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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Electricity Rate Designs for Large Loads: Evolving Practices and Opportunities 2026 Update

Electricity demand from large-load customers such as data centers is projected to grow significantly in the near term. While these large loads play an important role in advancing technology innovation and economic growth in the United States, meeting their energy needs requires utilities and regulators to consider important operational and financial risks, such as insufficient energy supply or underutilized investments, that can impact all customers. This paper builds on similar research published in January 2025, providing an overview of how utilities and regulators are managing these risks through different tariffs, including rate structures and electric service agreements. Regulators, utilities, customers, and other stakeholders can use this paper as a foundation when discussing issues and sharing perspectives on developing or reviewing large-load tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Evolving Role of Demand Charges in Retail Electricity Rates

Retail electricity demand charges refer to a type of retail rate that is based on a metric of kilowatt (kW) demand rather than kilowatt-hour (kWh) energy usage. Demand charges are widely used in the commercial and industrial (C&I) electricity sectors to recover significant portions of utility revenue and are also used in residential rates in a modest but growing number of locations. This paper explores the historical context of and motivations for demand charges, describes their implementation and impacts in today's context, and uses a variety of opinions collected from relevant parties through semi-structured interviews to inform how demand charges align with four widely accepted rate design principles. This project is funded by the Department of Energy's Office of Electricity, which is interested in conducting research to understand the current state of affairs related to demand charges and how the future U.S. electricity grid will help to define retail rates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Accelerating polyketide synthase engineering for high TRY production of biofuels and bioproducts (Final Technical Report)

Polyketide synthase (PKS) enzymes have a modular, deterministic logic that holds the potential to act as a flexible chemical factory for the biological production of a huge diversity of valuable small molecule compounds. However, engineering a custom PKS to produce a specific desired product currently requires years of trial and error, for reasons that remain poorly understood. In this project, we have developed a rapid, high throughput, Design-Build-Test-Learn (DBTL) cycle for polyketide synthases (PKSs) and demonstrate its utility for production of materials precursors. The objectives are 1) to develop a rapid, high-throughput (HT) DBTL cycle for PKSs that will enable production of a large number of unnatural, organic molecules on demand at high titer, rate, and yield (TRY); 2) to demonstrate the utility of the PKS DBTL cycle to produce three molecules: one commodity chemical (caprolactam or valerolactam) and two novel materials precursors (caprolactam or valerolactam derivatives); and 3) to demonstrate the utility of the PKS DBTL cycle to increase the TRY of one molecule (caprolactam or valerolactam). In this project, we have successfully demonstrated our high throughput PKS DBTL pipeline, and have biologically produced valerolactam and several other novel nylon monomers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerating polyketide synthase engineering for high TRY production of biofuels and bioproducts (CRADA Final Report)

Polyketide synthase (PKS) enzymes have a modular, deterministic logic that holds the potential to act as a flexible chemical factory for the biological production of a huge diversity of valuable small molecule compounds. However, engineering a custom PKS to produce a specific desired product currently requires years of trial and error, for reasons that remain poorly understood. In this project, we have developed a rapid, high throughput, Design-Build-Test-Learn (DBTL) cycle for polyketide synthases (PKSs) and demonstrate its utility for production of materials precursors. The objectives are 1) to develop a rapid, high-throughput (HT) DBTL cycle for PKSs that will enable production of a large number of unnatural, organic molecules on demand at high titer, rate, and yield (TRY); 2) to demonstrate the utility of the PKS DBTL cycle to produce three molecules: one commodity chemical (caprolactam or valerolactam) and two novel materials precursors (caprolactam or valerolactam derivatives); and 3) to demonstrate the utility of the PKS DBTL cycle to increase the TRY of one molecule (caprolactam or valerolactam). In this project, we have successfully demonstrated our high throughput PKS DBTL pipeline, and have biologically produced valerolactam and several other novel nylon monomers.

09 BIOMASS FUELS↗
DOE OSTI2025

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗
DOE OSTI2021

Disaggregating Future Retail Electricity Rate Growth [Slides]

Recent Berkeley Lab research found that modest retail rate increases over the past 10 years were mostly driven by large increases in capital expenditures (CapEx) that were offset in part by substantial wholesale price reductions. Decision-makers are increasingly concerned about the potential future rate impacts of a number of policies and industry trends that support rapid decarbonization, electrification, and grid modernization. Using historical FERC Form 1 data and the existing literature on policies and industry trends that are likely to affect utility-incurred costs and retail sales, Berkeley Lab researchers developed ranges of forecasted growth rates for cost-related rate drivers (i.e., fuel and purchased power; transmission, distribution, generation, and other categories of both non-fuel operations & maintenance and CapEx) and non-cost related rate drivers (i.e., retail sales, peak demand, and customers). These were then used as inputs to a pro-forma utility financial model (FINDER) that estimated the growth in retail electric rates between 2020 and 2030 for a prototypical vertically-integrated investor-owned utility in the United States. The analysis produced the following results: 1. Assuming average growth rates in all rate drivers, future retail rate growth is driven by sizable increases in all CapEx costs, where fuel and purchased power costs are replaced by generation CapEx as the largest rate component between 2020 and 2030. 2. Growth in sales/peak demand/customers, generation CapEx costs, and fuel and purchased power (FPP) costs, in isolation, produce the most uncertainty in rate growth. Specifically, a 1% increase in the compound annual growth rate (CAGR) of retail sales, coincident peak demand (CP), and customers (Sales-CP-Cust) results in a 0.88-0.93% decrease in the CAGR of rates, in isolation. However, a 1% increase in the CAGR of generation CapEx budgets results in a 0.07-0.14% increase in the CAGR of rates, while a 1% increase in the CAGR of FPP costs causes a 0.10-0.14% increase in the CAGR of rates, all else being equal. 3. Taking into account the correlation and variability of the growth in all rate drivers jointly, generation CapEx is expected to be both the largest and most uncertain rate component by 2030 (20-25% share of the retail rate). Transmission and distribution CapEx, along with fuel and purchased power costs are each expected to comprise between 12% and 17% of retail rates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Nucleation Rate Theory for Coordination Number: Elucidating Water-Mediated Formation of a Zigzag Na 2 SO 4 Morphology

Predicting and controlling nanostructure formation during nucleation can pave the way to synthesizing novel energy materials via crystallization. However, such control over nucleation and crystallization remains challenging due to an inadequate understanding of critical factors that govern evolving atomistic structures and dynamics. Here, in this paper, we utilize coordination number as a reaction coordinate and rate theory to investigate how sodium sulfate, commonly known as a phase-change energy material, nucleates in a supersaturated aqueous solution. In conjunction with ab initio and force field-based molecular dynamics simulation, the rate theoretical analysis reveals that sodium sulfate from an initially dissolved metastable state transits to a heterogeneous mixture of prenucleated clusters and finally to a large cylindrical zigzag morphology. Measurements of Raman spectra and their ab initio modeling confirm that this nucleated morphology contains a few waters for every sulfate. Rate processes such as solvent exchange and desolvation exhibit high sensitivity to the evolving prenucleation/nucleation structures, providing a means to distinguish between critical nucleation precursors. Desolvation and forming the first-shell interionic coordination structure via monomer-by-monomer addition around sulfates are found to explain the formation of large nuclei. Thus, a detailed understanding of the step-by-step structure formation across scales has been achieved. This can be leveraged to predict nucleation-related structures and dynamics and potentially control the synthesis of novel phase-change materials for energy applications.

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

Electrolyte Lifetime in Aqueous Organic Redox Flow Batteries: A Critical Review

Aqueous organic redox flow batteries (RFBs) could enable widespread integration of renewable energy, but only if costs are sufficiently low. Because the levelized cost of storage for an RFB is a function of electrolyte lifetime, understanding and improving the chemical stability of active reactants in RFBs is a critical research challenge. We review known or hypothesized molecular decomposition mechanisms for all five classes of aqueous redox-active organics and organometallics for which cycling lifetime results have been reported: quinones, viologens, aza-aromatics, iron coordination complexes, and nitroxide radicals. We collect, analyze, and compare capacity fade rates from all aqueous organic electrolytes that have been utilized in the capacity-limiting side of flow or hybrid flow/nonflow cells, noting also their redox potentials and demonstrated concentrations of transferrable electrons. We categorize capacity fade rates as being “high” (>1%/day), “moderate” (0.1–1%/day), “low” (0.02–0.1%/day), and “extremely low” (≤0.02%/day) and discuss the degree to which the fade rates have been linked to decomposition mechanisms. Capacity fade is observed to be time-denominated rather than cycle-denominated, with a temporal rate that can depend on molecular concentrations and electrolyte state of charge through, e.g., bimolecular decomposition mechanisms. We then review measurement methods for capacity fade rate and find that simple galvanostatic charge–discharge cycling is inadequate for assessing capacity fade when fade rates are low or extremely low and recommend refining methods to include potential holds for accurately assessing molecular lifetimes under such circumstances. We consider separately symmetric cell cycling results, the interpretation of which is simplified by the absence of a different counter-electrolyte. We point out the chemistries with low or extremely low established fade rates that also exhibit open circuit potentials of 1.0 V or higher and transferrable electron concentrations of 1.0 M or higher, which are promising performance characteristics for RFB commercialization. We indicate important directions for future research.

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

Evaluation of cosmogenic production of 39 Ar and 42 Ar for rare-event physics using underground argon

Underground argon (UAr) with lower cosmogenic activities of 39 Ar and 42 Ar has been planned as a detector in detecting scintillation light and charge collection using time projection chambers for dark matter searches and as a veto detector in suppressing backgrounds for neutrinoless double beta decay (0$vββ$ ) experiments. Long-lived radioactive isotopes, 39 Ar and 42 Ar, can also be produced on the surface when UAr is pumped out from a deep well. Understanding the production of long-lived isotopes in Ar is important for utilizing UAr for dark matter and 0$vββ$ experiments in terms of its production, transportation, and storage. Ar exposure to cosmic rays at sea-level is simulated using Geant4 for a given cosmic ray muon, neutron, and proton energy spectrum. Here we report the simulated cosmogenic production rates of 39 Ar, 42 Ar, and other long-lived isotopes at sea-level from fast neutrons, high energy muons, and high energy protons. Total production rates of 938.53/kg Ar day and 5.81 × 10 -3 /kg Ar day for 39 Ar and 42 Ar are found from our simulation. Utilizing these production rates, we set a time limit of 954 days constrained by the production of 39 Ar for UAr to be on the surface before it compromises the sensitivity for a dark matter experiment. Similarly, a time limit of 1702 days constrained by the production of 42 Ar is found for a 0$vββ$ experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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↗
DOE OSTI2022

Self-regulating power management for a neural network system

A neural network runs a known input data set using an error free power setting and using an error prone power setting. The differences in the outputs of the neural network using the two different power settings determine a high level error rate associated with the output of the neural network using the error prone power setting. If the high level error rate is excessive, the error prone power setting is adjusted to reduce errors by changing voltage and/or clock frequency utilized by the neural network system. If the high level error rate is within bounds, the error prone power setting can remain allowing the neural network to operate with an acceptable error tolerance and improved efficiency. The error tolerance can be specified by the neural network application.

Kegel, Andrew G.↗
DOE OSTI2023

Self-regulating power management for a neural network system

A neural network runs a known input data set using an error free power setting and using an error prone power setting. The differences in the outputs of the neural network using the two different power settings determine a high level error rate associated with the output of the neural network using the error prone power setting. If the high level error rate is excessive, the error prone power setting is adjusted to reduce errors by changing voltage and/or clock frequency utilized by the neural network system. If the high level error rate is within bounds, the error prone power setting can remain allowing the neural network to operate with an acceptable error tolerance and improved efficiency. The error tolerance can be specified by the neural network application.

Kegel, Andrew G.↗
DOE OSTI2023

Plant-wide modeling and techno-economic analysis of a direct non-oxidative methane dehydroaromatization process via conventional and microwave-assisted catalysis

Direct non-oxidative methane dehydroaromatization (DHA) process via conventional and microwave (MW)-assisted thermo-catalytic catalysis is studied. Rate models for methane DHA reactions, including the effect of catalyst deactivation, are developed by using the in-house experimental data. Model results for gas concentration profile and catalyst deactivation are in good agreement with the experimental data. This rate model is then used for the development of dynamic multi-scale, multi-physics commercial-scale reactor models. Total number of fixed bed reactors desired for a cyclic steady state process is estimated. Plant-wide models are then developed for conventional and MW-assisted processes for producing products of desired specifications. Techno-economic analysis of the methane DHA process is undertaken. Economics of these methane DHA processes are compared with the typical multi-step natural gas to aromatics production process via methanol synthesis. Sensitivity of internal rate of return (IRR) and net present value (NPV) to various economic and process parameters such as plant scale, desired rate of return, reactor cost, feedstock and utility cost, catalyst variable cost, and MW reactor cost is studied. Here, electric equivalent efficiency of the conventional methane DHA process is found to be 69.2 % and 67.3 % at 750 °C and 800 °C, respectively, while the MW-assisted methane DHA process has the electric equivalent efficiency of 48.9 % at 800 °C. IRRs of the conventional methane DHA process at 750 °C and 800 °C, and MW-assisted process are 15.2 %, 17.5 %, and 18.8 %, respectively for a methane feed flowrate of 19,782 kg/h, while the IRR of the multi-step natural gas to aromatics production process is estimated to be 0 % for the same plant scale. Impact of change in the methane price, electricity price, and catalyst cost is found to be considerable on the process economics, while the cost of the MW reactor is found to have negligible impact.

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

Growth mechanism of glucose-based hydrochar under the effects of acid and temperature regulation

Improving the tailorability of hydrochar synthesis is an effective way to enhance its performance and utilization efficiency. In this study, the growth rate, morphology, and molecular structure of hydrochar were controlled by regulating the pH and temperature of the hydrothermal carbonization process. Growth process analysis indicates that hydrochar has three growth periods: induction, rapid, and stable growth periods. It is mainly controlled by 5-hydroxymethylfurfural (HMF), which is formed by converting glucose and its transformation products. Further, the regulation of acid can significantly shorten the induction period of hydrochar, even under low-temperature conditions (<180 °C), and increase the growth rate of hydrochar. However, the degree of hydrochar adhesion varies: the lower the temperature, the greater the degree of its adhesion. Molecular structural analysis demonstrates that hydrochar mainly consists of furan structural domains and aromatic clusters, and its surface is rich in oxygen-containing functional groups. The degree at which hydrochar was aromatized was improved by increasing the reaction temperature (160–220 °C); whereas the regulation of acid reduced it and increased the content of oxygen-containing functional groups on the hydrochar surface. Based on these results, it is proposed that hydrochar formation has five stages and three growth periods with or without acid regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗
DOE OSTI2023

Investigation of carbon incorporation in laser-assisted MOCVD of GaN

Background carbon (C) impurity incorporation in metalorganic chemical vapor deposition (MOCVD) grown gallium nitride (GaN) represents one of the major issues in further improving GaN vertical power device performance. This work presents a laser-assisted MOCVD (LA-MOCVD) technique to address the high-C issue in MOCVD homoepitaxial GaN under different growth rate (Rg) regimes and studies the correlations between [C] and Rg. [C] in LA-MOCVD GaN is reduced by 50%–90% as compared to the conventional MOCVD GaN for a wide growth rate range between 1 and 16 μm/h. A mass-transport based model is developed to understand the C incorporation at different Rg regimes. The results obtained from the developed model are in good agreement with experimental data. The model further reveals that LA-MOCVD effectively suppresses C incorporation by reducing the active C species in the gas phase. Moreover, high step velocity in step flow growth mode can facilitate C incorporation at fast Rg, exhibiting steeper C increase. The theoretical model indicates that [C] can be suppressed below 1016 cm−3 with a fast growth rate (Rg) of 10 μm/h by utilizing higher power LA-MOCVD and freestanding GaN substrates with larger off-cut angles.

Physics↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗