma density is large enough that a species (typically ions) is strongly correlated in the sense that the Coulomb coupling parameter exceeds one. In this situation, DIH causes ions to rapidly heat following ionization. PIC is not well suited to capture DIH because doing so requires using a macroparticle weight of one and a grid that well resolves the physical interparticle spacing. These criteria render PIC intractable for macroscale domains. The second effect is a numerical error due to Artificial Correlation Heating (ACH). ACH is like DIH in that it is caused by the Coulomb repulsion between particles, but differs in that it is a numerical effect caused by a macroparticle weight larger than one. Like DIH, it is associated with strong correlations. However, here the macroparticle coupling strength is found to scale as Γ w^2/3, where Γ is the physical coupling strength and w is the macroparticle weight. So even if the physical coupling strength of a species is small, as is expected for electrons in atmospheric pressure plasmas, a sufficiently large macroparticle weight can cause the macroparticles to be strongly coupled and therefore heat due to ACH. Furthermore, it is shown that simulations in reduced dimensions exacerbate these issues.

Continued dependence on crude oil and natural gas resources for fossil fuels has caused global atmospheric carbon dioxide (CO₂) emissions to increase to record-setting proportions. There is an urgent need for efficient and inexpensive carbon sequestration systems to mitigate large-scale CO₂ emissions from industrial flue gas. Carbonic anhydrase (CA) has shown high potential for enhanced CO₂ capture applications compared to conventional absorption-based methods currently utilized in various industrial settings. This study aims to understand structural aspects that contribute to the stability of CA enzymes critical for their applications in industrial processes, which require the ability to withstand conditions different from their native environments. Here, we evaluated the thermostability and enzyme activity of mesophilic and thermophilic CA variants at different temperature conditions and in the presence of atmospheric gas pollutants like nitrogen oxides (NO_x) and sulphur oxides (SO_x). Based on our enzyme activity assays and molecular dynamics simulations, we see increased conformational stability and CA activity levels in thermostable CA variants incubated week-long at different temperature conditions. The thermostable CA variants also retained high levels of CA activity despite changes in solution pH due to increasing NO_x and SO_x concentrations. Furthermore, a loss of CA activity was observed only at high concentrations of NO_x/SO_x that possibly can be minimized with appropriate buffered solutions.

As the field of low-dimensional materials (1D or 2D) grows and more complex and intriguing structures are continuing to be found, there is an emerging need for techniques to characterize the nanoscale mechanical properties of all kinds of 1D/2D materials, in particular in their most practical state: sitting on an underlying substrate. While traditional nanoindentation techniques cannot accurately determine the transverse Young's modulus at the necessary scale without large indentations depths and effects to and from the substrate, herein an atomic-force-microscopy-based modulated nanomechanical measurement technique with Angstrom-level resolution (MoNI/ÅI) is presented. This technique enables non-destructive measurements of the out-of-plane elasticity of ultra-thin materials with resolution sufficient to eliminate any contributions from the substrate. This method is used to elucidate the multi-layer stiffness dependence of graphene deposited via chemical vapor deposition and discover a peak transverse modulus in two-layer graphene. While MoNI/ÅI has been used toward great findings in the recent past, here all aspects of the implementation of the technique as well as the unique challenges in performing measurements at such small resolutions are encompassed.

Mode-locked vertical external cavity semiconductor lasers are a unique class of nonlinear dynamical systems driven far from equilibrium. We present a novel, to the best of our knowledge, experimental result, supported by rigorous microscopic simulations, of two coexisting mode-locked V-cavity configurations sourced by a common gain medium and operating as independent channels at angle controlled separated wavelengths. Microscopic simulations support pulses coincident on the common gain chip extracting photons from a nearby pair of coexisting kinetic holes burned in the carrier distributions.

Traditional methods of shielding fragile goods and human tissues from impact energy rely on isotropic foam materials. The mechanical properties of these foams are inferior to an emerging class of metamaterials called plate lattices, which have predominantly been fabricated in simple 2.5-dimensional geometries using conventional methods that constrain the feasible design space. In this work, additive manufacturing is used to relax these constraints and realize plate lattice metamaterials with nontrivial, locally varying geometry. The limitations of traditional computer-aided design tools are circumvented and allow the simulation of complex buckling and collapse behaviors without a manual meshing step. By validating these simulations against experimental data from tests on fabricated samples, sweeping exploration of the plate lattice design space is enabled. Numerical and experimental tests demonstrate plate lattices absorb up to six times more impact energy at equivalent densities relative to foams and shield objects from impacts ten times more energetic while transmitting equivalent peak stresses. In contrast to previous investigations of plate lattice metamaterials, designs with nonuniform geometric prebuckling in the out-of-plane direction is explored and showed that these designs exhibit 10% higher energy absorption efficiency on average and 25% higher in the highest-performing design.

The effect of doping concentration on the temperature performance of the novel split-well resonant-phonon (SWRP) terahertz quantum-cascade laser (THz QCL) scheme supporting a clean 4-level system design was analyzed using non-equilibrium Green’s functions (NEGF) calculations. Experimental research showed that increasing the doping concentration in these designs led to better results compared to the split-well direct-phonon (SWDP) design, which has a larger overlap between its active laser states and the doping profile. However, further improvement in the temperature performance was expected, which led us to assume there was an increased gain and line broadening when increasing the doping concentration despite the reduced overlap between the doped region and the active laser states. Through simulations based on NEGF calculations we were able to study the contribution of the different scattering mechanisms on the performance of these devices. We concluded that the main mechanism affecting the lasers’ temperature performance is electron-electron (e-e) scattering, which largely contributes to gain and line broadening. Interestingly, this scattering mechanism is independent of the doping location, making efforts to reduce overlap between the doped region and the active laser states less effective. Optimization of the e-e scattering thus could be reached only by fine tuning of the doping density in the devices. By uncovering the subtle relationship between doping density and e-e scattering strength, our study not only provides a comprehensive understanding of the underlying physics but also offers a strategic pathway for overcoming current limitations. This work is significant not only for its implications on specific devices but also for its potential to drive advancements in the entire THz QCL field, demonstrating the crucial role of e-e scattering in limiting temperature performance and providing essential knowledge for pushing THz QCLs to new temperature heights.

Porous liquids (PLs), which are solvent-based systems that contain permanent porosity due to the incorporation of a solid porous host, are of significant interest for the capture of greenhouse gases, including CO2. Type 3 PLs formed by using metal-organic frameworks (MOFs) as the nanoporous host provide a high degree of chemical turnability for gas capture. However, pore aperture fluctuation, such as gate-opening in zeolitic imidazole framework (ZIF) MOFs, complicates the ability to keep the MOF pores available for gas adsorption. Therefore, an understanding of the solvent molecular size required to ensure exclusion from MOFs in ZIF-based Type 3 PLs is needed. Through a combined computational and experimental approach, the solvent-pore accessibility of exemplar MOF ZIF-8 was examined. Density functional theory (DFT) calculations identified that the lowest-energy solvent-ZIF interaction occurred at the pore aperture. Experimental density measurements of ZIF-8 dispersed in various-sized solvents showed that ZIF-8 adsorbed solvent molecules up to 2 Å larger than the crystallographic pore aperture. Density analysis of ZIF dispersions was further applied to a series of possible ZIF-based PLs, including ZIF-67, −69, −71(RHO), and −71(SOD), to examine the structure-property relationships governing solvent exclusion, which identified eight new ZIF-based Type 3 PL compositions. Solvent exclusion was driven by pore aperture expansion across all ZIFs, and the degree of expansion, as well as water exclusion, was influenced by ligand functionalization. Using these results, a design principle was formulated to guide the formation of future ZIF-based Type 3 PLs that ensures solvent-free pores and availability for gas adsorption.

The need for clean, renewable energy has driven the expansion of renewable energy generators, such as wind and solar. However, to achieve a robust and responsive electrical grid based on such inherently intermittent renewable energy sources, grid-scale energy storage is essential. The unmet need for this critical component has motivated extensive grid-scale battery research, especially exploring chemistries “beyond Li-ion”. Among others, molten sodium (Na) batteries, which date back to the 1960s with Na-S, have seen a strong revival, owing mostly to raw material abundance and the excellent electrochemical properties of Na metal. Recently, many groups have demonstrated important advances in battery chemistries, electrolytes, and interfaces to lower material and operating costs, enhance cyclability, and understand key mechanisms that drive failure in molten Na batteries. For widespread implementation of molten Na batteries, though, further optimization, cost reduction, and mechanistic insight is necessary. In this light, this work provides a brief history of mature molten Na technologies, a comprehensive review of recent progress, and explores possibilities for future advancements.

Cytokines and acute-phase proteins are promising biomarkers for inflammatory disease. Despite its potential, early diagnosis based on these biomarkers remains challenging without technology enabling highly sensitive protein detection immediately after sample collection, because of the low abundance and short half-life of these proteins in bodily fluids. Enzyme-linked immunosorbent assay (ELISA) is a gold-standard method for such protein analysis, but it often requires labor-intensive and time-consuming sample handling and as well as a bulky benchtop platereader, limiting its utility in the clinical site. We developed a portable microfluidic immunoassay device capable of sensitive, quantitative, and high-throughput protein detection at point-of-need. The portable microfluidic system performs eight magnetic bead-based sandwich immunoassays from raw samples in 40 min. An innovative bead actuation strategy was incorporated into the system to automate multiple sample handling steps with minimal user intervention. The device enables quantitative protein analysis with picomolar sensitivity, as demonstrated using human samples spiked with interleukin-6 and C-reactive protein. The affinity-based assays are highly specific to the target without cross-reactivity. Therefore, we envision the reported device offering ultrasensitive and field-deployable immunoassay tests for timely and accurate clinical diagnosis.

Metal-organic frameworks (MOFs) are a class of porous, crystalline materials that have been systematically developed for a broad range of applications. Incorporation of two or more metals into a single crystalline phase to generate heterometallic MOFs has been shown to lead to synergistic effects, in which the whole is oftentimes greater than the sum of its parts. Because geometric proximity is typically required for metals to function cooperatively, deciphering and controlling metal distributions in heterometallic MOFs is crucial to establish structure-function relationships. However, determination of short- and long-range metal distributions is nontrivial and requires the use of specialized characterization techniques. Advancements in the characterization of metal distributions and interactions at these length scales is key to rapid advancement and rational design of functional heterometallic MOFs. This perspective summarizes the state-of-the-art in the characterization of heterometallic MOFs, with a focus on techniques that allow metal distributions to be better understood. Using complementary analyses, in conjunction with computational methods, is critical as this field moves toward increasingly complex, multifunctional systems.

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.

We report a comparative study of three rectifying gate metals, W, Pd, and Pt/Au, on ultrawide bandgap Al0.86Ga0.14N barrier/Al0.7Ga0.3N channel high electron mobility transistors for use in extreme temperatures. The transistors were electrically characterized from 30 to 600 °C in air. Of the three gate metals, the Pt/Au stack exhibited the smallest change in threshold voltage (0.15 V, or 9% change between the 30 and 600 °C values, and a maximum change of 42%), the highest on/off current ratio (1.5 × 106) at 600 °C, and a modest forward gate leakage current (0.39 mA/mm for a 3 V gate bias) at 600 °C. These favorable results showcase AlGaN channel high electron mobility transistors' ability to operate in extreme temperature environments.

Current nuclear facility emergency planning zones (EPZs) are based on outdated distance-based criteria, predating comprehensive dose and risk-informed frameworks. Recent advancements in simulation tools have permitted the development of site-specific, dose, and risk-based consequence-driven assessment frameworks. This study investigated the computation of advanced reactor (AR) EPZs using two atmospheric dispersion models: a straight-line Gaussian plume model (GPM) and a semi-Lagrangian Particle in Cell (PIC). Two case studies were conducted: (1) benchmarking the NRC SOARCA study for the Peach Bottom Nuclear Generating Station and (2) analyzing an advanced INL Heat Pipe Design A microreactor's end-of-cycle inventory. The dose criteria for both cases were 10 mSv at mean weather conditions and 50 mSv at 95th percentile weather conditions at 96 h post-release. Results demonstrated that GPM and PIC estimated similar mean peak dose levels for large boiling water reactors in the farfield case, placing EPZ limits beyond current regulations. For ARs with source terms remaining in the nearfield, PIC modeling without specific nearfield considerations could result in excessively high doses and inaccurate EPZ designations. PIC dispersion demonstrated an order of magnitude higher estimate of nearfield inhalation dose contribution when compared to GPM results. Both models significantly reduced EPZ sizing within the nearfield. Thus, reductions in the AR source term may eliminate the need for a separate EPZ.

Laccases from white-rot fungi catalyze lignin depolymerization, a critical first step to upgrading lignin to valuable biodiesel fuels and chemicals. In this study, a wildtype laccase from the basidiomycete Fomitiporia mediterranea (Fom_lac) and a variant engineered to have a carbohydrate-binding module (Fom_CBM) were studied for their ability to catalyze cleavage of β-O-4′ ether and C–C bonds in phenolic and non-phenolic lignin dimers using a nanostructure-initiator mass spectrometry-based assay. Fom_lac and Fom_CBM catalyze β-O-4′ ether and C–C bond breaking, with higher activity under acidic conditions (pH < 6). The potential of Fom_lac and Fom_CBM to enhance saccharification yields from untreated and ionic liquid pretreated pine was also investigated. Adding Fom_CBM to mixtures of cellulases and hemicellulases improved sugar yields by 140% on untreated pine and 32% on cholinium lysinate pretreated pine when compared to the inclusion of Fom_lac to the same mixtures. Adding either Fom_lac or Fom_CBM to mixtures of cellulases and hemicellulases effectively accelerates enzymatic hydrolysis, demonstrating its potential applications for lignocellulose valorization. We postulate that additional increases in sugar yields for the Fom_CBM enzyme mixtures were due to Fom_CBM being brought more proximal to lignin through binding to either cellulose or lignin itself.

Measurement