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At least 343 records · Page 19

Automatic Generation of Chemical Mechanisms for Electrochemical Systems: Solid Electrolyte Interphase Formation in Lithium Batteries

Electrolytes in many lithium ion batteries decompose at the low potentials near the anode. The decomposition products form a layer termed the solid electrolyte interphase (SEI). The composition and growth of the SEI layer significantly affect both the capacity fade and safety of lithium ion batteries. However, SEI formation and growth kinetics are not well understood. In this work, we present an extension of the Reaction Mechanism Generator (RMG) software to automatically generate mechanisms for SEI formation. We extend RMG’s solvation correction framework to account for kinetic solvent effects and demonstrate the accuracy of our technique. We calculate thermochemical parameters for 252 species and rate coefficients for 69 reactions, most with associated solvation corrections. This and additional quantum chemistry data are used to extend RMG’s thermodynamic group additivity and solute parameter estimation schemes to handle lithiated species and add 14 new reaction families to RMG. RMG is additionally extended to simulate electrocatalytic systems. Lastly, we demonstrate RMG on the decomposition of acetonitrile and ethylene carbonate near a battery anode. Furthermore, while this framework does not yet resolve individual ions, as appropriate thermochemistry estimators are not available, and thus, cannot yet resolve more complex electrochemical pathways, RMG is able to generate reasonable pathways for SEI formation that match literature pathways and products. In particular, RMG identifies a new important reaction pathway that is not present in literature.

Chemical reactions↗

Open-channel metal particle superlattices

Although tremendous advances have been made in preparing porous crystals from molecular precursors, there are no general ways of designing and making topologically diversified porous colloidal crystals over the 10-1,000 nm length scale. Control over porosity in this size range would enable the tailoring of molecular absorption and storage, separation, chemical sensing, catalytic and optical properties of such materials. In this paper a universal approach for synthesizing metallic open-channel superlattices with pores of 10 to 1,000 nm from DNA-modified hollow colloidal nanoparticles (NPs) is reported. By tuning hollow NP geometry and DNA design, one can adjust crystal pore geometry (pore size and shape) and channel topology (the way in which pores are interconnected). The assembly of hollow NPs is driven by edge-to-edge rather than face-to-face DNA-DNA interactions. Two new design rules describing this assembly regime emerge from these studies and are then used to synthesize 12 open-channel superlattices with control over crystal symmetry, channel geometry and topology. The open channels can be selectively occupied by guests of the appropriate size and that are modified with complementary DNA (for example, Au NPs).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Design principles for lifelong learning AI accelerators

Lifelong learning-an agent's ability to learn throughout its lifetime-is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, particularly if the models are to be deployed on edge platforms, which have strict size, weight and power constraints. Here we explore the design of lifelong learning AI accelerators that are intended for deployment in untethered environments. We identify key desirable capabilities for lifelong learning accelerators and highlight metrics to evaluate such accelerators. We then discuss current edge AI accelerators and explore the future design of lifelong learning accelerators, considering the role that different emerging technologies could play. The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.

42 ENGINEERING↗

Chemical and Biological Catalysis for Plastics Recycling and Upcycling

Plastics pollution is causing an environmental crisis, prompting the development of new approaches for recycling, and upcycling. Here, we review challenges and opportunities in chemical and biological catalysis for plastics deconstruction, recycling, and upcycling. We stress the need for rigorous characterization and use of widely available substrates, such that catalyst performance can be compared across studies. Where appropriate, we draw parallels between catalysis on biomass and plastics, as both substrates are low-value, solid, recalcitrant polymers. Innovations in catalyst design and reaction engineering are needed to overcome kinetic and thermodynamic limitations of plastics deconstruction. Either chemical and biological catalysts will need to act interfacially, where catalysts function at a solid surface, or polymers will need to be solubilized or processed to smaller intermediates to facilitate improved catalyst–substrate interaction. Overall, developing catalyst-driven technologies for plastics deconstruction and upcycling is critical to incentivize improved plastics reclamation and reduce the severe global burden of plastic waste.

28 EE - Advanced Manufacturing Office (EE-5A)↗

Machine learning surrogate models for Landau fluid closure

The first result of applying the machine/deep learning technique to the fluid closure problem is presented in this letter. As a start, three different types of neural networks (multilayer perceptron (MLP), convolutional neural network (CNN) and two-layer discrete Fourier transform (DFT) network) were constructed and trained to learn the well-known Hammett-Perkins Landau fluid closure in configuration space. We found that in order to train a well-preformed network, a minimum size of training data set is needed; MLP also requires a minimum number of neurons in the hidden layers equals to the degrees of freedom in Fourier space despite training data is fed in configuration space. Out of three models DFT performs the best for the clean data most likely due to the existence of nice Fourier expression for Hammett-Perkins closure but it is least robust with respect to input noise. Overall, with appropriate tuning and optimization, all three neural networks are able to accurately predict Hammett-Perkins closure and reproduce the inherit nonlocal feature, suggesting a promising path to calculate more sophisticated closures with the machine/deep learning technique.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

An Adaptive Reference Scheme to Extend the Functional Range of Optical Backscatter Reflectometry in Extreme Environments

Optical frequency domain reflectometry (OFDR) measurements are performed by recording the interference pattern of light backscattered by density fluctuations or Bragg gratings along the length of an optical fiber. Changes in local temperature or strain in the fiber cause shifts in the backscattered light spectrum, which can be calibrated to the applied temperature or strain. Comparing the backscattered optical spectra from each reflection site with a reference (unstrained) spectrum allows for quantification of local spectral shifts. While mature OFDR-based technologies can provide high-precision spatially distributed measurements over kilometer lengths, the post-processing approaches used to recover spectral shifts from raw optical intensity data can limit the use of OFDR in harsh environments. This paper presents a novel approach to post-processing OFDR data which extends the usability of optical fibers exposed to harsh environments. This approach is the first to use the spectral shift quality, calculated for two OFDR measurements, to identify an appropriate reference scan from which the spectral shift can be determined. Here, three experimental data sets are used to test the algorithm: (1) an optical fiber heated to >950°C, (2) an aluminum-embedded optical fiber under strain from differential thermal expansion, and (3) an optical fiber exposed to a total neutron flux of 2×10 13 n/cm 2 /s over 40 hours in a nuclear test reactor. Results show that using a metric of quality to select an appropriate reference measurement extends the functional range of OFDR strain and temperature sensors. Furthermore, this algorithm can be applied to existing OFDR data.

47 OTHER INSTRUMENTATION↗

Data Placement Optimization for ATLAS in a Multi-Tiered Storage System within a Data Center

Scientific experiments and computations, especially in High Energy Physics, are generating and accumulating data at an unprecedented rate. Effectively managing this vast volume of data while ensuring efficient data analysis poses a significant challenge for data centers, which must integrate various storage technologies. This paper proposes addressing this challenge by designing and developing a precise data popularity prediction model utilizing state-of-theart AI/ML techniques. This model is crafted from the analysis of ATLAS data and access patterns. It enables us to migrate infrequently accessed data to more economical storage media, such as tape drives, while storing frequently accessed data on faster yet costlier storage media like HDD or SSD. This strategic approach ensures data is placed optimally into the appropriate storage classes, thereby maximizing storage capacity while minimizing data access latency for end-users. Furthermore, the paper includes a performance evaluation of the prediction model using various key metrics such as F1 score, accuracy, precision and recall. Finally, we present a prototype use case, leveraging real-world file access data to assess the model’s impact on performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Computational Tools for Additive Manufacture of Tailored Microstructure and Properties

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Currently most additive builds use a single set of process parameters (e.g. laser power and scan speed) which results in a part with a homogenous microstructure that provides a singular performance level. To move beyond this state, Raytheon Technologies Research Center worked to create a set of computational tools to track material evolution through each step of the additive process. Computational fluid dynamics and phase field models for microstructure evolution as a function of processing parameters, and crystal plasticity models fully coupling microstructure and mechanical properties for performance predictions were leveraged to establish a connection between additive parameters and the final microstructure. This framework was utilized to tailor spatially-varying mechanical properties in a part by appropriately controlling the microstructure evolution during the additive process. Specifically, a turbine blade was 3D printed from nickel superalloy IN718 using laser powder bed fusion with coarse grains in the airfoil section which experiences the highest temperatures and is creep limited while finer grains were printed in the root of the blade which experience higher stresses but at lower temperatures and is therefore fatigue limited. The benefit of being able to intentionally insert coarse grains in the high temperature region of the blade was showcased with a microstructure sensitive creep model that indicates longer creep life for coarser grains.

20 FOSSIL-FUELED POWER PLANTS↗

How Should Researchers Measure Perovskite-Based Monolithic Multijunction Solar Cells’ Performance? A Calibration Lab's Perspective

Perovskite multijunctions (PVSK MJs) have made remarkable progress with monolithic PVSK/PVSK tandems surpassing the efficiency of single-junction (1 J) PVSK cells and PVSK/Si cells reported to exceed the 30% efficiency mark. These efficiencies are reported at standard test conditions (STC), established by the photovoltaic (PV) community to facilitate comparison between devices and technologies. Herein, it is discussed why an accurate STC performance measurement for a MJ is more complicated than for a 1 J cell and the special aspects to be considered when measuring the current-voltage characteristics and the performance of PVSK-containing MJ cells are emphasized. It is discussed why a spectrally adjustable solar simulator is needed and the sequence of accurate performance measurement, namely measurement of the spectral response of each junction, adjustment of the spectrum, and appropriate protocols for measuring the power output of the device at STC, is presented. For all these, common errors and pitfalls that sometime lead to misleading interpretation of the results are presented, and the methods to evaluate the accuracy of the data when a spectrally adjustable solar simulator is not available are recommended. Finally, first step is taken toward recommending performance measurement approaches when high throughput is required as will eventually be in a production line.

14 SOLAR ENERGY↗

Grid Resilient, Self-Powered, Fuel Flexible, High Efficiency Heating System (Final Report)

The Grid Independent High Efficiency System (GIHES) delivers a heating system that operates free from the power grid and provides ultra-low emissions and high efficiency heating for residential and commercial buildings. The project demonstrated two weeks of continuous grid independent operation of a storage water heater equipped with a powered damper. The Thermoelectric Generator (TEG) integrated GIHES technology revolutionizes heating systems to provide grid resilient hot water production, while delivering significant value to the end user by eliminating impacts of power interruption and enhancing comfort, convenience and productivity. The system maximizes the thermal-to-electric conversion efficiency by optimizing location and orientation of the TEGs and durability while lowering system costs. The TEGs generate enough power to charge a battery and run the storage water heater unit with a powered damper uninterrupted by providing the required parasitic electrical power for start-up, shutdown and during idling. Several TEGs were analytically and experimentally evaluated based on the size, surface area and the power being generated before designing and fabricating a complete multi-TEG assembly for integration with the water heater. The GIHES technology can be extended to boilers, furnaces and tankless water heaters and integrated with heat pump to further increase the overall equipment efficiency. The technology has the potential to reduce the amount of dispatch power and peak power plant operation. An advanced burner was designed, developed and 3D printed for fuel flexible operation. The burner was designed to enable tighter integration with TEGs for potentially higher TEG output, should this be necessary. A bench-scale test rig to perform testing of a 200,000 Btu/h burner and capable of handling up to 10% H2 (by volume) was setup at GTI’s laboratory with appropriate safety and controls to ensure smooth and safe operation. Testing of the 3D burner showed < 5 ppm NOx emissions can be achieved for the entire firing range and with different levels of hydrogen blends with natural gas. The burner will enable a more integrated TEG-burner module design for improved system performance, should this be required in the future. Techno-economic analysis (TEA) of the TEG integrated with the water heaters was performed. The testing data and the power output were used to develop the TEA. The analysis for the storage unit with damper showed that the TEG integrated system costs $95. This is much lower than a one-time charge needed to wire and install dedicated power. Discussions with TEG manufacturers and OEM’s will provide more detailed information and methods to further lower these costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Dual-Axis Radiographic Hydrodynamic Test Facility Capability eXpansion (DCX) Strategy

This document details the strategy proposed to meet the National Nuclear Security Administration (NNSA) requirement for the Dual-Axis Radiographic Hydrodynamic Test (DARHT) Facility Sustainment and Modernization projects identified in multiple NNSA Stockpile Stewardship and Management Plan (SSMP) reports to Congress [NNSA, 2020; 2021; 2022; 2023; 2024]. DARHT has become indispensable for the certification of the primaries of U.S. nuclear weapons since the current U.S. moratorium halted underground nuclear testing more than 30 years ago. However, aging facilities, evolving technology, and other issues are limiting or threatening the capability of DARHT to meet NNSA’s expanding mission needs now and into the future. This DARHT Capability eXpansion (DCX) strategy was developed to address NNSA needs by extending DARHT’s reliability and resilience, increasing the quality and quantity of DARHT data, and enabling hydrodynamic measurements in complex environments. The strategy can be implemented through integration of line-item projects, acquisition of major items of equipment, and other actions coordinated with the execution of the SSMP. This strategy document is intended for U.S. Government officials, particularly authorizers, appropriators, and program leaders. It may also be furnished to DARHT users, customers, collaborators, stakeholders, and visitors as needed

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗

Driving rotamak currents with minimal power dissipation

The Rotamak is a proposed thermonuclear fusion device which employs rotating magnetic fields (RMF) to generate an azimuthal current to produce a field-reversed configuration. The efficiency of the currents that produce the field reversal by RMFs was debated some 40 years ago. The debate revolved around whether the currents would incur dissipation by the conventional Spitzer perpendicular resistivity, or whether some other relation between current and dissipation would be more appropriate. By employing an electron–ion pitch-angle scattering model, we find that the dissipation is non-Spitzer in nature. However, curiously, there appears to exist a regime where the power dissipated to maintain the current becomes vanishingly small.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Investigating the use of field solvers for simulating classical systems

We explore the use of field solvers as approximations of classical Vlasov-Poisson systems. This correspondence is investigated in both electrostatic and gravitational contexts. We demonstrate the ability of field solvers to be excellent approximations of problems with cold initial condition into the nonlinear regime. We also investigate extensions of the Schrödinger-Poisson system that employ multiple stacked cold streams, and the von Neumann–Poisson equation as methods that can successfully reproduce the classical evolution of warm initial conditions. We then discuss how appropriate simulation parameters need to be chosen to avoid interference terms, aliasing, and wave behavior in the field solver solutions. Finally, we present a series of criteria clarifying how parameters need to be chosen in order to effectively approximate classical solutions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Tank Side Cesium Removal IXC-150 Cask Detonation Report

A set of experiments were conducted at Los Alamos National Laboratory’s firing point 88 to determine the blast pressure and effects of a stoichiometric gas mixture of Hydrogen (H 2 ) and Nitrous Oxide (N 2 O) on the IXC-150 Storage Pad Vent Stack assemblies. The Lab was asked to design, build, and test the vent stack assemblies using a reaction gas volume of 340 in 3 that mimics the Tank Side Cesium Removal (TSCR) Ion Exchange Column (IXC) assembly. The experimental system with vent stack assembly was designed with input from the Washington River Protection Services (WRPS) to ensure an accurate test setup. The tests were conducted at LANL on the newly built filter/gas assembly at the end of April 2021 timeframe, using WRPS supplied vent stack assemblies and internal HEPA filters. The objective of these tests was to observe that the structural integrity of the vent stack assemblies was maintained and determine the resulting blast overpressures at a distance of 16 and 32 inches from the assembly. All work was performed under the LANL Quality Assurance Program (SD330), using a graded application of ASME NQA-1- 2008/NQA-1a-2009. To ensure that all client quality assurance (QA) expectations were addressed, evaluation of the end data needs was performed and the appropriate controls applied for this work.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Multi-species collisions for delta-f gyrokinetic simulations: Implementation and verification with GENE

A multi-species linearized collision operator based on the model developed by Sugama et al. has been implemented in the nonlinear gyrokinetic code, GENE. Such a model conserves particles, momentum, and energy to machine precision, and is shown to have negative definite free energy dissipation characteristics, satisfying Boltzmann’s H-theorem, including for realistic mass ratio. Finite Larmor Radius (FLR) effects have also been implemented into the local version of the code. For the global version of the code, the collision operator has been developed to allow for block-structured velocity space grids, allowing for computationally tractable collisional global simulations. The validity of the collision operator has been demonstrated by relaxation and conservation tests, as well as appropriate benchmarks. The newly implemented operator shall be used in future simulations to study magnetically confined fusion plasma turbulence and transport in more extreme regions with higher collisionality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗

Effect of the Selective Halogenation of Small Molecule Acceptors on the Blend Morphology and Voltage Loss of High-Performance Solar Cells

Herein, the impacts of the selective halogenation at two different positions of dicyanomethylene-3-indanone (IC) end groups and inner side chains of small molecular acceptors (SMAs) on the P D :SMA interfacial interactions, blend morphology, and resulting photovoltaic properties are described. In this study, four different SMAs (A1, A2, A3, and A4) with the same molecular backbone, but with different degrees of halogenation, are synthesized. The IC end groups on the backbones of the A1 and A3, and A2 and A4 SMAs are chlorinated and fluorinated, respectively; in addition, 6-phenoxyhexyl inner side chains of the A3 and A4 are chlorinated. The SMAs are paired with a chlorinated PBDT-Cl PD to construct organic solar cells (OSCs). The PBDT-Cl:A4-based OSC exhibits the highest power conversion efficiency of 17.2%, outperforming the PBDT-Cl:A1-(13.3%), PBDT-Cl:A2-(15.6%), and PBDT-Cl:A3-based OSC (16.5%). The Cl atoms on the side chains in the A3 and A4 SMAs enhance the molecular/energetic interactions at the P D :SMA interfaces and improve the blend morphology in terms of domain purity and spacing. These effects lead to the improved fill factors and reduced voltage loss of the PBDT-Cl:A3- and PBDT-Cl:A4-based OSCs. This study demonstrates the importance of appropriate halogenation of SMAs in optimizing the blend morphology, reducing voltage loss, and improving OSC performance.

36 MATERIALS SCIENCE↗