Publications

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.

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

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.

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.

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.

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)%.

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.

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.

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.

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.

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.

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.

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.