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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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Temperature-dependent energy gain of bifacial PV farms: A global perspective
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Gains, losses, and uncertainties from computerizing referrals and consultations
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Reduction and potential remediation of U(VI) by dithionite at an in-situ recovery mine: Insights gained by δ 238 U
In-situ recovery is a common method of mining U ore. This technique involves injecting an oxidant to oxidize U deposited as insoluble U(IV) to soluble U(VI) and a complexing agent to form mobile U(VI) compounds, which are extracted from mining waters. Unfortunately, U concentrations often remain elevated in groundwater following this mining process. One promising technique to decrease U concentrations to acceptable levels is in-situ remediation where a reductant is injected into the mined area to reduce U(VI) back to insoluble U(IV). Here we assess the efficacy of the chemical reductant dithionite to reduce U(VI) concentrations. While dithionite can reduce environmental Cr(VI), dithionite has previously never been tested for reducing U(VI) in the environment. We determine how effectively dithionite remediates U(VI) contamination using aqueous U concentration and δ 238 U data. U concentrations and δ 238 U decrease during the dithionite experiment, demonstrating that dithionite induces U(VI) reduction. As dithionite is removed by pumping, U concentrations and δ 238 U increase towards background levels, but U(VI) reduction continues for an extended period. In conclusion, measurements demonstrate that re-oxidation and remobilization does not appear to occur due to the reducing condition of groundwater.
A new perspective on the initial hydrogenation of $\mathrm{TiFe_{0.9}M_{0.1}}$ ($\mathrm{M = V, Cr, Fe, Co, Ni}$) gained from surface oxide analysis and nucleation thermodynamics
Despite the promise of TiFe-based alloys as low-cost solid-state hydrogen storage materials with mild operating conditions and reasonable hydrogen capacity, their initial hydrogenation process is difficult, hindering broad utilization. The effect of alloying element on the initial hydrogenation kinetics of TiFe alloys, TiFe 0.9 M 0.1 (M = V, Cr, Fe, Co and Ni), was evaluated by analyzing changes to the passivating surface oxide layer that inhibits hydrogen permeation, as well as the ease of initial-stage hydrogen absorption into the underlying matrix. X-ray photoelectron spectroscopy and atom probe tomography revealed key variations in surface oxide compositions and thinning of the passivating oxide layer compared to pure TiFe, which suggests suppressed oxide growth by alloying-induced elemental redistribution. At the same time, density functional theory calculations predicted exothermic formation of hydride nuclei when alloying with V or Cr, as well as a reduced nucleation barrier when alloying with Co or Ni. Overall, these results are consistent with the observed experimental trend of the activation kinetics. We propose that improvements in activation kinetics of TiFe with alloying arises from the combined effect of reduced passivating oxide thickness and easier hydride nucleation, offering a starting point for alloy design strategies towards further improvement.
Soil fertility management for sustainable Miscanthus × giganteus production: Increased tiller weight from nitrogen management explains yield gains in aged miscanthus
Aging-related yield decline in Miscanthus × giganteus (miscanthus) remains a major constraint to sustainable biomass production. This study evaluated how nitrogen (N) management and soil fertility influence yield-component traits and productivity in aging miscanthus. Trials were conducted at two sites established in 2008 at the University of Illinois Energy Farm, Urbana, IL. (i) The Sun Grant trial received 0, 60, and 120 kg N ha −1 annually until 2015. Starting 2021, half of each plot received 60 or 120 kg N ha −1 , resulting in six legacy-contemporary treatments: 0N–0N, 0N–120N, 60N–0N, 60N–60N, 120N–0N, 120N–120N. (ii) The Energy Farm trial remained unfertilized until 2014, when one half of each plot received 56 kg N ha −1 , forming two treatments: 0N–0N, 0N–56N. Sun Grant trial results showed N fertilization increased tiller density (tillers m −2 ) and tiller weight (g tiller −1 ) in juvenile to early-mature miscanthus (2011–2015). After N withdrawal, both traits declined (20 % and 40 %), though legacy effects persisted in tiller weight in the aging stands (2020–2023). Contemporary N had little effect on tiller density but increased tiller weight by 34 %–77 %, resulting in 23 %–106 % higher machine-harvested biomass yield in 0–120N, 60-60N, and 120-120N plots. At the Energy Farm trial, 0N–56N plots yielded 59 %–108 % more biomass than 0N–0N. Soil total N increased (Sun Grant: 47 % by 2020; Energy Farm: 58 % by 2023), while Mehlich-3 P (42 %–44 %) and K (21 %–46 %) declined. These findings identify tiller weight as a key determinant of biomass yield in aging miscanthus and highlight the need for P and K management for long-term productivity.
DNA polymerase λ Loop1 variant yields unexpected gain-of-function capabilities in nonhomologous end-joining
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Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States
Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.
A Histologic Low-Grade Glioma with 7 Gain, 10 Loss—A Wolf in Sheep’s Clothing
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Gaining insight into lithium-ion battery degradation by a calorimetric approach
With the growing demand for lithium-ion batteries (LIBs) in transportation and renewable energy sectors, ensuring reliability and mitigating degradation are crucial challenges for their widespread deployment. Here this study investigates the relationship between thermal characteristics and degradation/reliability by developing an in-situ heat measurement methodology for LIBs operating under grid services. Using adiabatic mode-based calorimetry, we examine the thermal behavior of two commercial cells with different cathode chemistries (Ni-rich layered oxide and olivine) before and after two years of grid service (peak shaving) and demonstrate a strong correlation between thermal characteristics and electrochemical performance and its degradation. By further analyzing the heat sources separation (differentiating between irreversible and reversible heat), we disclose the underlying degradation mechanisms of LIBs. These findings emphasize the critical role of thermal characteristics in determining LIB reliability and deterioration, while the obtained heat data can aid in the development and calibration of LIB state-of-health models for grid applications.
Digital switch boards for IV and CV measurements of large-array Low Gain Avalanche Detectors
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Nitrogen induced soil carbon gains are resistant to loss after the cessation of excess nitrogen inputs
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On the impact of debris accumulation on power production of marine hydrokinetic turbines: Insights gained via LES
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Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling
The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.
Insights into Photosynthetic Energy Transfer Gained from Free-Energy Structure: Coherent Transport, Incoherent Hopping, and Vibrational Assistance Revisited
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