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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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In this report, we explore the internal structural features of polyMOFs consisting of equal mass ratios of metal-coordinating poly(benzenedicarboxylic acid) blocks and non-coordinating poly(ethylene glycol) (PEG) blocks.
In the past two decades, ionic liquids (ILs) have blossomed as versatile task-specific materials with a unique combination of properties, which can be beneficial for a plethora of different applications. The additional need of incorporating ILs into solid devices led to the development of a new class of ionic soft-solid materials, named here iongels. Nowadays, iongels cover a wide range of materials mostly composed of an IL component immobilized within different matrices such as polymers, inorganic networks, biopolymers or inorganic nanoparticles. Here this review aims at presenting an integrated perspective on the recent progress and advances in this emerging type of material. We provide an analysis of the main families of iongels and highlight the emerging types of these ionic soft materials offering additional properties, such as thermoresponsiveness, self-healing, mixed ionic/electronic properties, and (photo)luminescence, among others. Next, recent trends in additive manufacturing (3D printing) of iongels are presented. Finally, their new applications in the areas of energy, gas separation and (bio)electronics are detailed and discussed in terms of performance, underpinning it to the structural features and processing of iongel materials.
This perspective deviates from exclusively focusing on structural features of actinide-containing metal-organic frameworks and pivots towards their prospect as avant-garde materials with an emphasis on their physicochemical properties.
Luminescent metal-organic frameworks (LMOFs) with diverse structural features and promising fluorescence-based applications have attracted wide attention in the past two decades. In this work, a LMOF with the formula [Ca 4 (tcbpe-F) 2 (H 2 O) 3 ] (1, LMOF-411) has been constructed from calcium (Ca) and 1,1,2,2-tetrakis(4-(4-carboxyphenyl)phenyl)ethene (H 4 tcbpe-F). Compound 1 features a three-dimensional framework with a 10-nodal net topology. Due to the relatively high hydration energy of Ca 2+ , compound 1 readily transforms into a new phase formulated as [Ca(H 2 tcbpe-F)(H 2 O) 2 ] (1') upon exposure to water. Combining experimental characterization and theoretical calculations, we elucidated the mechanism of H 2 O-induced phase transition from 1 to 1'. Notably, the water induced phase transformation can be detected visibly from the change in luminescence, which originates from the fluorescent linker. Compound 1 emits green light ( λ em = 490 nm) under UV excitation, while compound 1' emits bright yellow light ( λ em = 550 nm) under blue excitation (450 nm). Compound 1' represents the first Ca based LMOF yellow phosphor and its luminescence quantum yield reaches 68%. It can be coated directly onto a commercial blue light-emitting-diode (LED) chip to fabricate a white LED (WLED).
Lithium-ion batteries (LIBs) feature structural and chemical complexities across a broad range of length scales. It is the hierarchy of the battery structure that determines its functionality. An in-depth understanding of the battery function, degradation, and failure mechanisms requires a thorough and systematic investigation from structural, chemical, mechanical, and dynamic perspectives. Here we present a macro-to-nano zoom through the hierarchy of a commercial 18650 type LIB using a suite of state-of-the-art X-ray microscopy techniques. Damage, deformation, and heterogeneity at different length scales are visualized and are associated with different degradation phenomena and mechanisms. Our results highlight the importance of the mechanical properties of the cathode material, which could impact both the immediate and the long-term cell behaviors significantly. While this study focuses on a commercial lithium cell with a standard configuration, our findings can be extrapolated and are applicable to the development of next-generation energy storage technology, e.g. solid-state batteries.
The G-quadruplex is a fascinating nucleic acid motif with implications in biology, medicine, and nanotechnologies. G-quadruplexes can form in the telomeres at the edges of chromosomes and in other guanine-rich regions of the genome. They can also be engineered for exploitation as biological materials for nanodevices. Their higher stiffness and higher charge transfer rates make them better candidates in nanodevices than duplex DNA. For the development of molecular nanowires, it is important to optimize electron transport along the wire axis. One powerful basis to do so is by manipulating the structure, based on known effects that structural changes have on electron transport. Here, for this work, we investigate such effects, by a combination of classical simulations of the structure and dynamics and quantum calculations of electronic couplings. We find that this structure–function relationship is complex. A single helix shape parameter alone does not embody such complexity, but rather a combination of distances and angles between stacked bases influences charge transfer efficiency. By analyzing linear combinations of shape descriptors for different topologies, we identify the structural features that most affect charge transfer efficiency. We discuss the transferability of the proposed model and the limiting effects of inherent flexibility.
Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.
While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. Here, in this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.
Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.
The trend towards electrifying transportation systems has stimulated research endeavours aimed at developing electric machines that are not only high speed and efficient but also low‐cost and compact. To support this transition, the US Department of Energy (DOE) has established ambitious targets of $7/kW cost and 12 kW/L power density for the electric drivetrains. This paper attempts to meet these targets by proposing high‐speed permanent magnet assisted synchronous reluctance machine (PMASynRM) topologies enabled by hybrid magnet strategies, combining rare‐earth (RE) magnets with low‐cost, RE‐free alternatives. Modified 2‐layer and 3‐layer U‐shaped rotor configurations, featuring structural reinforcements to withstand high mechanical stress at elevated speeds, are developed. A multi‐objective design optimisation framework is employed to optimise the rotor design, targeting reductions in RE magnet volume, magnet demagnetisation risk and torque ripple while achieving the desired electromagnetic performance. A comprehensive analysis of the optimised rotor designs, compared against a 16,000 rpm, 800 V baseline IPM motor, shows over 70% RE reduction, demagnetisation risk below 3.5% and torque ripple under 10%. Detailed analyses of performance trends across various magnet combinations and rotor configurations highlight the viability of hybrid magnet PMASynRM designs as cost‐effective, robust and energy‐efficient solutions for next‐generation EVs.
Ab initio quantum mechanical charge field molecular dynamics (QMCF-MD) were successfully applied to Cu(II) embedded in water to elucidate structure and to understand dynamics of ligand exchange mechanism. From the simulation studies, it was found that using an extended large quantum mechanical region including two shells of hydration is required for a better description of the dynamics of exchanging water molecules. The structural features characterized by radial distribution function, angular distribution function and other analytical parameters were consistent with experimental data. The major outcome of this study was the dynamics of exchange mechanism and reactions in the first hydration shell that could not be studied so far. The dynamical data such as mean residence time of the first shell water molecules and other relevant data from the simulations are close to the results determined experimentally. Another major characteristic of hydrated Cu(II) is the Jahn-Teller distortion which was also successfully reproduced, leading to the final conclusion that the dominating aqua complex is a 6-coordinated species. The ab initio QMCF-MD formalism proved again its capabilities of unraveling even ambiguous properties of hydrated species that are far difficult to explore by any conventional quantum mechanics/molecular mechanics (QM/MM) approach or experiment.
The first-principles calculations are employed to provide a fundamental understanding of the structural features and relative stability, mechanical and electronic properties, and possible reactive route for chromium triboride. The predicted new phase of CrB{sub 3} belongs to the rhombohedral phase with R-3m symmetry and it transforms into a hexagonal phase with P-6m2 symmetry at 64 GPa. The mechanical and thermodynamic stabilities of CrB{sub 3} are verified by the calculated elastic constants and formation enthalpies. Also, the full phonon dispersion calculations confirm the dynamic stability of predicted CrB{sub 3}. Considering the role of metallic contributions, the calculated hardness values from our semiempirical method for rhombohedral and hexagonal phases are 23.8 GPa and 22.1 GPa, respectively. In addition, the large shear moduli, Young's moduli, low Poisson's ratios, and small B/G ratios indicate that they are potential hard materials. Relative enthalpy calculations with respect to possible constituents are also investigated to assess the prospects for phase formation and an attempt at high-pressure synthesis is suggested to obtain chromium triboride.
Visible quantum cutting (QC) through downconversion was observed upon 4f{sup 8}-4f{sup 7}5d{sup 1} excitation of Tb{sup 3+} in both BaGdB{sub 9}O{sub 16} and Ca{sub 8}MgGd(PO{sub 4}){sub 7} compounds. The QC involves a cross relaxation process between two neighboring Tb{sup 3+}. Modified and new QC models are proposed based on the QC mechanisms in Gd{sup 3+}-Tb{sup 3+} system. Two calculation equations for the cross relaxation efficiency are suggested according to the energy transfer theory and spectral results. By studying the spectral characteristics of Tb{sup 3+} in hosts from fluoride to oxide, it indicates that the visible QC in Gd{sup 3+}-Tb{sup 3+} system may occur mainly depending on the structural features of phosphors, rather than the phonon energies of matrixes. All of the above conclusions have meaning of guidance for investigating other phosphors with QC phenomenon.
We study a calcium aluminosilicate glass of composition (SiO{sub 2}){sub 0.60}(Al{sub 2}O{sub 3}){sub 0.10}(CaO){sub 0.30} by means of molecular dynamics. To this end, we conduct parallel simulations, following a consistent methodology, but using three different potentials. Structural and elastic properties are analyzed and compared to available experimental data. This allows assessing the respective abilities of the potentials to produce a realistic glass. We report that, although all these potentials offer a reasonable glass structure, featuring tricluster oxygen atoms, their respective vibrational and elastic predictions differ. This allows us to draw some general conclusions about the crucial role, or otherwise, of the interaction potential in silicate systems.
First-principles calculations are employed to provide a fundamental understanding of the structural features, elastic anisotropy, shear-induced atomistic deformation behaviors, and its electronic origin of the recently proposed superhard t-SiCN. According to the dependences of the elastic modulus on different crystal directions, the t-SiCN exhibits a well-pronounced elastic anisotropy which may impose certain limitations and restrictions on its applications. The further mechanical calculations demonstrated that t-SiCN shows lower elastic moduli and ideal shear strength than those of typical hard substances of TiN and TiC, suggesting that it cannot be intrinsically superhard as claimed in the recent works. We find that the failure modes of t-SiCN at the atomic level during shear deformation can be attributed to the breaking of C-C bonds through the bonding evolution and electronic localization analyses.
Molecular dynamics simulations were performed to study the structural features of graphene over a wide range of temperatures from 50 to 4000 K using the PPBE-G potential [D. Wei, Y. Song, and F. Wang, J. Chem. Phys. 134, 184704 (2011)]. This potential was developed by force matching the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional and has been validated previously to provide accurate potential energy surface for graphene at temperatures as high as 3000 K. Simulations with the PPBE‑G potential are the best available approximation to a direct Car-Parrinello Molecular Dynamics study of graphene. One advantage of the PBE-G potential is to allow large simulation boxes to be modeled efficiently so that properties showing strong finite size effects can be studied. Our simulation box contains more than 600 000 C atoms and is one of the largest graphene boxes ever modeled. With the PPBE-G potential, the thermal-expansion coefficient is negative up to 4000 K. With a large box and an accurate potential, the critical exponent for the scaling properties associated with the normal-normal and height-height correlation functions was confirmed to be 0.85. This exponent remains constant up to 4000 K suggesting graphene to be in the deeply cooled regime even close to the experimental melting temperature. The reduced peak heights in the radial distribution function of graphene show an inverse power law dependence to distance, which indicates that a macroscopic graphene sheet will lose long-range crystalline order as predicted by the Mermin-Wagner instability. Although graphene loses long-range translational order, it retains long range orientational order as indicated by its orientational correlation function; graphene is thus partially ordered but not periodic.
We compared the structural properties and electrical characteristics of high-κ Lu{sub 2}O{sub 3} and Lu{sub 2}TiO{sub 5} gate dielectrics for amorphous indium-gallium-zinc oxide (α-InGaZnO) thin-film transistor (TFT) applications. The Lu{sub 2}O{sub 3} film has a strong Lu{sub 2}O{sub 3} (400) peak in the X-ray diffraction pattern, while the Lu{sub 2}TiO{sub 5} sample shows a relatively weak Lu{sub 2}TiO{sub 5} (102) peak. Atomic force microscopy reveals that the Lu{sub 2}O{sub 3} dielectric exhibits a rougher surface (about three times) than Lu{sub 2}TiO{sub 5} one. In X-ray photoelectron spectroscopy analysis, we found that the intensity of the O 1s peak corresponding to Lu(OH){sub x} for Lu{sub 2}O{sub 3} film was higher than that of Lu{sub 2}TiO{sub 5} film. Furthermore, compared with the Lu{sub 2}O{sub 3} dielectric, the α-InGaZnO TFT using the Lu{sub 2}TiO{sub 5} gate dielectric exhibited a lower threshold voltage (from 0.43 to 0.25 V), a higher I{sub on}/I{sub off} current ratio (from 3.5 × 10{sup 6} to 1.3 × 10{sup 8}), a smaller subthreshold swing (from 276 to 130 mV/decade), and a larger field-effect mobility (from 14.5 to 24.4 cm{sup 2}/V s). These results are probably due to the incorporation of TiO{sub x} into the Lu{sub 2}O{sub 3} film to form a Lu{sub 2}TiO{sub 5} structure featuring a smooth surface, a low moisture absorption, a high dielectric constant, and a low interface state density at the oxide/channel interface. Furthermore, the stability of Lu{sub 2}O{sub 3} and Lu{sub 2}TiO{sub 5} α-InGaZnO TFTs was investigated under positive gate-bias stress (PGBS) and negative gate-bias stress (NGBS). The threshold voltage of the TFT performed under NGBS is more degradation than that under PGBS. This behavior may be attributed to the electron charge trapping at the dielectric–channel interface under PGBS, whereas the oxygen vacancies occurred in the InGaZnO under NGBS.
Inorganic analogues of hydrocarbons or polycyclic aromatic hydrocarbons (PAHs) are of current interest in chemistry. Based upon global structural searches and B3LYP and CCSD(T) calculations, we present herein the perfectly planar dicyclic boron sulfide clusters: D{sub 2h} B{sub 6}S{sub 6} (1, {sup 1}A{sub g}), D{sub 2h} B{sub 6}S{sub 6}{sup −} (2, {sup 2}B{sub 3u}), and D{sub 2h} B{sub 6}S{sub 6}{sup 2−} (3, {sup 1}A{sub g}). These are the global minima of the systems, being at least 0.73, 0.81, and 0.53 eV lower in energy, respectively, than their alternative isomers at the CCSD(T) level. The D{sub 2h} structures feature twin B{sub 3}S{sub 2} five-membered rings, which are fused together via a B{sub 2} unit and terminated by two BS groups. Bonding analyses show that the closed-shell B{sub 6}S{sub 6}{sup 2−} (3) cluster possesses 10 delocalized π electrons, closely analogous to the bonding pattern of the aromatic naphthalene C{sub 10}H{sub 8}. The B{sub 6}S{sub 6}{sup −} (2) and B{sub 6}S{sub 6} (1) species are readily obtained upon removal of one or two π electrons from B{sub 6}S{sub 6}{sup 2−} (3). The results build a new analogous relationship between boron sulfide clusters and their PAH counterparts. The B{sub 6}S{sub 6}{sup −} (2) monoanion and B{sub 6}S{sub 6}{sup 2−} (3) dianion can be effectively stabilized in neutral LiB{sub 6}S{sub 6} and Li{sub 2}B{sub 6}S{sub 6} salts, respectively.