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DOE OSTI2020

Quantum Joule expansion of one-dimensional systems

Here, we investigate the Joule expansion of nonintegrable quantum systems that contain bosons or spinless fermions in one-dimensional lattices. A barrier initially confines the particles to be in half of the system in a thermal state described by the canonical ensemble and is removed at time t = 0. We investigate the properties of the time-evolved density matrix, the diagonal ensemble density matrix, and the corresponding canonical ensemble density matrix with an effective temperature determined by the total energy conservation using exact diagonalization. The weights for the diagonal ensemble and the canonical ensemble match well for high initial temperatures that correspond to negative effective final temperatures after the expansion. At long times after the barrier is removed, the time-evolved Rényi entropy of subsystems bigger than half can equilibrate to the thermal entropy with exponentially small fluctuations. The time-evolved reduced density matrix at long times can be approximated by a thermal density matrix for small subsystems. Few-body observables, like the momentum distribution function, can be approximated by a thermal expectation of the canonical ensemble with strongly suppressed fluctuations. The negative effective temperatures for finite systems go to non-negative temperatures in the thermodynamic limit for bosons, but there is a true thermodynamic effect for fermions, which is confirmed by finite-temperature density matrix renormalization group calculations. We propose the Joule expansion as a way to dynamically create negative temperature states for fermion systems with repulsive interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Electrically Conductive Amine Functionalized Reduced Graphite Oxide Foam for CO 2 Removal from the Air

Rapid regeneration of CO 2 adsorbents is critical to improving the productivity of direct air capture (DAC) systems. In this study, we codesigned a material to have appropriate electrical conductivity and CO 2 adsorption properties to enable efficient CO 2 capture from air. Specifically, we present a poly(ethylenimine) (PEI)-impregnated thermally annealed graphite oxide (TAGO900) foam adsorbent tailored for vacuum-assisted electrically driven thermal swing adsorption (V-ETSA). This structured adsorbent leverages the high electrical conductivity of the reduced graphite oxide framework to enable fast and direct heating of the adsorbent material by electrical resistance heating (Joule heating). An optimal sample, 40 wt % PEI (molecular weight 25k) impregnated TAGO900, shows the best balance between adsorption capacity (1.54 mmol g –1 ) and adsorption rates (0.016 mmol g –1 min –1 ) using fixed bed breakthrough experiments at 25 °C and 70% RH using 50 sccm 400 ppm of CO 2 /N 2 flow. Compared to conventional temperature vacuum swing adsorption (TVSA), the V-ETSA approach achieves substantially faster CO 2 desorption, achieving average desorption rates (including cooling time) of 0.09 mmol g –1 min –1 ─approximately 2.5 times faster than TVSA under similar operating conditions. The maximum desorption rate reaches 0.23 mmol/g/min during the desorption stage. These results underscore the importance of the direct heating strategy, such as Joule heating, for fast and highly productive vacuum swing adsorption in DAC systems.

amines↗

DOE OSTI2020

Materials for the biorefinery: high bio-content, shape memory Kraft lignin-derived non-isocyanate polyurethane foams using a non-toxic protocol

Polyurethanes are among the top six polymers produced in the world and are widely used in the automotive, furniture, construction and appliance industry for their light weight, impact resistance, and insulating properties. However, the use of hazardous diisocyanates used in polyurethane formulations has led many to search for more sustainable alternatives. The lignin component of biomass has been targeted to replace the often toxic and petroleum-derived precursors to polymer synthesis in support of the biorefinery concept where natural materials are used as the feedstock for commodity plastics. The use of lignin is often hampered due to its low reactivity, heterogeneity and the necessity of employing extensive purification and/or functionalization measures to ensure materials are of comparable quality. In this report, a unique method is presented for the synthesis and processing of a non-toxic, non-isocyanate polyurethane foam utilizing unmodified Kraft lignin and a biobased curing agent. Here, raw Kraft lignin is functionalized with green organic carbonates and a non-toxic approach is taken to solubilize the precursors with a curing agent from renewable fatty acids enabling rapid gel-times. For the first time, a NIPU foam from lignin is synthesized while the structure–property relationship of different reaction mixtures is studied demonstrating shape memory capacity and 100% biobased carbon content.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Collapse of Jahn-Teller phonons in La 1−x Sr x MnO 3 with weak magnetoresistance

Perovskite manganites are quantum materials exhibiting competing interactions inducing colossal magnetoresistance (CMR). The prevailing theory of CMR highlights the essential role of electron-phonon coupling (EPC), but mounting evidence suggests the underlying mechanism is more complicated. Here, we investigate phonons and spin-phonon coupling in ferromagnetic CMR manganites La 1−x Sr x MnO 3 (x=0.2,0.3) with relatively small CMR associated with melting of the magnetic order above room temperature. High-resolution neutron scattering experiments combined with density functional theory (DFT) show that the low-temperature ferromagnetic phase is conventional: neutron scattering from phonons agrees with DFT predictions and magnons follow sinusoidal dispersions. Fluctuating magnetic moments and low-energy phonons remain conventional in the high-temperature paramagnetic phase, indicating the Mn and La/Sr sublattices are not strongly perturbed by melting of ferromagnetism. In contrast, the Jahn–Teller-active optical oxygen vibrations collapse entirely above the Curie temperature, despite low CMR in these compositions, with some of the lost spectral weight reappearing as quasielastic scattering. We attribute this highly anomalous behavior to giant EPC in the charge and/or orbital channel. It drives cooperative diffusive motion of quasistatic carrier-trapping oxygen sublattice distortions once ferromagnetism disappears. We hypothesize the magnitude of magnetoresistance correlates with the rate of diffusion rather than with the strength of Jahn–Teller EPC.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis

Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Multimode theory of electron hole transverse instability

We present Vlasov–Poisson three-dimensional linear stability analysis of an initially planar electron hole structure, solving for the distribution function by integration along unperturbed orbits. The non-sinusoidal potential perturbation shape (parallel to $B$ ) is expanded in eigenfunctions of the adiabatic Poisson operator, generalizing the prior assumption of a rigid shift of the equilibrium. We show that the shiftmode is then modified by a second discrete mode plus an integral over a continuum of wave-like modes. A rigorous treatment shows that the continuum can be approximated effectively by a single mode that satisfies the external wave dispersion relation, thus making the perturbation a weighted sum of three modes. We find numerically the solution for the complex instability frequency, and the corresponding three mode amplitudes determining the perturbation eigenmode. This multimode analysis refines the accuracy of the prior single-mode results, giving slightly higher growth rates at most parameters, as expected from the extra mode shape freedom. Oscillating modes near stability boundaries have larger mode distortions which help explain particle-in-cell simulations that observe instability up to ${\sim }20$ % beyond the prior shiftmode thresholds, and narrowing of the perturbation. At high magnetic field, the multimode analysis predicts a reduction of the already small growth rate.

Physics↗

DOE OSTI2025

Colistin resistance plasmids dually enhance bacterial virulence and antibiotic resistance via surface polysaccharide biosynthesis

Plasmids carrying the mobilized colistin-resistance gene mcr-1 are prevalent among multidrug-resistant Gram-negative pathogens, yet their broad impact on bacterial physiology and virulence remains unclear. Here, we demonstrate that acquisition of an mcr-1 plasmid concurrently increases antimicrobial resistance and pathogenicity in Escherichia coli. On the same plasmid, the XRE-family transcriptional regulator EcaR cooperates with MCR-1 to activate the wec operon, driving biosynthesis of two surface polysaccharides: enterobacterial common antigen (ECA) and a high-molecular-weight O-chain. Expression of these surface polysaccharides increases bile resistance and virulence in a murine model and further elevates colistin resistance. MCR-1 enhances transcription of upstream genes in the wec operon, whereas EcaR directly activates an internal promoter (PwecE) to induce downstream gene expression. Thus, both components are required for surface polysaccharide expression, and deletion of either abolishes the phenotype. Genomic analysis of publicly available mcr plasmids reveals widespread co-occurrence of mcr-1 and ecaR on IncI2 and IncX4 plasmids, indicating their functional complementarity. These findings uncover a mechanism by which resistance plasmids remodel the bacterial surface, linking horizontal gene transfer to coordinated regulation of antimicrobial resistance and virulence.

Antimicrobial resistance↗

DOE OSTI2026

Correlation function metrology for warm dense matter: Recent developments and practical guidelines

X-ray Thomson scattering (XRTS) has emerged as a valuable diagnostic for matter under extreme conditions, as it captures the intricate many-body physics of the probed sample. Recent advances, such as the model-free temperature diagnostic of Dornheim et al. [Nat. Commun. 13 , 7911 (2022)], have demonstrated how much information can be extracted directly within the imaginary-time formalism. However, since the imaginary-time formalism is a concept often difficult to grasp, we provide here a systematic overview of its theoretical foundations and explicitly demonstrate its practical applications to temperature inference, including relevant subtleties. Furthermore, we present recent developments that enable the determination of the absolute normalization, Rayleigh weight, and density from XRTS measurements without reliance on uncontrolled model assumptions. Finally, we outline a unified workflow that guides the extraction of these key observables, offering a practical framework for applying the method to interpret experimental measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Polyalkenamers as Drop-In Additives for Ring-Opening Metathesis Polymerization: A Promising Upcycling Paradigm

Here we report a distinct strategy to upcycle waste polyalkenamers such as polybutadiene into new, performance-advantaged materials by using them as drop-in additives for ring-opening metathesis polymerization (ROMP). The polyalkenamers serve as competent chain-transfer agents in ROMPs of common classes of cyclic olefin monomers, facilitating good molecular weight control, allowing low Ru catalyst loadings, and enabling efficient incorporation of the polyalkenamer into the synthesized polymeric material. We successfully demonstrate ROMP using model polyalkenamers and translate these learnings to leverage commercial polybutadiene and acrylonitrile butadiene styrene (ABS) as chain transfer agents for ROMP copolymerizations. Critically, our strategy is shown to be highly efficient and operationally simple, quantitatively incorporating the polyalkenamer and inheriting aspects of its thermomechanical performance. Our results highlight a promising pathway for the upcycling of polyalkenamers and provide an alternative to existing deconstruction and functional upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

DOE OSTI2021

Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries

Feedstock cost and cost variability is expected to increase with the number of biorefineries. To quantify this effect, this spatial-economic analysis simulates feedstock cost and cost variability of an industry based on corn stover as a function of the number of biorefineries. Results are reported for nine scenarios (a base case and sensitivity analysis of four variables – harvest efficiency, sustainability constraints, opportunity cost, and corn grain yield) under deterministic and stochastic simulations, assuming biorefineries using 658 000 Mg (725 000 tons) year- 1 of corn stover in 2019. The resulting supply curves are highly elastic (i.e. little change in cost) for the first 50 of the 121 biorefineries, with price increases in subsequent biorefineries depending on scenario. In the base-case deterministic scenario, weighted-average stover costs are $66 Mg -1 ($60 ton- 1 ), $69 Mg -1 ($62 ton -1 ), and $156 Mg -1 ($142 ton -1 ), at the first, 60th, and 121st biorefineries, respectively. The stochastic simulations, subject to observed 30-year corn yield variability, follow a similar pattern, with price distributions that vary by scenario. The base-case stochastic simulations illustrate minimal cost variability for the first 60 biorefineries, but rapid increases in cost variability in the second half of potential biorefineries, with similar patterns observed in the other scenarios. Of the four variables explored, price was most sensitive to harvest efficiency, followed by sustainability constraints, corn yield, and opportunity cost. Results suggest that, under conventional logistics, about half of the US corn stover resource is reliably available with minimum cost increase and variability. Interactive visualization is available at https://doi.org/10.11578/1828779.

09 BIOMASS FUELS↗

DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗

DOE OSTI2022

Role of Nematicity in Controlling Spin Fluctuations and Superconducting Tc in Bulk FeSe

FeSe undergoes a transition from a tetragonal to a slightly orthorhombic phase at 90 K and becomes a superconductor below 8 K. The orthorhombic phase is sometimes called a nematic phase because quantum oscillation, neutron, and other measurements detect a significant asymmetry in x and y. How nematicity affects superconductivity has recently become a matter of intense speculation. Here, we employ an advanced ab initio Green's function description of superconductivity and show that bulk tetragonal FeSe would, in principle, superconduct with almost the same critical temperature Tc as the nematic phase. The mechanism driving the observed nematicity is not yet understood. Since the present theory underestimates it, we simulate the full nematic asymmetry by artificially enhancing the orthorhombic distortion. For benchmarking, we compare theoretical spin susceptibilities against experimentally observed data over all energies and relevant momenta. When the orthorhombic distortion is adjusted to correlate with observed nematicity in spin susceptibility, the enhanced nematicity causes spectral weight redistribution in the Fe-3dxz and Fe-dyz orbitals, but it leads to at most a 10-15% increment in Tc. This is because the dxy orbital always remains the most strongly correlated and provides most of the source of the superconducting glue. Nematicity suppresses the density of states at the Fermi level; nevertheless, Tc increases, in contradiction to both BCS theory and the theory of Bose-Einstein condensation. We show how the increase is connected to the structure of the particle-particle vertex. Our results suggest that while nematicity may be an intrinsic property of bulk FeSe, it is not the primary force driving the superconducting pairing.

bulk FeSe↗

DOE OSTI2025

TRISO Particle Isolation and Graphite Removal Using SRNL Vapor Digestion Technology

Tri-structural isotropic fueled reactors are planned to come online in the next decade, but there is not currently a widely agreed upon disposition pathway for this new fuel stream. For long term used nuclear fuel storage, there would be a significant benefit if the waste volume could be reduced or mitigated. Most of the volume from used TRISO fuel contains graphite. SRNL currently has an active patent which describes one pathway to digest the graphite. One benefit in the utilization of this pathway is that multiple cylindrical pieces can be stacked end-to-end in a reaction tube, then nitric acid and water vapor can be flowed through the tube, and the weight change of the individual cores can provide both a reaction profile and overall oxidant use efficiency. The reaction of nitric acid and graphite passes through a series of intermediate products – NO 2 , NO, and N 2 O – from reaction and decomposition to eventually form N2. The data shows that NO2 grows in with increasing temperature between 500-600°C and then decreases by way of either reaction with graphite or decomposition. Nitric oxide, a reaction and thermal decomposition product of NO 2 , exhibits a consistent decline as a function of temperature, which is consistent with the literature. Similarly, the concentrations of N 2 O and N 2 increase as a function of temperature as their reactions with graphite become more favorable.

Advanced Reactors↗

DOE OSTI2021

East River Watershed Stable Water Isotope Data in Precipitation, Snowpack and Snowmelt 2016-2020

Stable water isotopes (d18O, d2H and d-excess) are important tracers in hydrologic research to understand water partitioning between vegetation, groundwater, and runoff but are rarely applied to large watersheds with persistent snowpack and complex topopgraphy. Data were collected for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area supported by the U.S Department of Energy in the East River, CO Hydrologic Unit Code (140200010) with limited data also collected in adjacent watersheds Ohio Creek and Taylor River. Data are provided in csv and includes isotopic information for precipitation (years 2014-2016), snowmelt (years 2016-2017) and snowpits (years 2016-2020). Snowpit data contain depth resolved information at 10 cm intervals for density, snow water equivalent (SWE) and stable water isotopes. Bulk isotopic data for 86 snowpits contain depth, SWE, density and SWE-weighted isotope values. Sampling locations and elevations are provided within the data files, while kmz files are provided to view sampling locations using Google Earth software.

54 ENVIRONMENTAL SCIENCES↗