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DOE OSTI2024

Non-mercury methylating microbial taxa are integral to understanding links between mercury methylation and elemental cycles in marine and freshwater sediments

The goal of this study was to explore the role of non-mercury (Hg) methylating taxa in mercury methylation and to identify potential links between elemental cycles and Hg methylation. Statistical approaches were utilized to investigate the microbial community and biochemical functions in relation to methylmercury (MeHg) concentrations in marine and freshwater sediments. Sediments were collected from the methylation zone (top 15 cm) in four Hg-contaminated sites. Both abiotic (e.g., sulfate, sulfide, iron, salinity, total organic matter, etc.) and biotic factors (e.g., hgcA, abundances of methylating and non-methylating taxa) were quantified. Random forest and stepwise regression were performed to assess whether non-methylating taxa were significantly associated with MeHg concentration. Co-occurrence and functional network analyses were constructed to explore associations between taxa by examining microbial community structure, composition, and biochemical functions across sites. Regression analysis showed that approximately 80% of the variability in sediment MeHg concentration was predicted by total mercury concentration, the abundances of Hg methylating taxa, and the abundances of the non-Hg methylating taxa. The co-occurrence networks identified Paludibacteraceae and Syntrophorhabdaceae as keystone non Hg methylating taxa in multiple sites, indicating the potential for syntrophic interactions with Hg methylators. Strong associations were also observed between methanogens and sulfate-reducing bacteria, which were likely symbiotic associations. The functional network results suggested that non-Hg methylating taxa play important roles in sulfur respiration, nitrogen respiration, and the carbon metabolism-related functions methylotrophy, methanotrophy, and chemoheterotrophy. Interestingly, keystone functions varied by site and did not involve carbon- and sulfur-related functions only. In conclusion, our findings highlight associations between methylating and non-methylating taxa and sulfur, carbon, and nitrogen cycles in sediment methylation zones, with implications for predicting and understanding the impact of climate and land/sea use changes on Hg methylation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Controllable growth of interpenetrating or random copolymer networks

Interpenetrating and random copolymer networks are vital in a number of industrial applications, including the fabrication of automotive parts, damping materials, and tissue engineering scaffolds. We develop a theoretical model for a process that enables the controlled growth of interpenetrating network (IPNs), or a random copolymer network (RCN) of specified size and mechanical properties. In this process, a primary gel “seed” is immersed into a solution containing the secondary monomer and crosslinkers. After the latter species are absorbed into the primary network, the absorbed monomers are polymerized to form the secondary polymer chains, which then can undergo further crosslinking to form an IPN, or undergo inter-chain exchange with the existing network to form a RCN. The swelling and elastic properties of the IPN and RCN networks can be tailored by modifying the monomer and crosslinker concentrations in the surrounding solution, or by tuning the enthalpic interactions between the primary polymer, secondary monomer and solvent through a proper choice of chemistry. This process can be used repeatedly to fabricate gels with a range of mechanical properties from stiff, rigid materials to soft, flexible networks, allowing the method to meet the materials requirements of a variety of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population’s mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Quantum entanglement in nuclear fission

Nuclear fission presents a unique example of quantum entanglement in strongly interacting many-body systems. A heavy nucleus can split into hundreds of combinations of two complementary fragments in the fission process. The entanglement of fragment wave functions is persistent even after separation and impacts the partition of particles and energies between fragments. Based on microscopic dynamical calculations of the fission of 240 Pu, this work finds that dynamical quantum entanglement is indispensable in the appearance of sawtooth distributions of average excitation energies of fragments and thus neutron multiplicities, but not in average neutron excess of fragments. Both sawtooth slopes from particle-number projections are found to be steep – a feature which can be alleviated by random fluctuations. The persistent entanglement is mainly due to non-adiabatic dynamics since the final splitting is so fast that the non-localization of wave functions is kept during the separation. These findings may impact the understanding of quantum entanglement more broadly in mesoscopic systems

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Spectral fluctuations in the Sachdev-Ye-Kitaev model

We present a detailed quantitative analysis of spectral correlations in the Sachdev-Ye-Kitaev (SYK) model. We find that the deviations from universal Random Matrix Theory (RMT) behavior are due to a small number of long-wavelength fluctuations (of the order of the number of Majorana fermions N ) from one realization of the ensemble to the next one. These modes can be parameterized effectively in terms of Q-Hermite orthogonal polynomials, the main contribution being due to scale fluctuations for which we give a simple analytical estimate. Our numerical results for N = 32 show that only the lowest eight polynomials are needed to eliminate the nonuniversal part of the spectral fluctuations. The covariance matrix of the coefficients of this expansion can be obtained analytically from low-order double-trace moments. We evaluate the covariance matrix of the first six moments and find that it agrees with the numerics. We also analyze the spectral correlations in terms of a nonlinear σ-model, which is derived through a Fierz transformation, and evaluate the one and two-point spectral correlation functions to two-loop order. The wide correlator is given by the sum of the universal RMT result and corrections whose lowest-order term corresponds to scale fluctuations. However, the loop expansion of the σ-model results in an ill-behaved expansion of the resolvent, and it gives universal RMT fluctuations not only for q= 4 or higher even q-body interactions, but also for the q= 2 SYK model albeit with a much smaller Thouless energy while the correct result in this case should have been Poisson statistics. In our numerical studies we analyze the number variance and spectral form factor for N = 32 and q = 4. We show that the quadratic deviation of the number variance for large energies appears as a peak for small times in the spectral form factor. After eliminating the long-wavelength fluctuations, we find quantitative agreement with RMT up to an exponentially large number of level spacings for the number variance or exponentially short times in the case of the spectral form factor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Janus skyrmion: Interfacial quasiparticle with two-faced helicity

Janus particles are functional particles with at least two surfaces showing asymmetric properties. Here, we show at the interface between two magnetic regions with different antisymmetric exchange interactions, an alternative species of two-dimensional topological quasiparticles can emerge, in which different helicity structures can coexist. We name such an interfacial quasiparticle a “Janus skyrmion,” in analogy to the Janus particle. As the Janus skyrmion shows helicity asymmetry, its size could vary with both the in-plane and out-of-plane magnetic fields. A vertical spin current could drive the Janus skyrmion into one-dimensional motion along the interface without showing the skyrmion Hall effect, at a speed which depends on both the in-plane spin-polarization direction and current density. Thermal fluctuations could also lead to one-dimensional random walk of a Brownian Janus skyrmion. This work uncovers unique dynamics intrinsic to interfacial quasiparticles with exotic helicity, which may be realized in interface-engineered magnetic layers.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Machine learning applied to classifying neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this project, we apply a machine learning technique, namely decision trees, to automate the quantum number assignments. The tree is trained from simulated data generated to mimic the errors found in real data. We explore the use of several physics-motivated features for training our trees, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results using random matrix theory motivated fits which demonstrated that we can determine resonance spin groups somewhat reliably. If we use these fits as features in our trees, we can train them to spot outliers corresponding to misassigned resonances. We found that with the large number of features used in this project that the decision tree tended to over t training data resulting in poor performance with respect to the test data. By reducing the number of features, we can achieve nearly perfect assignment of quantum numbers with our training data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Impact of hydrogen bonding pendant groups in polymer grafted nanoparticles on interlayer adhesion and mechanical properties in material extrusion printing

The addition of nanoparticles or copolymer grafted nanoparticles (CPGNPs) to polymeric matrices greatly improves thermomechanical properties of the resulting nanocomposite, but corresponding studies of nanocomposite systems created by 3D printing are few, especially in the realm of functional polymeric materials. Here we describe how silica nanoparticle-grafted, random copolymers of poly(methyl methacrylate-random-2-uriedo-[1H]-pyrimidinone methacrylate) (P(MMA-r-UPyMA) dramatically increases the mechanical properties of poly(methyl methacrylate) (PMMA) based nanocomposite specimens created by melt extrusion printing. Most notably, when these novel CPGNPs are combined with PMMA matrix chains via a solution-based process, printed specimens containing only 0.5 wt% additive show significant increases in Young’s modulus (90%), storage modulus (93%), tensile modulus (148%) and ultimate tensile strength (110%). These improvements are ascribed to strengthening of adhesion across interfaces due to multi-point hydrogen bonding between UPyMA groups, the reinforcement effect of the P(MMA-r-UPyMA)-grafted silica nanoparticles, as well as hydrogen bonding interactions and entanglements between graft and matrix chains. Imaging of fracture surfaces after tensile testing reveals that in comparison to nanocomposites created by simple mechanical mixing of solids, the solution-casting process improves the dispersion of nanoparticles and reduces the void spaces between printed roads. These studies demonstrate that introducing functionality into polymer grafts, such as hydrogen bonding interactions, and intimate mixing of polymer-modified nanomaterials can greatly improve interlayer adhesion and mechanical properties, thereby advancing this method of polymer additive manufacturing

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Smooth periodic gauge satisfying crystal symmetry and periodicity to study high-harmonic generation in solids

Intense lasers can easily drive nonadiabatic transitions of excited electron wave packets across the Brillouin zones, thus transition dipole moments (TDM) between energy bands of solids should be continuous, satisfying crystal symmetry, and periodic at zone boundaries. While current ab initio algorithms are powerful in calculating band structures of solids, they all introduced random phases into the eigenfunctions at each crystal momentum k. In this work, we show how to choose a “smooth-periodic” gauge where TDMs can be smooth versus k, preserving crystal symmetry, as well as maintaining periodic at boundaries. The symmetry properties of TDMs with respect to k ensure the absence of even-order harmonics from MgO with inversion symmetry, while the TDM in the “smooth-periodic” gauge for broken-symmetry ZnO is responsible for even harmonics that were underestimated in previous simulations. These results reveal the importance of correctly treating the complex TDMs that satisfy crystal symmetry and continuous across zone boundaries in nonlinear laser-solid interactions, which has been elusive in most theories so far.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Nitrogen Metabolism in Pseudomonas putida : Functional Analysis Using Random Barcode Transposon Sequencing

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop-out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. So we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Noise-aware circuit compilations for a continuously parameterized two-qubit gateset

State-of-the-art noisy-intermediate-scale quantum processors are currently implemented across a variety of hardware platforms, each with their own distinct gatesets. As such, circuit compilation should not only be aware of but also deeply connect to the native gateset and noise properties of each. Trapped-ion processors are one such platform that provides a gateset that can be continuously parameterized across both one- and two-qubit gates. Here we use the Quantum Scientific Computing Open User Testbed to study noise-aware compilations focused on continuously parameterized two-qubit 𝑍⁢𝑍 gates (based on the Mølmer-Sørensen interaction) using $\scriptsize{SUPERSTAQ}$, a quantum software platform for hardware-aware circuit compiler optimizations. We discuss the realization of 𝑍⁢𝑍 gates with arbitrary angle on the all-to-all connected trapped-ion system. Then we discuss a variety of different compiler optimizations that innately target these 𝑍⁢𝑍 gates and their noise properties. These optimizations include moving from a restricted maximally entangling gateset to a continuously parameterized one, swap mirroring to further reduce the total entangling angle of the operations, focusing the heaviest 𝑍⁢𝑍 angle participation on the best-performing gate pairs, and circuit approximation to remove the least impactful 𝑍⁢𝑍 gates. We demonstrate these compilation approaches on the hardware with randomized quantum volume circuits, observing the potential to realize a larger quantum volume as a result of these optimizations. Using differing yet complementary analysis techniques, we observe the distinct improvements in system performance provided by these noise-aware compilations and study the role of stochastic and coherent error channels for each compilation choice.

Noise↗

DOE OSTI2022

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Experimental observation of magnetic dimers in diluted Yb:YAlO 3

In this paper, we present a comprehensive experimental investigation of Yb magnetic dimers in Yb 0.04 Y 0.96 AlO 3 , an Yb-doped yttrium aluminum perovskite YAlO3, by means of specific heat, magnetization, and high-resolution inelastic neutron scattering (INS) measurements. In our sample, the Yb ions are randomly distributed over the lattice and ~7% of Yb ions form quantum dimers due to nearest-neighbor antiferromagnetic coupling along the c axis. At zero field, the dimer formation manifests itself in an appearance of an inelastic peak at Δ ≈ 0.2 meV in the INS spectrum and a Schottky-like anomaly in the specific heat. The structure factor of the INS peak exhibits a cosine modulation along the $L$ direction, in agreement with the $c$-axis nearest-neighbor intradimer coupling. A careful fitting of the low-temperature specific heat shows that the excited state is a degenerate triplet, which indicates a surprisingly small anisotropy of the effective Yb-Yb exchange interaction despite the low crystal symmetry and anisotropic magnetic dipole contribution, in agreement with previous reports for the Yb parent compound, YbAlO 3 , and in contrast to Yb 2 Pt 2 Pb. The obtained results are precisely reproduced by analytical calculations for the Yb dimers.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Magnetic order and disorder in a quasi-two-dimensional quantum Heisenberg antiferromagnet with randomized exchange

In this work, we present an investigation of the effect of randomizing exchange coupling strengths in the S = 1 / 2 square lattice quasi-two-dimensional quantum Heisenberg antiferromagnet (QHAF) ( QuinH ) 2 Cu ( Cl x Br 1 - x ) 4 · 2 H 2 O (QuinH = Quinolinium, C 9 H 8 N + ), with 0 ≤ x ≤ 1 . Pulsed-field magnetization measurements allow us to estimate an effective in-plane exchange strength J in a regime where exchange fosters short-range order, while the temperature T N at which long-range order (LRO) occurs is found using muon-spin relaxation, allowing us to construct a phase diagram for the series. We evaluate the effectiveness of disorder in suppressing T N and the ordered moment size, and we find an extended disordered phase in the region 0.4 ≲ x ≲ 0.8 where no magnetic order occurs. The observed critical substitution levels are accounted for by an energetics-based competition between different local magnetic orders. Furthermore, we demonstrate experimentally that the ground-state disorder is driven by quantum effects of the exchange randomness, which is a feature that has been predicted theoretically and has implications for other disordered quasi-two-dimensional QHAFs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Cooling and dietary crude protein affected milk production on heat-stressed dairy cows

Environmental factors, such as temperature and humidity, exert dramatic effects on the production, health, and well-being of lactating dairy cows. Heat-stressed cows undergo a series of homeorhetic adaptations that alters cellular homeostasis and nutrient utilization. As such, the objective of this study was to determine the effects of cooling and dietary crude protein (CP) content on fermentability of rumen inoculum, milk production, and health of heat-stressed cows. It was hypothesized that cooled but not heat-stressed cows would support productivity in response to an increase in dietary CP content. Thirty-six multiparous Holstein cows were randomly assigned to 1 of 4 treatments in a 2 × 2 factorial arrangement (9 cows/treatment). Treatments consisted of cooling (CO) or moderate long-term heat stress (HS) and diets containing low CP content (LCP; 12.5% CP) or moderate CP content (MCP; 16.1% CP) of dry matter. The CO treatment provided heat abatement with sprinklers and fans that came on at > 20.0°C, whereas the HS treatment did not provide sprinklers and fans. Cows were housed in pens in a freestall barn, and treatments were imposed for 21 d during July and August. Compared with the HS treatment, CO decreased afternoon rectal and vaginal temperatures (0.60 and 0.70°C) and afternoon respiration rate (27.6 breaths/min). Treatment interactions showed that compared with the LCP treatment, MCP increased milk fat yield (23%) in CO but not in HS cows. Compared with the LCP treatment, MCP decreased milk protein percent (0.14 units) in CO but increased milk protein percent (0.09 units) in HS cows. Rumen inoculum collected on d 21 showed a ~3-fold increase in total gas production in vitro in CO compared with the HS treatment. Relative to the LCP treatment, MCP increased plasma glucose and insulin and decreased total free fatty acids concentrations. In summary, under the conditions of this study CO but not HS cows responded to a CP stimulus by increasing productivity.

59 BASIC BIOLOGICAL SCIENCES↗