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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Atomic Layer Deposition for Materials-Based H2 Storage: Opportunities and Limitations

The transportation demands in our growing hydrogen (H2) economy requires robust storage systems. The current commercially implemented technology in fuel cell cars relies on the well-established technology of hydrogen gas compressed to 350-700 bar, depending on the application. The compressed gas tanks in use today are bulky and cost intensive. To address this challenge, material-based storage is one of the long-term alternatives considered and constitutes the focus of this talk. Material-based storage is broadly defined as hydrogen bound to solid materials, with its binding strength varying from physisorption to porous materials, such as zeolites and metal organic frameworks, to chemisorption in (complex) metal hydrides. The ultimate targets set by the U.S. Department of Energy for this technology include a system gravimetric capacity of 6.5 wt% and volumetric capacity of 40 g/L at 100 bar, operating temperatures ranging between -40 C and +40 C and adsorption/desorption timescales of < 5 min. Storage in the form of physisorbed or chemisorbed hydrogen has guided the materials research, which metal- organic framework and (complex) metal hydrides being the most promising materials classes. A variety of these materials have met one or more of the targets, but it has remained elusive for a single material system to meet all these stringent requirements. Nano-encapsulation and low-concentration chemical additives have previously been employed separately to overcome such challenges. Functionalization via atomic layer deposition (ALD), however, offers unique characteristics that make it suitable for both, nano-encapsulation and "doping" with low-concentration additives. This deposition technique has sub-monolayer thickness control, is highly conformal in high-surface area materials and is self-limiting, i.e., once the gas-phase precursor reacts with the available surface sites, the surface reactions stop. In this presentation, we will showcase examples where ALD, more generally vapor-phase functionalization, on (complex) metal hydrides and organic frameworks has improved the material properties for H2 storage. In our first study, Al2O3 was deposited on magnesium borohydride, Mg(BH4)2, at room temperature using trimethylaluminum (TMA) and water. From our findings, encouraging initial results were obtained: the H2 desorption temperature was lowered by 60-120 degrees C, the desorbed gravimetric H2 capacity at temperatures < 250 degrees C was doubled, and the desorption kinetics increased by a factor of ~6 compared to uncoated Mg(BH4)2. However, hydrolysis reactions caused by residual surface -OH groups from the ALD water-pulse degraded the sample substantially. Through this study, the use of TMA was observed to be highly reactive with the Mg(BH4)2 surface species, leading to the strategy of exposing the sample only to TMA. The relative mole fraction of the vapor-phase additive can be precisely controlled with the number of TMA pulses and pulse duration. By tuning these parameters, we show in our second study that 10 pulses of TMA at ambient conditions were able to decrease the H2 desorption temperature by ~100 degrees C while retaining <95 % of its H2 capacity. We applied this approach with other additives such as BBr3, TiCl4 and tetrahydrofuran, and demonstrate that this unique approach opens the door to a new class of molecular additives and catalysts which cannot easily be introduced with conventional mechano-chemical or solvent-based techniques for (complex) metal-hydrides. In the case of metal organic frameworks, ALD is a promising technique to functionalize the pores with metal atoms or functional groups able to tune the gas selectivity and binding energy, for which ~15 kJ/mol has been established as the optimal value for H2 storage in sorbent materials. Vapor-phase techniques such as ALD have numerous benefits over other functionalization tools for H2 storage materials opening innumerable opportunities to the field. To exploit these opportunities, the current limitations on room temperature and water-less ALD processes needs to be overcome which will greatly expand the possibilities of encapsulation and incorporation of additives for organic frameworks and (complex) metal hydrides.

atomic layer deposition↗
DOE OSTI2026

Concept of Dynamic Heat Insulation for Rotating Detonation Engines

This work introduces a new class of materials concept to dynamically reduce instantaneous heat fluxes in Rotating Detonation Engine (RDE) combustor chamber walls. The high-frequency and high-amplitude surface heat fluxes observed in RDEs arise from large instantaneous temperature differences between the detonation shockwave and chamber wall surface. These temperature gradients drive substantial energy loss and reduce the chamber gas pressure, ultimately limiting the cycle’s thermodynamic efficiency. This work introduces a concept for dynamically insulating the combustion chamber surfaces using surface layers or coatings with low thermal time scale. With such coatings, the surface temperature may follow the fluctuations of the cyclic detonation wave temperature, thus reducing the instantaneous heat flux therefore cycle-mean heat flux. To analyze these cyclic thermal phenomena, a one-dimensional analytical conduction solver was utilized with the capability to handle multilayered structures. Parametric modeling was performed using transient heat flux boundary conditions representative of a hydrogen–air RDE across a broad range of coating thermal properties and engine conditions. The coating effectiveness scaled with the product of thermal time constant and detonation wave frequency and the results were non-dimensionalized to guide future materials development. This strategy may offer benefits in increasing material survivability, reducing cooling requirements, and enhancing pressure gain.

heat transfer↗
DOE OSTI2025

Nuclear Magnetic Resonance ‐based fragment screen of the E3 ligase Fem‐1 homolog B

Abstract Targeted protein degradation using PROTACs (PROteolysis TArgeting Chimeras) has emerged as a transformative therapeutic strategy, largely relying on a small number of E3 ubiquitin ligases such as CRBN and VHL. However, resistance, toxicity, and poor oral bioavailability limit the utility of PROTACs and highlight the need to expand the E3 ligase toolbox. Fem‐1 homolog B (FEM1B) is a lesser‐known E3 ligase that offers a promising alternative due to its broad expression and ability to recognize diverse degron motifs. Here, we describe the development of a stable construct of FEM1B, the results of a protein‐observed NMR‐based fragment screen using this construct, and the X‐ray structures of some of the fragment hits when bound to the protein. From these results, new PROTACs utilizing FEM1B as the E3 ligase may be discovered, providing an alternative E3 ligase for targeted protein degradation.

Katinas, Jade M. [Department of Biochemistry Vande↗
DOE OSTI2021

VirION2: a short- and long-read sequencing and informatics workflow to study the genomic diversity of viruses in nature

Microbes play fundamental roles in shaping natural ecosystem properties and functions, but do so under constraints imposed by their viral predators. However, studying viruses in nature can be challenging due to low biomass and the lack of universal gene markers. Though metagenomic short-read sequencing has greatly improved our virus ecology toolkit—and revealed many critical ecosystem roles for viruses—microdiverse populations and fine-scale genomic traits are missed. Some of these microdiverse populations are abundant and the missed regions may be of interest for identifying selection pressures that underpin evolutionary constraints associated with hosts and environments. Though long-read sequencing promises complete virus genomes on single reads, it currently suffers from high DNA requirements and sequencing errors that limit accurate gene prediction. Here we introduce VirION2, an integrated short- and long-read metagenomic wet-lab and informatics pipeline that updates our previous method (VirION) to further enhance the utility of long-read viral metagenomics. Using a viral mock community, we first optimized laboratory protocols (polymerase choice, DNA shearing size, PCR cycling) to enable 76% longer reads (now median length of 6,965 bp) from 100-fold less input DNA (now 1 nanogram). Using a virome from a natural seawater sample, we compared viromes generated with VirION2 against other library preparation options (unamplified, original VirION, and short-read), and optimized downstream informatics for improved long-read error correction and assembly. VirION2 assemblies combined with short-read based data (‘enhanced’ viromes), provided significant improvements over VirION libraries in the recovery of longer and more complete viral genomes, and our optimized error-correction strategy using long- and short-read data achieved 99.97% accuracy. In the seawater virome, VirION2 assemblies captured 5,161 viral populations (including all of the virus populations observed in the other assemblies), 30% of which were uniquely assembled through inclusion of long-reads, and 22% of the top 10% most abundant virus populations derived from assembly of long-reads. Viral populations unique to VirION2 assemblies had significantly higher microdiversity means, which may explain why short-read virome approaches failed to capture them. These findings suggest the VirION2 sample prep and workflow can help researchers better investigate the virosphere, even from challenging low-biomass samples. Our new protocols are available to the research community on protocols.io as a ‘living document’ to facilitate dissemination of updates to keep pace with the rapid evolution of long-read sequencing technology.

Long-reads↗
DOE OSTI2023

Deciphering the multiple deformation mechanisms responsible for sustained work hardening in a FeCrCoNi medium entropy alloy

Two important and desirable properties of materials for most structural applications are high tensile strength and ductility, which typically require high work hardening to delay necking. Here, in this work, we designed and tensile tested a face-centered cubic (fcc) Fe-Cr-Co-Ni medium-entropy alloy in which multiple deformation mechanisms are triggered during tensile loading at different temperatures to induce sustained work hardening. Our strategy involved control of the relative stabilities of the fcc, hcp (hexagonal close-packed), and bcc (body-centered cubic) phases in this quaternary system via high-throughput thermodynamic calculations. This alloy not only exhibits extensive deformation-induced nanotwinning at room temperature, but also displays a two-step sequential phase transformation [γ (fcc) → ε (hcp) martensite → α’ (bcc) martensite] at 77 K, which contrasts with the single-step phase transformation [γ → ε martensite] observed in many other fcc high/medium entropy alloys with a low stacking fault energy. The sequence of phase transformation at 77K was supported by first-principles density functional theory calculations. This work provides new templates for the design of alloys capable of multiple deformation mechanisms for sustained work hardening.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Correlation of coercivity, microstructure, and surface defects in HDDR-processed Nd-Fe-B powders for bonded magnet applications

Hydrogenation-Disproportionation-Desorption-Recombination (HDDR) is an effective method for producing high coercivity, anisotropic powder from either fresh Nd-Fe-B alloy or recycled Nd-Fe-B magnets for use in bonded magnets. We investigated the impact of surface defects on the coercivity of HDDR-processed Nd-Fe-B using scanning electron microscopy (SEM), magnetic measurements, and micromagnetic simulations. We observed that coercivity decreases as particle size reduces, with SEM revealing surface defects and the detachment of Nd 2 Fe 14 B grains and Nd-rich phases from the particle surface. Micromagnetic simulations indicate that demagnetization initiates at the particle surface, where these defects are most concentrated, leading to reduced coercivity. The reduction in squareness of demagnetization curve, knee point field and coercivity for smaller HDDR particles is attributed to an increased specific surface area, which exhibits reduced nucleation field and weak domain wall pinning field during magnetization reversal. By addressing the role of surface defects in coercivity degradation, this study provides insights for improving both new powder production and recycling strategies, ultimately leading to enhanced performance of bonded magnets and contributing to more sustainable practices in the rare earth supply chain. One potential strategy to enhance the performance of HDDR Nd-Fe-B materials involves reducing the fraction of fine particles ($<$35 µm) and promoting the formation of grain boundary phases on particle surfaces.

Liu, Xubo B. [Ames Laboratory (AMES), Ames, IA (Un↗
DOE OSTI2025

Thermodynamic Modeling of Complex Solid Solutions in the Lu-H-N System via Graph Neural Network Accelerated Monte Carlo Simulations

Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂⁡(10 2 ) ⁢GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂⁡(10 0 ) ⁢GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va) 3 system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.

Monte Carlo methods↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2023

A new coupling of a GPU-resident large-eddy simulation code with a multiphysics wind turbine simulation tool

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.

17 WIND ENERGY↗
DOE OSTI2020

Advanced Non-Tread Materials for Fuel-Efficient Tires

PPG Industries, Inc. proposes to develop a new silica filler that can increase fuel efficiency by 2% while maximizing key performance properties in non-tread tire components compared to current carbon black/silica filler blends. In this project PPG focused on a sidewall compound. The developed compounds/components will reduce energy losses by approximately 25%, with no more than a 5% loss of resistance to degradative forces (targeting better performance). PPG plans to achieve this goal by characterizing the impact the silica filler can have on rubber properties and then tuning the silica morphology and surface chemistry for this end use. PPG will also provide guidance in optimizing the compound formulation to make the best use of this new filler. A key aspect is to understand and maintain, if not improve, the aging performance as evaluated by crack growth resistance under ozone environment. Two key strategies were used to meet the rolling resistance goal while maintaining other performance properties. The first factor was understanding the surface treatments and chemical makeup of the silica and its effects on compound performance. The second factor was using the best performing silicas and determining the optimum compound modifications to achieve the final sidewall compound performance. Using statistical analysis throughout the optimization process, it was determined that changing the silica morphology played a role in improving the reduction of tear strength while reducing rolling resistance and the improvements observed varied depending on the CB type.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗
DOE OSTI2024

Studies of new Higgs boson interactions through nonresonant $HH$ production in the $ b\overline{b}\gamma \gamma $ final state in $pp$ collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A search for nonresonant Higgs boson pair production in the $ b\overline{b}\gamma \gamma $ final state is performed using 140 fb –1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. This analysis supersedes and expands upon the previous nonresonant ATLAS results in this final state based on the same data sample. The analysis strategy is optimised to probe anomalous values not only of the Higgs (H) boson self-coupling modifier κλ but also of the quartic HHVV (V = W, Z) coupling modifier κ 2V . No significant excess above the expected background from Standard Model processes is observed. An observed upper limit μHH < 4.0 is set at 95% confidence level on the Higgs boson pair production cross-section normalised to its Standard Model prediction. The 95% confidence intervals for the coupling modifiers are –1.4 < κ λ < 6.9 and –0.5 < κ 2V < 2.7, assuming all other Higgs boson couplings except the one under study are fixed to the Standard Model predictions. The results are interpreted in the Standard Model effective field theory and Higgs effective field theory frameworks in terms of constraints on the couplings of anomalous Higgs boson (self-)interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Mitigation of the Brighter-Fatter Effect in the LSST Camera

Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). This particularly impacts bright calibration stars, which appear larger than their intrinsic shape, creating a flux-dependent point-spread function (PSF) that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSST Camera before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new sampling method based on a physically motivated model to account these higher-order terms in the correction, and then we test the modified correction on both datasets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars which we conclude is the result of a unmodeled curl component of the deflection field by the correction. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Learning physics at future $e^–e^+$ colliders with machine

Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future $e^–e^+$ colliders. Because of their dominance in data, the measurements of such events are crucial for advancing the precision frontier of Higgs and electroweak physics in the next decades. We show that this difficulty can be well-addressed by synergizing the event-level information into the data analysis, with the techniques of deep neutral network. In relation to this, we introduce a CMB-like observable scheme, where the event-level kinematics is encoded as Fox-Wolfram (FW) moments at leading order and multi-spectra of spherical harmonics at higher orders. Then we develop a series of jet-level (w/ and w/o the FW moments) and event-level classifiers, and analyze their sensitivity performance comparatively with two-jet and four-jet events. As an application, we analyze measuring Higgs decay width at $e^–e^+$ colliders with the data of 5ab –1 @240GeV. The precision obtained is significantly better than the baseline ones presented in documents. We expect this strategy to be applied to many other hadronic- event measurements at future $e^–e^+$ colliders, and to open a new angle for evaluating their physics capability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Measuring pH Changes Inside a Bipolar Membrane Junction

Here, the local pH environment within bipolar membrane (BPM) junctions is complex and not well understood, yet it is important to control for advancing the performance of BPM-based electrochemical systems. We report a voltammetric strategy using an ultrathin Ni mesh pH probe to spatially resolve pH changes in the BPM junction during model BPM electrolyzer operation. Under reverse bias, we observe depletion of OH – at the anion-exchange layer (AEL) interface, with a degree diminishing with increasing distance from the AEL. These gradients correlate with current-dependent water dissociation (WD) and are modulated by the electric field and the surface charge state of the catalyst. By correlating spatial pH profiles with the surface-charging behavior of WD catalysts, we explore a mechanism of catalyst-mediated H + and OH – transfer facilitated by hydrogen-bonding networks. These findings highlight the role of local chemistry and electrostatics in BPM performance and offer new methods to probe and engineer catalytic junctions in electrochemical energy devices.

Hou, Shujin [University of California, Berkeley, C↗
DOE OSTI2023

Optically Enhanced Solid-State 1 H NMR Spectroscopy

Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor–acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hyperpolarization. Systems that exhibit photo-CIDNP in solids are not common, and this effect has, up to now, only been observed for 13 C and 15 N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here, we report the first example of optically enhanced solid-state 1 H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor–chromophore–acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled 1 H nuclei relays polarization through the whole sample, yielding a 16-fold bulk 1 H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Coupled Microbial-Conversion and Computational-Fluid-Dynamics (CFD) Models for Butanediol Production in Micro-Aerated Reactors

Microbial conversion of substrates to macromolecules has been widely used in the synthesis of value-added products in pharmaceutical and biotechnology industries. These bioreactions are also being investigated in the production of low-value commodities such as biofuels [1]. Gas-liquid mass-transfer and transport-reaction coupling are important challenges when designing and scaling up these reactor systems. Experiments in wellmixed small-scale reactors have enabled characterization of microbial reactivity, while their coupling with macroscale transport remains relatively unexplored. In this work, we use a coupled metabolic-CFD model to study the action of a genetically engineered microbe Zymomonas mobilis [2] on sugars to produce 2,3-Butanediol (BDO). BDO is an important hydrocarbon intermediate that can be catalytically upgraded to several fuels and chemicals [3]. An important aspect to this particular microbial conversion is the need for micro-aerated environments as opposed to traditional aerobic fermentation. Slight variations in oxygen concentration can result in competing reaction pathways that disable BDO production. Hence, gas-liquid mass transfer and transport need to be optimized in large-scale reactors to maximize BDO production, for which CFD is a valuable tool. The aerobic-fermentation CFD model previously developed by the authors [5] for simulating bubble-column and airlift reactors at scale was used in this study. The Reynolds-averaged mass, momentum and energy transport equations for interpenetrating gas and liquid phase are solved in this model along with the transport and interphase mass transfer of oxygen. Our previous work used a phenomenological model for microbial oxygen uptake that neglected microbial growth and other reaction pathways. In this work, a detailed metabolic model enabled prediction of product formation and inhibition pathways. In order to manage computational cost, we used a subcycling technique [6] that takes advantage of the clear separation in transport (~ 200 sec) and reaction (~ 2-3 hours) timescales. The CFD model is first solved to steady state, after which the metabolic model is advanced at every cell in the computational domain using the local oxygen concentration. The CFD model is then run to achieve a new steady state that provides a new oxygen distribution for the metabolic model. This process, where reaction and fluid updates are interleaved together, is iterated until reactants are completely exhausted. This work will examine the performance of different reactor designs such as bubble column and airlift reactors at scale (250-500 m3). Oxygen mass-transfer coefficient and distribution are critically analyzed among reactors, and optimization studies pertaining to aeration is presented. Furthermore, it has been observed in experiments that high BDO production may be achieved by manipulating the aerobic environment over the course of reaction, such that oxygen concentration is high during the growth phase, and very low as sugar is depleted. This characteristic will be addressed by our simulations for which a timedependent scheduling strategy for aeration is presented that maximizes BDO production. [1] Humbird, D., Davis, R., and McMillan, J., Aeration costs in stirred-tank and bubble column bioreactors, Biochemical Engineering Journal, 127, 161—166, 2017 [2] Yang, S., Mohagheghi, A., Franden, M. A., Chou, Y.-C., Chen, X., Dowe, N., Himmel, M. E., and Zhang, M., Metabolic engineering of zymomonas mobilis for 2, 3-butanediol production from lignocellulosic biomass sugars. Biotechnology for biofuels, 9(1):189, 2016 [3] Kim, S. J., Sim, H. J., Kim, J. W., Lee, Y. G., Park, Y. C., and Seo, J. H., Enhanced production of 2,3-butanediol from xylose by combinatorial engineering of xylose metabolic pathway and cofactor regeneration in pyruvate decarboxylase-deficient Saccharomyces cerevisiae. Bioresource Technology, 245:1551–1557, 2017 [4] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

This work develops new techniques within Horovod, a generic communication library supporting data parallel training across deep learning frameworks. In particular, we improve the Horovod control plane by implementing a new coordination scheme that takes advantage of the characteristics of the typical data parallel training paradigm, namely the repeated execution of collectives on the gradients of a fixed set of tensors. Using a caching strategy, we execute Horovod’s existing coordinator-worker logic only once during a typical training run, replacing it with a more efficient decentralized orchestration strategy using the cached data and a global intersection of a bitvector for the remaining training duration. Next, we introduce a feature for end users to explicitly group collective operations, enabling finer grained control over the communication buffer sizes. To evaluate our proposed strategies, we conduct experiments on a world-class supercomputer — Summit. We compare our proposals to Horovod’s original design and observe 2x performance improvement at a scale of 6000 GPUs; we also compare them against tf.distribute and torch.DDP and achieve 12% better and comparable performance, respectively, using up to 1536 GPUs; we compare our solution against BytePS in typical HPC settings and achieve about 20% better performance on a scale of 768 GPUs. Finally, we test our strategies on a scientific application (STEMDL) using up to 27,600 GPUs (the entire Summit) and show that we achieve a near-linear scaling of 0.93 with a sustained performance of 1.54 exaflops (with standard error +- 0.02) in FP16 precision.

Romero, Joshua↗