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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Shaw Cortez, Wenceslao E.↗
DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗
DOE OSTI2023

Origin of rate limitations in solid-state polymer batteries from constrained segmental dynamics within the cathode

Currently, one of the key challenges preventing the wide commercialization of polymer-based solid-state batteries is achieving high-rate capabilities. This work unravels chain structure and segmental dynamics in a polymer-based composite cathode consisting of LiFePO 4 (LFP), carbon, and poly(ethylene oxide) (PEO) with lithium bis(trifluoromethanesulfonyl)imide. Small-angle neutron scattering (SANS) data reveal that PEO chains are adsorbed on the surface of LFP particles during slurry processing to make the composite electrode. The strong interaction between LFP and PEO chains leads to greatly reduced segmental dynamics of PEO, as discovered by quasi-elastic neutron scattering (QENS). The reduced segmental dynamics results in 70% decreased Li + mobility of the polymer electrolyte in the composite cathode. The combined SANS and QENS study indicates that one of the key bottlenecks that limits the rate performance of PEO-based polymer batteries originates from molecular interactions within the cathode.

25 ENERGY STORAGE↗
DOE OSTI2025

Exploring a new synthesis route to lithium-excess disordered rock salt (DRX) cathode materials

Lithium-excess disordered rock salt (DRX) materials are promising candidates for Co/Ni-free Li-ion cathodes due to their high specific energy (800+ W h kg −1 ) and compositional flexibility. DRX cathodes are typically synthesized using solid-state reactions, which are difficult to scale and provide little-to-no control over particle morphology. Here, to address this bottleneck, the present study reports a two-step, solution-based reaction route to prepare Mn/Ti-based DRX oxyfluoride cathodes with nominal compositions of Li 1.25 Mn 0.5 Ti 0.3 O 1.95 F 0.05 and Li 1.35 Mn 0.7 Ti 0.1 O 1.85 F 0.15 . More specifically, a glycine–nitrate combustion reaction is used to produce a lithiated transition metal oxide, which is further reacted with LiF to produce high-purity DRX powders. Remarkably, this route yields 80–90% pure DRX after annealing for 1 h at 800–1000 °C, and 19 F solid-state nuclear magnetic resonance (ssNMR) spectra demonstrate that F− anions are successfully incorporated into the DRX structure. Cathodes prepared using this approach exhibit promising electrochemical performance, with Li 1.35 Mn 0.7 Ti 0.1 O 1.85 F 0.15 attaining reversible capacities ∼210 mA h g −1 and moderate cycling stability in half cells (65% capacity retention over 150 cycles). Overall, these results demonstrate that utilizing novel metal oxide precursors presents a viable and largely unexplored method to produce high-performance Co/Ni-free DRX cathodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Electronic heat generation in semiconductors: Non-equilibrium excitation and evolution of zone-edge phonons via electron–phonon scattering in photo-excited germanium

We investigate experimentally and using first-principles theory the generation of phonons and the relaxation of carriers on picosecond timescales across the Brillouin zone of photo-excited Ge by inter-valley electron–phonon scattering. The phonons generated are typical of those generated in semiconductor devices, contributing to the accumulation of heat within the material. We simulate the time-evolution of phonon populations, based on first-principles band structure and electron–phonon and phonon–phonon matrix elements, and compare them to data from time-resolved x-ray diffuse scattering experiments, performed at the Linac Coherent Light Source x-ray free-electron laser facility, following photo-excitation by a 50 fs near-infrared optical pulse. We show that the intensity of the non-thermal x-ray diffuse scattering signal, which is observed to grow substantially near the L-point of the Brillouin zone over 3–5 ps, is due to phonons generated by scattering of carriers between the Δ and L valleys. These phonons have low group velocities, resulting in a heat bottleneck. With the inclusion of phonon decay through 3-phonon processes, the simulations also account for other non-thermal features observed in the x-ray diffuse scattering intensity, which are due to anharmonic phonon–phonon scattering of the phonons initially generated by electron–phonon scattering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Multitask methods for predicting molecular properties from heterogeneous data

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.

Chemistry↗
DOE OSTI2021

Extended cycle life implications of fast charging for lithium-ion battery cathode

Enabling extreme fast charging (XFC, =10–15 min charging) requires a comprehensive understanding of its implications. While lithium plating is a key bottleneck for the anode, the full extent of limitations for the cathode are not well-understood, particularly in extended-cycle settings with well-defined battery designs and conditions. This article presents cycle-life implications of XFC on cathodes at multiple length scales, combining electrochemical analyses, degradation modeling, and post-test characterizations. The comprehensive test matrix includes 41 well-defined gr/NMC pouch cells under varied fast-charge rates (1–9C) and state-of-charges cycled up to 1000 times. Cathode issues remain minimal in early cycling, but begin to accelerate in later life, when distinct cracking is found and identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but distinct particle surface reconstruction is observed; however, this shows less pronounced effect on cathode aging than does cracking.

25 ENERGY STORAGE↗
DOE OSTI2022

Vertex protein PduN tunes encapsulated pathway performance by dictating bacterial metabolosome morphology

Engineering subcellular organization in microbes shows great promise in addressing bottlenecks in metabolic engineering efforts; however, rules guiding selection of an organization strategy or platform are lacking. Here, we study compartment morphology as a factor in mediating encapsulated pathway performance. Using the 1,2-propanediol utilization microcompartment (Pdu MCP) system from Salmonella enterica serovar Typhimurium LT2, we find that we can shift the morphology of this protein nanoreactor from polyhedral to tubular by removing vertex protein PduN. Analysis of the metabolic function between these Pdu microtubes (MTs) shows that they provide a diffusional barrier capable of shielding the cytosol from a toxic pathway intermediate, similar to native MCPs. However, kinetic modeling suggests that the different surface area to volume ratios of MCP and MT structures alters encapsulated pathway performance. Finally, we report a microscopy-based assay that permits rapid assessment of Pdu MT formation to enable future engineering efforts on these structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media

Abstract The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains’ solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( $$>512^3$$ > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role.

36 MATERIALS SCIENCE↗
DOE OSTI2022

PGM-Free Oxygen-Reduction Catalyst Development for Proton-Exchange Membrane Fuel Cells: Challenges, Solutions, and Promises

Proton-exchange membrane fuel cells (PEMFCs) are efficient and clean hydrogen energy technologies for transportation and stationary applications. Highly active and durable low-cost cathode catalysts for the oxygen-reduction reaction (ORR) under challenging acidic environments are desperately needed to address the cost and durability issues of PEMFCs. The most promising platinum group metal (PGM)-free catalysts for the ORR in acidic media are atomically dispersed and nitrogen-coordinated metal site catalysts denoted as M–N–C, M = Fe, Co, or Mn. Due to significant efforts in the past few decades, these catalysts have demonstrated much-improved ORR activity and promising initial fuel cell performance approaching traditional Pt/C catalysts. However, the insufficient long-term stability (up to 5000 h) under PEMFC operation represents a primary technical barrier to making current PGM-free catalysts less viable yet in PEMFCs. In this Account, we highlight recent advances in synthesizing efficient PGM-free catalysts for the ORR in PEMFCs, emphasizing effective strategies to improve mass and intrinsic activity and the possible degradation mechanisms. In particular, a chemical doping method based on the zeolitic imidazolate framework (ZIF)-8 represents the key to developing efficient M–N–C catalysts containing atomically dispersed and nitrogen-coordinated single metal active sites (i.e., MN 4 ). The newly acquired understanding of the formation mechanism of MN 4 active sites during the thermal activation and its correlation to catalytic properties guide the rational catalyst design rather than relying on current trial-and-error approaches. Considerable efforts have further been invested in increasing the active site density and enhancing intrinsic activity by regulating carbon-phase structures and the local coordination environment. Furthermore, these highly active catalysts usually suffer from significant activity loss during the ORR. Therefore, breaking the activity–stability trade-off is the key to simultaneously achieving activity and stability in one catalyst, which is discussed on the basis of our recent successes in regulating local carbon structures surrounding active single metal sites. Significant research efforts toward understanding the degradation mechanisms and improving the lifetime of PGM-free catalysts are still crucial for viable applications in the future. Novel electrode designing strategies are needed to translate the PGM-free catalysts’ ORR activity to solid-state electrolyte-based membrane electrode assemblies (MEAs) with robust three-phase (i.e., gas–liquid–solid) interfaces for efficient charge and mass transports for performance improvement. On the basis of our effort at the University at Buffalo supported by ElectroCat Consortium associated with U.S. DOE’s Hydrogen and Fuel Cell Technologies Office, we provide a perspective on PGM-free cathode catalysts concerning remaining bottlenecks and future opportunities, aiming to inspire the community in both mechanistic understanding and technological development.

08 HYDROGEN↗
DOE OSTI2026

Sequence-based generative AI design of versatile tryptophan synthases

Enzymes are powerful and sustainable catalysts, but their widespread application is limited by the difficulty of identifying functional starting points for optimization, creating a major bottleneck in early- stage biocatalyst discovery. Designing libraries of such starting enzymes remains particularly challenging. Here, we use the GenSLM protein language model to generate novel β-subunit of tryptophan synthase (TrpB) enzymes that express in Escherichia coli and are both stable and catalytically active. Many generated TrpBs also display significant substrate promiscuity, outperforming their natural counterparts on non-native substrates. Some even surpass laboratory-evolved TrpBs. Comparison of the most-active and most-promiscuous generated TrpB to its closest natural homolog confirms that the enhanced versatility is absent from the natural enzyme, highlighting the creative potential of generative models. These results demonstrate that the generated TrpBs not only preserve natural structure and function but also acquire non-natural properties, establishing generative models as powerful tools for biocatalyst discovery and engineering.

biocatalysis↗
DOE OSTI2020

Scalable Synthesis of Ti 3 C 2 T x MXene

Scaling the production of synthetic 2D materials to industrial quantities has faced significant challenges due to synthesis bottlenecks whereby few have been produced in large volumes. These challenges typically stem from bottom‐up approaches limiting the production to the substrate size or precursor availability for chemical synthesis and/or exfoliation. In contrast, MXenes, a large class of 2D transition metal carbides and/or nitrides, are produced via a top‐down synthesis approach. The selective wet etching process does not have similar synthesis constraints as some other 2D materials. The reaction occurs in the whole volume; therefore, the process can be readily scaled with reactor volume. Herein, the synthesis of 2D titanium carbide MXene (Ti 3 C 2 T x ) is studied in two batch sizes, 1 and 50 g, to determine if large‐volume synthesis affects the resultant structure or composition of MXene flakes. Characterization of the morphology and properties of the produced MXene using scanning electron microscopy, X‐ray diffraction, dynamic light scattering, Raman spectroscopy, X‐ray photoelectron spectroscopy, UV–visible spectroscopy, and conductivity measurements show that the materials produced in both batch sizes are essentially identical. This illustrates that MXenes experience no change in structure or properties when scaling synthesis, making them viable for further scale‐up and commercialization.

Shuck, Christopher E.↗
DOE OSTI2023

Chemical Feedback in the Self-Assembly and Function of Air–Liquid Interfaces: Insight into the Bottlenecks of CO 2 Direct Air Capture

As fossil fuels remain a major source of energy throughout the world, developing efficient negative emission technologies, such as direct air capture (DAC), which remove carbon dioxide (CO 2 ) from the air, becomes critical for mitigating climate change. Although all DAC processes involve CO 2 transport from air into a sorbent/solvent, through an air–solid or air–liquid interface, the fundamental roles the interfaces play in DAC remain poorly understood. In this work, we study the interfacial behavior of amino acid (AA) solvents used in DAC through a combination of vibrational sum frequency generation spectroscopy and molecular dynamics simulations. This study revealed that the absorption of atmospheric CO 2 has antagonistic effects on subsequent capture events that are driven by changes in bulk pH and specific ion effects that feedback on surface organization and interactions. Among the three AAs (leucine, valine, and phenylalanine) studied, we identify and separate behaviors from CO 2 loading, chemical changes, variations in pH, and specific ion effects that tune structural and chemical degrees of freedom at the air–aqueous interface. The fundamental mechanistic findings described here are anticipated to enable new approaches to DAC based on exploiting interfaces as a tool to address climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes

We report in Li-ion batteries, the mechanical degradation initiated by micro cracks is one of the bottlenecks for enhancing the performance. Quantifying the crack formation and evolution in complex composite electrodes can provide important insights into electrochemical behaviors under prolonged and/or aggressive cycling. However, observation and interpretation of the complicated crack patterns in battery electrodes through imaging experiments are often time-consuming, labor intensive, and subjective. Herein, a deep learning-based approach is developed to extract the crack patterns from nanoscale hard X-ray holo-tomography data of a commercial 18650-type battery cathode. Efficient and effective quantification of the damage heterogeneity with automation and statistical significance is demonstrated. The crack characteristics are further associated with the active particles’ packing densities and a potentially viable architectural design is discussed for suppressing the structural degradation in an industry-relevant battery configuration.

25 ENERGY STORAGE↗
DOE OSTI2024

LES simulations of a vacuum membrane distillation channel with geometric alterations

3D LES simulations were carried out to study the performance of a vacuum membrane distillation module. A wiggly wall profile with/without embedded stiffeners was considered to alleviate axial and radial temperature polarization, the cause of performance decrease in membrane distillation. Results of the flow field show the wiggles create unsteady vortex shedding inducing intense mixing in the channel. Vortex shedding intensity increases as the Reynolds number increases or stiffeners are added. 3D results show a 56% improvement in flux when moving from a flat sheet membrane to a wiggly membrane with stiffeners at a constant Reynolds number, corresponding to an improvement from 11.7 to 41.7 in the Nusselt number, showing that the alterations improved the flux performance by enhancing the heat transfer along the membrane surface and therefore alleviating temperature polarization, a critical bottleneck in membrane distillation systems. Further, a merit criterion was defined based on the Nusselt number and friction factor, and a 40% increase in merit was shown switching from a flat to a wiggly channel, while a 97% merit increase was seen going from flat to wiggly with stiffeners. A 42% enhancement in the flux was also seen moving from a straight channel to a wiggly channel at a higher Reynolds number which highlights the importance of strategically choosing the mass flow rate, as well as inducing flow separation and vortex shedding in the channel to promote mixing and dissipate the thermal boundary layer. The variation in results between the wiggly module with stiffeners and the wiggly module at high Reynolds numbers suggests enhancing mixing structures can be more impactful on flux for this geometry than increasing the flow rate to a turbulent/transitional regime. However, both are preferable for peak system flux performance. Furthermore, a 2D approximation was used to perform simulations on more extended channels to examine the length degradation. The modules with wiggly channels performed at the same flux level with a doubling of the length. On the other hand, the flat sheet modules experienced length degradation by temperature polarization and dropped in flux yield by around 13% of the short-channel value. This work illustrates that modeling the system and understanding how the performance decreases as the membrane surface area increases are critical for a larger module (scaling up from a lab to a prototype module) to maintain high flux performance.

42 ENGINEERING↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

09 BIOMASS FUELS↗