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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Benchmarking and Fidelity Response Theory of High-Fidelity Rydberg Entangling Gates

The fidelity of entangling operations is a key figure of merit in quantum information processing, especially in the context of quantum error correction. High-fidelity entangling gates in neutral atoms have seen remarkable advancement recently. A full understanding of error sources and their respective contributions to gate infidelity will enable the prediction of fundamental limits on quantum gates in neutral atom platforms with realistic experimental constraints. In this work, we implement the time-optimal Rydberg controlled-Z (CZ) gate, design a circuit to benchmark its fidelity, and achieve a fidelity, averaged over symmetric input states, of 0.9971 ( 5 ) , downward corrected for leakage error, which together with our recent work [Nature 634, 321–327 (2024)] forms a new state of the art for neutral atoms. The remaining infidelity is explained by an error model, consistent with our experimental results over a range of gate speeds, with varying contributions from different error sources. Further, we develop a fidelity response theory to efficiently predict infidelity from laser noise with nontrivial power spectral densities and derive scaling laws of infidelity with gate speed. Besides its capability of predicting gate fidelity, we also utilize the fidelity response theory to compare and optimize gate protocols, to learn laser frequency noise, and to study the noise response for quantum simulation tasks. Finally, we predict that a CZ gate fidelity of ≳ 0.999 is feasible with realistic experimental upgrades. Published by the American Physical Society 2025

Tsai, Richard Bing-Shiun (ORCID:0000000286758677)↗
DOE OSTI2020

The organic redox transistor for neuromorphic computing

Inspired by the in-memory computing architectures of biological systems, neuromorphic computing using crossbar arrays of artificial synapses based on non-volatile memory (NVM) devices with variable conductances has emerged as a new paradigm to enable massively parallel and ultra-low power computing hardware for data centric applications. Although inference has been demonstrated successfully using crossbars based on a variety of NMV technologies, efficient learning and scaling to large arrays (>10 6 elements) remains a challenge due to the synaptic elements' non-ideal electrical characteristics which degrades ANN accuracy. A further challenge is that in the conductive state memristors draw large currents >μA resulting in significant voltage drops in the interconnect wires and increased probability of failure in scaled arrays. We suggest the organic polymer redox transistor (RT) is an alternate approach that could solve many of these challenges, enabling both inference and parallel outer product updates, as recently demonstrated by Fuller et al. An RT consists of redox-active channel and gate electrodes in contact with a liquid or solid electrolyte. lon insertion through the electrolyte controls the channel electronic conductivity, while electron transfer through an external circuit maintains overall charge neutrality. Unlike a rechargeable battery, in the RT the voltage built-up across the electrolyte is kept to a minimum (typically <100 mV) by using the same material for the gate and channel. Elimination of the voltage offset simplifies integration of the RT into programmable arrays by enabling the use of various selectors. RTs based on inorganic and organic materials have been recently demonstrated with conductance tuning occurring at potentials of just a few mV and hundreds to thousands of linearly and symmetrically programmable conductance states, enabling near ideal accuracy in neural network simulations. Introduced in the 1980's, redox transistors with metallic gate electrodes and organic channel materials, also known as organic electrochemical transistors (OECTs), have been explored for a variety of applications such as chem- and bio-sensing, neural interfaces, and low cost printed circuits. A typical channel material for OECTs is the conducting polymer poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS). PEDOT is a p-type semiconducting polymer with mobile positively charged polarons that hop chain-to-chain.

Talin, Albert Alec↗
DOE OSTI2024

Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

14 SOLAR ENERGY↗
DOE OSTI2025

High-Throughput Microfluidic Electroporation (HTME): A Scalable, 384-Well Platform for Multiplexed Cell Engineering

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME's core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform's ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME's potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck.

Gaillard, William R↗
DOE OSTI2023

An Integrated Testbed for Trojans in Printed Circuit Boards with Fuzzing Capabilities

This paper showcases an all-in-one testing environment that combines Trojan detection and fuzzing capabilities for printed circuit boards using the OpenPLC “NYU Trojan Edition” and a dedicated Trojan detection framework. The demo system is self-contained and equipped with two OpenPLC-based boards (one with a Trojan and one without), and automated tools for inserting the Trojan and collecting side-channel data. We developed a graphical user interface for interactive Trojan selection, data visualization, and anomaly detection analysis.

Trojan detection, anomaly detection, PCB, timing l↗
DOE OSTI2022

Chapter 10: Advances in Protein Engineering and its Application in Synthetic Biology

Protein engineering has been used successfully in fields ranging from medicine to food science to biofuels. Applications of protein engineering include developing antiviral peptides or other protein therapeutics, antibody engineering, designing protein-based logic circuits, engineering enzymes to be more specific or to function under industrially relevant conditions such as at higher temperatures or high/low pH, modifying cell signaling or regulatory functions, and so on. Advances in recombinant DNA, "omics," and CRISPR-Cas (clustered regularly interspaced short palindromic repeats and its associated proteins) technologies, combined with high-throughput screening facilities, will lead to improved methods for protein engineering, enabling easy modification of more proteins/enzymes for new specific applications. New methods for rational design, directed evolution, and computer-aided protein design will further accelerate the speed of protein evolution and expand the scope for protein engineering. In this chapter we discuss general protein engineering strategies and advances in engineering proteins with desired functions, focusing on the "design" and "build" part of the design-build-test-learn cycle.

BIOMASS FUELS↗
DOE OSTI2024

Twisty-puzzle-inspired approach to Clifford synthesis

The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's cube twisty puzzle, here we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is probabilistic and computationally intensive. However, when a decomposition is successfully found, it often involves fewer gates than the decomposition methods used in the Qiskit decomposition protocol, which uses a combination of several well-known Clifford decomposition schemes. Additionally, our approach is much more flexible than existing algorithms in that arbitrary gate sets, device topologies, and gate fidelities may be incorporated, thus allowing for the approach to be tailored to a specific device.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules

We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H 2 , LiH, and BeH 2 . The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

A multilayer multi-configurational approach to efficiently simulate large-scale circuit-based quantum computers on classical machines

Here, a multilayer multi-configurational theory framework is adapted to simulate circuit-based quantum computers. Quantum addition of superpositions of an exponential number of summands is performed in polynomial time with high accuracy. We demonstrate numerically accurate calculations including up to one million qubits for entangling benchmarks. Simulation cost can be assessed by entropy-based entanglement measures. For the considered systems, we show that the entanglement only grows weakly with the system size. The present simulations demonstrate how quantum algorithms in low-entropy regimes can be used efficiently on classically simulated quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-throughput chemical imaging for optimizing biofuel synthesis using synthetic biology (Final Technical Report)

Fatty acids can be produced biosynthetically in microbes and these compounds can serve as precursors to biodiesels and other high value oleochemicals. However, progress on engineering fatty acid biosynthesis, and biofuel synthesis more generally, has been hindered by current quantification methods that are either indirect or not amenable to high-throughput or single-cell resolution screening. In this project, we assembled an interdisciplinary team with complimentary expertise in synthetic biology and microscopy, metabolic engineering, and chemical imaging to address these challenges. We used chemical imaging to directly measure lipid biosynthesis in Escherichia coli engineered to produce fatty acids, obtaining detailed single-cell resolution measurements. We deployed stimulated Raman scattering (SRS) microscopy in concert with multiplexed genome engineering and gene circuit design strategies from synthetic biology to optimize production of fatty acids. These results provided novel insight into cell-to-cell heterogeneity present in biofuel production strains. In addition, we introduced new chemical imaging methods which are label-free and do not require fluorescent reporters. These efforts were complemented by other studied developing foundational tools for regulation and control, which offer excellent potential for advancing researchers’ ability to rapidly design, build, and test strains for enhanced biofuel synthesis.

09 BIOMASS FUELS↗
DOE OSTI2022

AI-Based Protective Relays for Electric Grid Resiliency

The protection systems (circuit breakers, relays, reclosers, and fuses) of the electric grid are the primary component responding to resilience events, ranging from common storms to extreme events. The protective equipment must detect and operate very quickly, generally <0.25 seconds, to remove faults in the system before the system goes unstable or additional equipment is damaged. The burden on protection systems is increasing as the complexity of the grid increases; renewable energy resources, particularly inverter-based resources (IBR) and increasing electrification all contribute to a more complex grid landscape for protection devices. In addition, there are increasing threats from natural disasters, aging infrastructure, and manmade attacks that can cause faults and disturbances in the electric grid. The challenge for the application of AI into power system protection is that events are rare and unpredictable. In order to improve the resiliency of the electric grid, AI has to be able to learn from very little data. During an extreme disaster, it may not be important that the perfect, most optimal action is taken, but AI must be guaranteed to always respond by moving the grid toward a more stable state during unseen events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation–dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. Here, we also propose practical criteria for AIGLE to enforce long-term DC. Case studies of a toy polymer, with 20 CG sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling CG conformational dynamics in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Applying Improved Optical Recognition with Machine Learning on Sorting Cu Impurities in Steel Scrap

Annually, 20–55 million tons of electronic waste (e-waste) is produced worldwide (5% of all municipal solid waste). Although e-waste embodies only 2% of America’s municipal waste, it accounts for a significantly larger proportion of the heavy metals and flame retardants present in the waste stream. Currently, less than 20% of all e-waste is recycled in the United States because the heterogeneity of the feedstock limits the opportunity for reuse in high value products and processing of the waste itself is often too costly to justify handling. To address this concern, the aims of this study are: 1) to identify the major plastic and metal compositions within electronic shredder residue (ESR), 2) to formulate solvents and processing conditions to separate 90% of the plastics targeted from consumer shred ESR, and 3) to develop a process design model to estimate the cost and energy efficiency of the proposed solvent-based processing. In this study, a pre-sorted heterogeneous ESR feedstock (one where aluminum, magnetic components, and hazardous battery materials removed by an e-waste recycling facility) was used, with the major compositions of the ESR characterized. It was found that 25 wt.% of the feedstock was composed of plastics, 6 wt.% rubber, 27 wt.% printed circuit boards, 23% wire, and the remainder metals and capacitors. Within the plastic portion, polystyrene (PS, 40 wt.%), acrylonitrile butadiene styrene (ABS, 25 wt.%), and styrene-acrylonitrile (SAN, 9wt.%) were identified to compose the majority of the screened plastics using Fourier transform infrared spectroscopy (FTIR). Next, selective solvents were screened using Hansen Solubility Parameter Theory (HSP) for dissolving PS and ABS. The pre-screening results show that methylene chloride (dichloromethane, DCM) and tetrahydrofuran (THF) are capable of dissolving the most PS and ABS, while methanol (MeOH) and ethylene glycol (EG) are capable of precipitating the most PS and ABS. These solvents were subsequently used to recover polymers and remove. flame retardants within the ESR feedstock. By optimizing the dissolution time and the solvents used, the highest polymer dissolution yield (99 wt.%%) was achieved using DCM for 48 hr. Both pre-screened anti-solvents (MeOH and EG) showed the highest polymer precipitation yield (71 wt.%). In terms of flame retardant removal rate, EG was found to have a high phosphorus-containing flame retardant removal rate (up to 98%). Characterization shows that the proposed solvent-based processing can preserve a high molecular weight fraction of the polymers and effectively remove flame retardants. Cost analysis indicates that the amount of the solvent/anti-solvent recovered after the reaction would play a critical role in reducing the operating costs. The energy analysis shows that the proposed solvent-based processes can save up to 60% of the embodied energy used to manufacture plastics used in electronics (PS and ABS were used for calculations). The results from this project prove the potential of solvent-based processing to produce secondary materials (plastics and metals) from e-waste for cross-industry reuse.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Characterizing electrical panel capacity, breaker space, and loads in U.S. single-family homes

Electrical panels distribute electricity to appliances, equipment, and other building loads and maintain safety by preventing overloads of branch circuits, feeders, and utility service wires connecting homes to the grid. Load calculations in the National Electrical Code (NEC) determine the capacity of installed electrical panels in the United States, which are rated in amps (A). Circuit breakers within electrical panels affect the magnitude and number of loads that panels can serve. In home retrofits and renovations, existing electrical panels may not accommodate new electrical loads, either due to insufficient electrical capacity or a lack of available breaker spaces. Homes with these panel constraints may need to replace existing panels with new panels that have greater capacity and breaker space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Progress toward favorable landscapes in quantum combinatorial optimization

The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for solving the combinatorial optimization problem MaxCut, and study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut objective function. In order to analytically characterize the impact of quantum features on the critical points of the landscape, we consider a family of quantum circuit ansätze composed of mutually commuting elements. We identify multiqubit operations as a key resource and show that overparameterization allows for obtaining favorable landscapes. Namely, we prove that an ansatz from this family containing exponentially many variational parameters yields a landscape free of local optima for generic graphs. However, we further prove that these ansätze do not offer superpolynomial advantages over purely classical MaxCut algorithms. Here, we then present a series of numerical experiments illustrating that noncommutativity and entanglement are important features for improving algorithm performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Efficient, Compact, and Smooth Variable Propulsion Motor (Final Report)

In this project, a new architecture of highly efficient hydraulic motor was developed for the propulsion of off-highway vehicles. The motor uses an adjustable linkage driving a cam to vary the displacement of the piston, resulting in a Variable Displacement Linkage Motor (VDLM). The motor uses low friction rolling element bearings to significantly reduce mechanical friction, especially in the demanding low-speed high-torque conditions experienced by off-highway vehicles. The VDLM has high torque capabilities for its size due to the radial piston packaging and use of a multi-lobe cam. A VDLM is very smooth due to the ability to tune the torque ripple through the design of the cam profile. The project was divided into three periods. During the first period, a dynamic model was constructed of the motor to predict the performance of the motor and the vehicle. During the second period, a single-cylinder learning prototype was designed, built, and tested to validate the models constructed in the first period. In the third period, a multi-cylinder prototype motor was optimized, designed, fabricated, and tested. The motor demonstrated excellent mechanical efficiency (above 92.5% across the range of displacements), but the experimentally measure volumetric efficiency was lower than expected due to higher leakage rates created by the poor tolerance control on the prototype. To validate the dynamic models developed in the first period and better understand design trade-offs. In the third period a multi-cylinder concept demonstration prototype will be designed, fabricated, and tested. The final prototype will be tested on a motor dynamometer and will be utilized in hardware-in-the-loop testing to demonstrate its efficiency and performance impacts on the overall drive train. The experimental results were used in a drive train simulation of a compact track loader operating through a drive cycle. Using the VDLM in a hydrostatic circuit yielded 17.1% reduction in fuel consumption and 36.5% reduction in a series hybrid transmission.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

AI-enabled traveling wave protection for microgrids

Grid forming inverters provide voltage and frequency regulations for microgrids; in the meantime, new challenges are introduced for microgrid protections. For instance, inverters’ control strategies can affect protection behaviors, and low short-circuit ratios and bi-directional power flows also make protection operations complex. Protection schemes based on conventional principles such as overcurrent and distance relays do not always provide reliable, sensitive, or selective operations. Here, we devise a traveling wave protection approach for microgrids using a wavelet-driven deep neural network named WaveletKernelNet (WKN). Compared with conventional methods, the presented approach provides enhanced sensitivity, higher selectivity, and better identification of various faults in microgrids. Extensive case studies validate the efficacy and excellent performance of the devised approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗