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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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Correction for ‘Ion pair extractant selective for LiCl and LiBr’ by Nam Jung Heo et al. , Chem. Sci. , 2024, 15 , 13958–13965, https://doi.org/10.1039/D4SC03760J.
Correction for ‘Infrared spectroscopy for understanding the structure of Nafion and its associated properties’ by Tanya Agarwal et al. , J. Mater. Chem. A , 2024, https://doi.org/10.1039/D3TA05653H.
Correction for ‘Acid–base concentration swing for direct air capture of carbon dioxide’ by Anatoly Rinberg and Michael J. Aziz, Energy Adv. , 2024, https://doi.org/10.1039/d4ya00251b.
[This corrects the article DOI: 10.1039/D5DD00019J.].
Correction for ‘Dicopper( i ) complexes of a binucleating, dianionic, naphthyridine bis(amide) ligand’ by Laurent Sévery et al. , Dalton Trans. , 2025, https://doi.org/10.1039/d5dt00034c.
Correction for ‘Reductive catalytic fractionation of agricultural residue and energy crop lignin and application of lignin oil in antimicrobials’ by Elvis Osamudiamhen Ebikade et al. , Green Chem. , 2020, 22 , 7435–7447, https://doi.org/10.1039/D0GC02781B.
Correction for ‘Cost-effective carbon fiber precursor selections of polyacrylonitrile-derived blend polymers: carbonization chemistry and structural characterizations’ by Qian Mao et al. , Nanoscale , 2022, 14 , 6357–6372, https://doi.org/10.1039/D2NR00203E.
Correction for ‘A comprehensive approach for elucidating the interplay between 4f n +1 and 4f n 5d 1 configurations in Ln 2+ complexes’ by Maria J. Beltran-Leiva et al. , Chem. Sci ., 2025, 16 , 2024–2033, https://doi.org/10.1039/d4sc05438e.
Correction for ‘Interfacial rheology of lanthanide binding peptide surfactants at the air–water interface’ by Stephen A. Crane et al. , Soft Matter , 2024, 20 , 9161–9173, https://doi.org/10.1039/D4SM00493K.
Correction for ‘Anharmonic effects on the dynamical stability of Ce–Co–Cu intermetallic ternary compounds’ by Wei-Shen Tee et al., RSC Adv., 2026, 16, 14395–14405, https://doi.org/10.1039/D5RA09680D. In the original article, the Acknowledgements section was omitted. The Acknowledgements section for this article is given below.
The interactions between the electronic magnetic moment and the nuclear spin moment, i.e., magnetic hyperfine (HF) interactions, play an important role in understanding electronic properties of magnetic systems and in realizing platforms for quantum information science applications. We investigate the HF interactions for atomic systems and small molecules, including Ti or Mn, by using Fermi–Löwdin orbital (FLO) based self-interaction corrected (SIC) density-functional theory. We calculate the Fermi contact (FC) and spin-dipole terms for the systems within the local density approximation (LDA) in the FLO-SIC method and compare them with the corresponding values without SIC within the LDA and generalized-gradient approximation (GGA), as well as experimental data. For the moderately heavy atomic systems (atomic number Z ≤ 25), we find that the mean absolute error of the FLO-SIC FC term is about 27 MHz (percentage error is 6.4%), while that of the LDA and GGA results is almost double that. Therefore, in this case, the FLO-SIC results are in better agreement with the experimental data. For the non-transition-metal molecules, the FLO-SIC FC term has the mean absolute error of 68 MHz, which is comparable to both the LDA and GGA results without SIC. For the seven transition-metal-based molecules, the FLO-SIC mean absolute error is 59 MHz, whereas the corresponding LDA and GGA errors are 101 and 82 MHz, respectively. Therefore, for the transition-metal-based molecules, the FLO-SIC FC term agrees better with experiment than the LDA and GGA results. We observe that the FC term from the FLO-SIC calculation is not necessarily larger than that from the LDA or GGA for all the considered systems due to the core spin polarization, in contrast to the expectation that SIC would increase the spin density near atomic nuclei, leading to larger FC terms.
Predictable circuit response is a critical prerequisite for accurate electronic measurements. Here, we describe a powerful yet straightforward experimental method and analysis model that utilizes an affordable LCR meter in conjunction with an in situ parasitic-impedance background-correction procedure to measure the temperature-dependent impedance (magnitude and phase) of up to ten individual passive circuit elements in a single cryostat run. We show how the model unambiguously identified a ∼20× drop in capacitance for 22 μF 5XR multilayer ceramic capacitors cooled from 300 K to 360 mK in an environment with a parasitic capacitance of ∼300 pF. The same experimental procedure, based on a simple two-wire measurement, was also used to measure 10 and 22 pF thin-film capacitors and 100 MΩ thick-film resistors. The results showed that the resistor values increased by up to an order of magnitude when the devices were cooled from 300 K to 360 mK. Most importantly, we showed that the simple data-acquisition method, coupled with our analysis model, enabled the measurement of component parasitics, and effectively extended the accuracy of a commercial LCR meter beyond its manufacturer-guaranteed values for a wide range of measurement frequencies. We also showed that, in the current configuration, our simple approach is limited to a capacitance accuracy of ∼10 pF.
In laser-heated diamond anvil cell (DAC) experiments, the effective heated region typically decreases in size with increasing pressure, leading to steeper thermal gradients. Under these conditions, chromatic aberration in the optical path from sample to detector can significantly create bias in spectro-radiometric temperature measurement. We present a radiance-mapping approach using a hyperspectral camera that records 25 spectral channels spanning 605–875 nm at each pixel in a single exposure, providing spatially and spectrally resolved radiance in each frame. This enables chromatic effects to be recorded and corrected in data processing. We developed a procedure for hyperspectral mapping, involving per-camera calibration, crosstalk removal, measured spectral throughput functions, and optional sub-pixel co-registration to minimize chromatic distortion. The calibrated radiance maps are then used to derive temperature maps of the laser-heated hotspots. For smaller heating spots, the radiance mapping approach reveals chromatic shifts that conventional spectro-radiometric methods cannot quantify. Ambient-pressure heating experiments confirm accurate temperature retrieval. At high pressure, application of the hyperspectral system to a platinum-heating experiment at 12 GPa demonstrates stable temperature reconstruction under steep thermal gradients. Beyond mitigating chromatic aberrations, the ability to diagnose optical artifacts separately from emissivity variations during controlled test experiments or in situ suggests a path toward more rigorous spectral emissivity analysis and improved modeling of thermal transport in laser-heated DAC experiments.
Gibbs free energies of clusters are required for predictive modeling of cluster growth during condensation of a cooling vapor. Here, we present a straightforward method of calculating free energies of cluster formation using the data from molecular dynamics (MD) simulations. We apply this method to iron clusters having from 2 to 100 atoms. The energies obtained are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation, which relies on a surface tension coefficient of a flat surface, as it is used in the classical nucleation theory (CNT). We show that the spherical cluster approximation in CNT can be improved by using a cluster-size-dependent Tolman correction for the surface tension. The Tolman length and effective surface tension values were derived for iron clusters, and they significantly differ from the commonly used experimentally measured values. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to more accurately model cluster formation from a cooling vapor. It better reproduces the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. In contrast, using a constant surface tension coefficient (as done in CNT) resulted in incorrect condensation dynamics and cluster size distributions. The analytical expression for cluster nucleation rate from CNT was updated to account for the size-dependence of cluster surface tension.
Here, we present an approach for including relativistic corrections in lepton-nucleus scattering calculations within the short-time approximation (STA). Previous ab initio studies employed electromagnetic currents expanded in powers of 𝑞/𝑚, where 𝑞 is the momentum transfer and 𝑚 is the nucleon mass, restricting their validity to low-𝑞 kinematics. We adopt an expansion scheme that treats the initial nucleon momentum perturbatively while allowing for arbitrary momentum transfer, thereby extending the applicability of the STA to high-𝑞 regimes. Additionally, we incorporate a relativistic treatment of the two-nucleon final-state energies. Calculations for 3 He and 4 He inclusive electron-scattering cross sections show a substantial improvement over previous results, achieving good agreement with experimental data in the quasi-elastic region for both low- and high-momentum transfer.
The minority-spin Fe/MgO interface states are at the Fermi level in density functional theory (DFT), but experimental evidence and GW calculations place them slightly higher in energy. This small shift can strongly influence tunneling magnetoresistance (TMR) in junctions with a thin MgO barrier and its dependence on the concentration of Co in the electrodes. Here, in this study, an empirical potential correction to DFT is introduced to shift the interface states up to match the tunnel spectroscopy data. With this shift, TMR in Fe/MgO/Fe junctions exceeds 800% and 3000% at 3 and 4 monolayers (ML) of MgO, respectively. We further consider the effect of alloying of the Fe electrodes with up to 30% Co or 10% V, treating them in the coherent potential approximation (CPA). Alloying with Co broadens the interface states and brings a large incoherent minority-spin spectral weight to the Fermi level. Alloying with V brings the minority-spin resonant states close to the Fermi level. However, in both cases the minority-spin spectral weight at the Fermi level resides primarily at the periphery of the Brillouin zone, which is favorable for spin filtering. Using convolutions of k II -resolved barrier densities of states calculated in CPA, it is found that TMR is strongly reduced by alloying with Co or V but still remains above 500% at 4 ML of MgO up to 30% of Co or 5% V. At 5 ML, the TMR increases above 1000% in all systems considered. However, while TMR declines sharply with increasing bias up to 0.2 eV in the tunnel junctions with pure Fe leads, it remains almost constant up to 0.5 eV if leads are alloyed with Co.
Camera or lens-based detector calibration is essential for spatial accuracy in applications like dimensional tomography, optical metrology, and computer vision. Many methods and software exist yet there is still a lack of approaches that achieve both high accuracy and robustness while being easy to use and capable of handling a wide range of distortions. Radial lens distortion is common in high-resolution X-ray detector optics used in parallel-beam tomography at synchrotrons. Achieving sub-pixel accuracy requires calibrating with an optical target image. Although methods for characterizing radial distortion are well established, acquired images often also include perspective distortion and optical center offset. Here, we present our approaches to individually characterize and correct both types of distortion using a single calibration image, implemented in the Discorpy software.
Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.