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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗
DOE OSTI2024

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Here, considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Linking Internal Dosimetries of the Propyl Metabolic Series in Rats and Humans Using Physiologically Based Pharmacokinetic (PBPK) Modeling

The metabolic series approach has successfully linked internal dosimetries of metabolically related compounds reducing cost and time for chemical risk assessments. Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the propyl metabolic series including propyl acetate, 1-propanol, propionaldehyde, and propionic acid. Manufacturers use these compounds as organic solvents and intermediates during chemical synthesis. Public exposures can occur through using consumer products containing propyl compounds like cosmetics, aerosol sprays, or foods. To develop the PBPK model, we measured in vitro metabolism of propyl acetate in blood and liver S9 fractions. We measured concentrations of propyl compounds in blood following intravenous (iv) infusion of 13C-propanol or 13C-propionic acid and closed chamber inhalation exposures to propyl acetate or propanol in rats. Using these studies and other published data, we modified an existing PBPK model for the butyl metabolic series to simulate time course concentrations of propyl compounds in rats and humans. Consistent with measured in vitro and in vivo data, the optimized propyl series model predicts rapid clearance of propyl acetate, higher concentrations of propanol in blood from propyl acetate inhalation compared to propanol inhalation, and low concentrations of propionic acid in blood from exposures to propyl acetate or propanol. Regulators can use this model as a tool for propyl compound risk assessment by linking internal dosimetries under various exposure scenarios.

Smith, Jordan N.↗
DOE OSTI2021

Interfacial Solar Evaporation by a 3D Graphene Oxide Stalk for Highly Concentrated Brine Treatment

In this study, we demonstrate a 3-dimensional graphene oxide (3D GO) stalk that operates near the capillary wicking limit to achieve an evaporation flux of 34.7 kg m -2 h -1 under 1 sun conditions (1 kW/m 2 ). This flux represents nearly a 100 times enhancement over a conventional solar evaporation pond. Interfacial solar evaporation traditionally uses 2D evaporators to vaporize water using sunlight, but their low evaporative water flux limits their practical applicability for desalination. Some recent studies using 3D evaporators demonstrate potential for more efficient water transfer, but the flux improvement has been marginal because of a low evaporation area index (EAI), which is defined as the ratio of the total evaporative surface area to the projected ground area. By using a 3D GO stalk with an ultrahigh EAI of 70, we achieved nearly a 20-fold enhancement over a 2D GO evaporator. The 3D GO stalk also exhibited additional advantages including omnidirectional sunlight utilization, a high evaporation flux under dark conditions from more efficient utilization of ambient heating, a dramatic increase of the evaporation rate by introducing wind, and scaling resistance in evaporating brines with a salt content of up to 17.5 wt %. This performance makes the 3D GO stalk well suited for the development of a low-cost, reduced footprint technology for zero liquid discharge in brine management applications.

3D evaporator↗
DOE OSTI2021

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm 3 between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Investigating the Effects of Copper Impurity Deposition on the Structure and Electrochemical Behavior of Hydrogen Evolution Electrocatalyst Materials

Electrolysis of impure water (such as seawater) has recently garnered research interest as it may enable hydrogen production at reduced costs. However, the tendency of impurity ions and other species to degrade electrocatalysts and membranes within an electrolyzer is a serious challenge. Here, we investigate the effects of copper impurities of varying concentrations on the hydrogen evolution reaction (HER) using platinum electrocatalysts. A decrease of current density is observed with an increasing copper concentration. By comparing the effect of ionic impurities on current density at different concentrations, we gain insight into how impurities can interfere with the HER at different potentials. Surface characterization of the electrodes reveals differences in the morphology and extent of copper deposition on HER-active platinum vs inactive gold electrodes. This enables an improved understanding of how copper nucleates and grows on the two types of electrodes under different electrochemical conditions while also confirming deposition in low-concentration cases, as present in seawater. The results indicate that copper electrodeposition competes with the HER, and the nature of copper electrodeposition varies depending on the electrocatalytic activity of the electrode. This study provides insight toward catalyst design that can withstand the effects of impurity-induced degradation over extended use.

08 HYDROGEN↗
DOE OSTI2025

Influence of Electrolyte Additives on Interfacial Stability of Manganese-Rich Lithium-Ion Battery Cathodes

Affordable, long-lasting energy storage has become critical to support increased electricity demand in recent years. Cobalt-free, lithium- and manganese-rich lithium nickel manganese oxide (LMR-NM) cathodes stand to reduce cost and supply-chain concerns associated with traditional cobalt-containing cathodes for lithium-ion batteries by leveraging more earth-abundant materials; however, they have shown issues with long-term cycling stability. Here, we investigate lithium difluoro(oxalate)borate (LiDFOB), tris(trimethylsilyl) phosphite (TMSPi), and vinylene carbonate (VC) electrolyte additives for their ability to improve cycling performance of LMR-NM (0.3 Li 2 MnO 3 + 0.7 LiMn 0.5 Ni 0.5 0 2 ) cells. Cryogenic scanning transmission electron microscopy (cryo-STEM) with electron energy loss spectroscopy enables the construction of a structure–function relationship between cathode electrolyte interphase (CEI) characteristics and the electrochemical performance of cells aged with these additives. We find the combination of 2 wt % TMSPi + 1 wt % LiDFOB performs better than any single additive, achieving a 28% improvement in specific capacity over the baseline electrolyte after long-term cycling. We attribute this to LiDFOB mitigating Mn ion dissolution, with cryo-STEM showing Mn stabilized up to the CEI surface, coupled with improved CEI structure and chemistry enabled by TMSPi, evidenced by a moderately thick (∼7–15 nm) CEI that appears to protect against further electrolyte reactions with the particle. These results, achieved through site-specific nanoscale characterization, directly reveal mechanisms through which electrolyte engineering can improve the performance of earth-abundant cathodes, enabling informed development of more affordable and reliable batteries to meet future energy storage needs.

25 ENERGY STORAGE↗
DOE OSTI2020

LiAlO 2 /LiAl 5 O 8 Membranes Derived from Flame-Synthesized Nanopowders as a Potential Electrolyte and Coating Material for All-Solid-State Batteries

Recently, γ-LiAlO 2 has attracted considerable attention as a coating in Li-ion battery electrodes. However, its potential as a Li + ceramic electrolyte is limited due to its poor ionic conductivity (<10 –10 S cm –1 ). Here, we demonstrate an effective method of processing LiAlO 2 membranes (<50 μm) using nanopowders (NPs) produced via liquid-feed flame spray pyrolysis (LF-FSP). Here, membranes consisting of selected mixtures of lithium aluminate polymorphs and Li contents were processed by conventional tape casting of NPs followed by thermocompression of the green films (100 °C/10 kpsi/10 min). The sintered green films (1100 °C/2 h/air) present a mixture of LiAlO 2 (~72 wt %) and LiAl 5 O 8 (~27 wt %) phases, offering ionic conductivities (>10 –6 S cm –1 ) at ambient with an activation energy of 0.5 eV. This greatly increases their potential utility as ceramic electrolytes for all-solid-state batteries, which could simplify battery designs, significantly reduce costs, and increase their safety. Furthermore, a solid-state Li/Li 3.1 AlO 2 /Li symmetric cell was assembled and galvanostatically cycled at 0.375 mA cm –2 current density, exhibiting a transference number ≈ 1.

25 ENERGY STORAGE↗
DOE OSTI2023

Critical Contribution of Imbalanced Charge Loss to Performance Deterioration of Si-Based Lithium-Ion Cells during Calendar Aging

Increasing the energy density of lithium-ion batteries, and thereby reducing costs, is a major target for industry and academic research. One of the best opportunities is to replace the traditional graphite anode with a high-capacity anode material, such as silicon. However, Si-based lithium-ion batteries have been widely reported to suffer from a limited calendar life for automobile applications. Heretofore, there lacks a fundamental understanding of calendar aging for rationally developing mitigation strategies. Both open-circuit voltage and voltage-hold aging protocols were utilized to characterize the aging behavior of Si-based cells. Particularly, a high-precision leakage current measurement was applied to quantitatively measure the rate of parasitic reactions at the electrode/electrolyte interface. The rate of parasitic reactions at the Si anode was found 5 times and 15 times faster than those of LiNi 0.8 Mn 0.1 Co 0.1 O 2 and LiFePO 4 cathodes, respectively. Here, the imbalanced charge loss from parasitic reactions plays a critical role in exacerbating performance deterioration. In addition, a linear relationship between capacity loss and charge consumption from parasitic reactions provides fundamental support to assess calendar life through voltage-hold tests. These new findings imply that longer calendar life can be achieved by suppressing parasitic reactions at the Si anode to balance charge consumption during calendar aging.

25 ENERGY STORAGE↗
DOE OSTI2025

Enhancing the Optically Detected Magnetic Resonance Signal of Organic Molecular Qubits

In quantum information science and sensing, electron spins are often purified into a specific polarization through an optical-spin interface, a process known as optically detected magnetic resonance (ODMR). Diamond-NV centers and transition metals are both excellent platforms for these so-called color centers, while metal-free molecular analogues are also gaining popularity for their extended polarization lifetimes, milder environmental impacts, and reduced costs. In our earlier attempt at designing such organic high-spin π-diradicals, we proposed to spin-polarize by shelving triplet M S = ±1 populations as singlets. This was recently verified by experiments albeit with low ODMR contrasts of <1% at temperatures above 5 K. In this work, we propose to improve the ODMR signal by moving singlet populations back into the triplet M S = 0 sublevel, designing a true carbon-based molecular analogue to the NV center. Our proposal is based upon transition-orbital and group-theoretical analyses of beyond-nearest-neighbor spin–orbit couplings, which are further confirmed by ab initio calculations of a realistic trityl-based radical dimer. Microkinetic analyses point toward high ODMR contrasts of around 30% under experimentally feasible conditions, a stark improvement from previous works. Finally, in our quest toward ground-state optically addressable molecular spin qubits, we exemplify how our symmetry-based design avoids Zeeman-induced singlet–triplet mixings, setting the scene for realizing electron spin qubit gates.

Group theory↗
DOE OSTI2026

Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This Perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

Algorithms↗
DOE OSTI2024

Quantifying the Value of Technology and Policy Innovation in Water Resource Portfolios

Abstract Recent water infrastructure planning models demonstrate that explicitly accounting for hydrological changes in long‐term water planning reduces costs and increases system robustness. However, current models do not consider the effects of other changes occurring in the system over long time horizons, namely technology and policy innovation. By leveraging state‐of‐the‐art dynamic control techniques, we propose a suite of tools that explicitly relate hydrologic change, technology innovation, and policy interventions to system‐level water costs. We design robust and cost‐optimal strategies for water supply expansion under hydrological variability and hundreds of plausible innovation states, for example, treatment cost and diversification improvements, deployment times, and demand reduction campaigns. Our approach circumvents the need to pre‐assign probabilities to innovation scenarios and, instead, directly calculates the effects of technology, policy, and hydrology variations on system cost and planning strategies to identify avenues of impactful innovation. These tools can support technology development hubs, urban, state, and federal water planners in anticipating cost implications of technology and policy innovation at the local level, and prioritizing high‐impact innovation goals based on quantitative targets. Results for the case study of Santa Barbara, California identify high‐value innovation attributes for the city as well as combinations of innovation attributes across technologies and policies, and quantify their potential utility cost reductions.

13 HYDRO ENERGY↗
DOE OSTI2021

422 Million intrinsic quality factor planar integrated all-waveguide resonator with sub-MHz linewidth

High quality-factor (Q) optical resonators are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration in a photonic waveguide platform is key to reducing cost, size, power and sensitivity to environmental disturbances. However, to date, the Q of all-waveguide resonators has been relegated to below 260 Million. Here, we report a Si 3 N 4 resonator with 422 Million intrinsic and 3.4 Billion absorption-limited Qs. The resonator has 453 kHz intrinsic, 906 kHz loaded, and 57 kHz absorption-limited linewidths and the corresponding 0.060 dB m -1 loss is the lowest reported to date for waveguides with deposited oxide upper cladding. These results are achieved through a careful reduction of scattering and absorption losses that we simulate, quantify and correlate to measurements. This advancement in waveguide resonator technology paves the way to all-waveguide Billion Q cavities for applications including nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recycling lead and transparent conductors from perovskite solar modules

Abstract Perovskite photovoltaics are gaining increasing common ground to partner with or compete with silicon photovoltaics to reduce cost of solar energy. However, a cost-effective waste management for toxic lead (Pb), which might determine the fate of this technology, has not been developed yet. Here, we report an end-of-life material management for perovskite solar modules to recycle toxic lead and valuable transparent conductors to protect the environment and create dramatic economic benefits from recycled materials. Lead is separated from decommissioned modules by weakly acidic cation exchange resin, which could be released as soluble Pb(NO 3 ) 2 followed by precipitation as PbI 2 for reuse, with a recycling efficiency of 99.2%. Thermal delamination disassembles the encapsulated modules with intact transparent conductors and cover glasses. The refabricated devices based on recycled lead iodide and recycled transparent conductors show comparable performance as devices based on fresh raw materials. Cost analysis shows this recycling technology is economically attractive.

14 SOLAR ENERGY↗
DOE OSTI2021

Quantum repeaters based on concatenated bosonic and discrete-variable quantum codes

Abstract We propose an architecture of quantum-error-correction-based quantum repeaters that combines techniques used in discrete- and continuous-variable quantum information. Specifically, we propose to encode the transmitted qubits in a concatenated code consisting of two levels. On the first level we use a continuous-variable GKP code encoding the qubit in a single bosonic mode. On the second level we use a small discrete-variable code. Such an architecture has two important features. Firstly, errors on each of the two levels are corrected in repeaters of two different types. This enables for achieving performance needed in practical scenarios with a reduced cost with respect to an architecture for which all repeaters are the same. Secondly, the use of continuous-variable GKP code on the lower level generates additional analog information which enhances the error-correcting capabilities of the second-level code such that long-distance communication becomes possible with encodings consisting of only four or seven optical modes.

Physics↗
DOE OSTI2023

Using machine learning with optical profilometry for GaN wafer screening

Abstract To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques—including optical profilometry—produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available. For this research project, we fabricated over 6000 vertical PiN GaN diodes across 10 wafers. Using low resolution wafer scale optical profilometry data taken before fabrication, we successfully trained four different machine learning models. All models predict device pass and fail with 70–75% accuracy, and the wafer yield can be predicted within 15% error on the majority of wafers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantitative phase imaging by gradient retardance optical microscopy

Abstract Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.

47 OTHER INSTRUMENTATION↗