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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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A compositionally tunable series of AuCu films is active for electrocatalytic urea generation from carbon dioxide and nitrate.
Synthetic polymers have found widespread use, but their ineffective end-of-life treatment is causing a significant environmental and human health crisis. Here, we demonstrate the upcycling of polynorbornene derivatives (pNBEs) through their deconstruction into distinct oligomeric buildings blocks that can be repolymerized into chemically recyclable pNBEs-like multiblock polymers via dehydrogenative polymerization. The resulting materials exhibit diverse mechanical properties, while integrating high melting temperatures (T m as high as 133 °C). Notably, this method could also enable the selective deconstruction of permanently cross-linked polydicyclopentadiene (pDCPD) thermosets into telechelic-OH functionalized oligomers, overcoming the significant challenges posed by their robust network structure in recycling and degradation. The resulting pDCPD oligomers can subsequently be repolymerized with macrodiols to create multiblock thermosets with tunable mechanical properties, including Young's modulus and tensile elongation. After use, upcycled plastics could be effectively deconstructed back to the oligomers for recovery and repolymerization. Overall, this work establishes an approach that can be utilized to upcycle pNBEs into previously inaccessible multiblock thermosets and thermoplastics with full recyclability, and may be generalizable to a range of polymers to shift their end-of-life waste disposal toward sustainable recovery and reuse.
Plastic pollution is a pressing challenge, such that traditional recycling struggles to handle mixed waste. In the March issue of Chem Catalysis, Li et al. introduce a process that co-upcycles two societally important plastics, polyethylene terephthalate (PET) and polyoxymethylene (POM), offering a solution for more complex waste streams.
This study presents a dual heat and light-responsive monolithic liquid crystal elastomers (LCEs) film designed to enhance actuation control. The LCE films were capable of various motion types, including curvature reversal, oscillation, and sustainable flipping. By incorporating an azobenzene compound, the films exhibited additional functionalities such as enhanced curvature reversal, accelerated oscillation, and the ability to lock continuous flipping motion upon light stimulation. Furthermore, by adjusting the aspect ratio (AR) of a single film, different thermal motion states were realized, demonstrating the flexibility of this approach. Observations using an NIR camera confirmed temperature fluctuations corresponding to thermal exchange, and the influence of UV light on motion regulation was quantitatively analyzed. In conclusion, the ability to regulate motion through dual stimuli paves the way for improved dynamic motion control and thermal management in advanced robotic applications.
In addressing the critical challenge of calendar aging in silicon (Si)-based lithium-ion batteries, this study introduces a groundbreaking strategy utilizing glyme-type dual-salt electrolytes (lithium bis(trifluoromethanesulfonyl)imide [LiTFSI] and lithium difluoro(oxalato)borate [LiDFOB]). These electrolytes are demonstrated to significantly mitigate parasitic reactions and capacity loss in Si-NMC (lithium nickel manganese cobalt oxide) full cells, especially when compared with traditional carbonate-based electrolytes. Further, our exhaustive mechanistic analysis reveals that such electrolytes not only preserve the integrity of the Si anode but also improve the cathode/electrolyte interphases (CEI) through the formation of a conformal coating on the high-voltage cathode surface. This dual-salt approach, enhanced by the addition of a phosphate additive, effectively decelerates calendar aging, marking a substantial advance in the quest for durable and reliable Si-based energy storage technologies. The findings underscore the vital role of electrolyte composition in extending the calendar life of Si batteries, offering an alternative avenue toward maximizing the performance and longevity of next-generation Li-Si batteries.
Flowable carbon slurries are actively studied and under development for charge transport in various electrochemical systems including flow capacitors, capacitive deionization cells, semi-solid flow batteries, and lithium extraction. However, much less is known about in operando slurry flow dynamics and their corresponding effect on charge transport. We performed an experimental study of mixing and settling dynamics of slurry electrodes within an electrochemical flow cell during continuous operations. The electrochemical cell consisted of two horizontal co-flowing channels, separated by a cation-exchange membrane (CEM). Here we used high-speed optical imaging of planes parallel to gravity and simultaneous electrochemical measurements. At low flow rates, dense yet dynamic particle beds formed on the bottom electrode in each channel, which unexpectedly yielded the highest currents. This approach enables the operation of the flow cell at low system-average particle concentrations while leveraging gravity-driven particle settling to locally enhance carbon concentrations precisely at the current collector sites. Conversely, high flow rates were characterized by thin particle beds and well-mixed particle flows. In the latter case, the electrodes in closest proximity (located on either side of the CEM) achieved a current higher than the other electrode pairs. The observations have implications for slurry control and electrode designs in electrochemical systems.
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The sizes of the basins of attraction on the potential energy surface are helpful indicators in determining the experimental synthesizability of metastable phases. In principle, these basins can be controlled with changes in thermodynamic conditions such as composition, pressure, and surface energy. Herein, we use random structure sampling to computationally study how alloying smoothly perturbs basin of attraction sizes. The TaC 1-x N x pseudobinary is an ideal test system given the structural and polymorphic contrast of its parent compounds and their technological relevance as epitaxial substrates for Al 1-x Ga x N. While we find limited thermodynamic stability across all computationally observed phases, random structure sampling shows a significant composition region where the rocksalt basin dominates. As such, we predict the potential for the nonequilibrium synthesis of metastable rocksalt TaC 1-x N x alloys as substrates for Al 1-x Ga x N. At higher nitrogen concentrations, other low-energy metastable polymorphs emerge that continue to retain the hexagonal close packing suitable for III-N growth. Confidence in these trends was established through uncertainty quantification of the basin sizes and energy distributions; such analysis utilized the Beta and Dirichlet distributions. In conclusion, we also find (a) polymorph basin sizes can be rationalized in terms of energetic preferences for different coordination environments; and (b) basin sizes universally shrink with increasing nitrogen content, making the system more prone to amorphous growth.
Non-sustainable packaging materials (polymers) are a large portion of the increasing amount of plastic waste that become environmental pollutants. While many biodegradable or compostable polymers have been developed in recent years, most fail to compete with the non-sustainable polymers dominating the market, due to lack of certain desired properties, such as thermal (high melt temperature) and mechanical (high ductility) performance. For example, the popular compostable polymer polylactic acid (PLLA) has a high melt temperature but is very brittle. Another polymer, poly(d-valerolactone) (PVL) demonstrates great ductility but has a very low melt temperature. By blending PLLA and PVL, there is an opportunity to create a new biodegradable material with synergistic desired properties for packaging applications. In this study, we produce several (9) physical blends of PVL and PLLA with varying compositions of the two polymers while applying three different materials to make them more compatible (compatibilizers). We test the success of compatibilization by scanning electron microscopy (SEM), mechanic testing (strain at break) and differential scanning calorimetry (melting temperature). Overall, we highlight several promising materials with high compatibility and desired thermomechanical properties for sustainable packaging. Ideally, these materials could help mitigate future plastic pollution in Colorado and beyond.
Noncovalent interactions (NCIs) are crucial for the formation and stability of host-guest complexes, which have wide-ranging implications across various fields, including biology, chemistry, materials science, pharmaceuticals, and environmental science. However, since NCIs are relatively weak and sensitive to bulk perturbation, direct and accurate measurement of their absolute strength has always been a significant challenge. This concept article aims to demonstrate the gas-phase electrospray ionization (ESI)-negative ion photoelectron spectroscopy (NIPES) as a direct and precise technique to measure the absolute interaction strength, probe nature of NCIs, and reveal the electronic structural information for host-guest complexes. Here, our recent studies in investigating various host-guest complexes that involve various types of NCIs such as anion–π, (di)hydrogen bonding, charge-separated ionic interactions, are overviewed. Finally, a summary and outlook are provided for this field.
The photocycloaddition (PCA) of chalcones represents an important reaction pathway for accessing substituted cyclobutanes, which is a molecular framework with utility in synthetic chemistry, materials science, and medicine. In the past, our group has demonstrated the utility of the large cavity of γ-CD as a container for encapsulating two photo reactants for directing the PCA of several classes of aryl alkenes with high stereo- and regioselectivity: the cavitand-mediated photodimerization (CMP) approach. The CMP of chalcones reported in this work further demonstrates the effectiveness of this approach as high yields of dimers were observed in the photoreactions, while they were non-reactive in the solid state and yielded only the isomerization product in homogeneous media. The γ-CD CMP of chalcones yielded predominantly dimerized products in very good to high yields (>70%), composed of a mixture of three dimers in different proportions with syn HH as the major product. Computational analysis of the ground state complex structures revealed a strong correlation between the stability of the complex and predominance of the stereoisomer in the mixture. Further insights were deduced from temperature-dependence studies, which showed a shift in dimer selectivity tending towards a single stereoisomer.
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, trained via active learning, to achieve ab initio accuracy for water confined between hydroxylated silica surfaces over a range of silanol coverages and slit widths. We find that partially hydroxylated surfaces (50 and 75% OH) support stronger water−surface hydrogen bonding and more extended interfacial density profiles than fully hydroxylated (100% OH) surfaces, indicating that increasing OH coverage does not necessarily strengthen interfacial hydrogenbond networks. Translational diffusion decreases approximately linearly with slit width and OH coverage, whereas rotational dynamics respond nonlinearly. In particular, at the smallest slit width of 5 Å, 75% OH coverage produces an enhanced local tetrahedral ordered interfacial network that strongly suppresses reorientation, while 100% coverage yields a crowded, disordered interfacial layer that also hinders rotation. In contrast, the 50% OH coverage is sufficiently sparse that it does not markedly alter water structure or dynamics under confinement. These results show that coupled control of pore size and surface chemistry enables nonlinear tuning of interfacial water structure and transport, providing a design strategy for optimizing porous silica for either enhanced interfacial stability and controlled reactivity or rapid and selective transport.
Magnetite nanocrystals show promise for electrically small gigahertz frequency applications, which could lead to miniaturizing transformer cores and new sensing technologies. This work presents a rigorous radiofrequency characterization of these nanocrystals using vector network analyzer (VNA) ferromagnetic resonance (FMR) measurements. For the first time, two different average diameters of Fe 3 O 4 nanocrystals are investigated (7.3 and 20.2 nm). When the VNA–FMR results were compared to micromagnetic simulations, the magnetic anisotropy (K 1 ) deviated from the ideal mean orientation value of K 1 /2 to K 1 /80 for the 7.3 nm nanocrystal. In contrast, the obtained magnetic anisotropy in 20.2 nm nanocrystals slightly deviated to K 1 /11 due to less structural deformations. These findings resulted in a newly proposed methodology for an approximate simulation based on VNA–FMR measurements. In addition, this work estimates the approximate amount of nanocrystals needed to measure a useful VNA–FMR spectrum.
Solid-solid phase change materials (SS-PCMs) hold promise for energy storage/dissipation in batteries and energetic materials. Yet, phase change kinetics for SS-PCMs undergoing metastable to semi-stable/stable phase transformations remain relatively ill-studied because trapping metastable phases remain challenging. Recently, we demonstrated the kinetic entrapment and stabilization of a highly disordered and amorphous Al-oxide phase m-AlO x @C (x~2.5-3.0) via laser ablation synthesis in solution (LASiS). We report here, to our knowledge, the first chemical kinetics analysis for S-S phase transition of the m-AlO 3 @C nanocomposites (< 5–8 nm sizes) into semi-stable equilibrium alumina phases (θ/γ-Al 2 O 3 ) via disproportionation reaction, while releasing excess trapped gases. Our results indicate the atomic density of the AlO 3 structures to be ~5–10 times less than that of the final Al 2 O 3 phases, which led to the hypothesis of a volume shrinkage process during their phase transition. Temperature-dependent X-ray diffraction studies reveal the high-temperature phase transition for m-AlO 3 → θ/γ-Al 2 O 3 to follow contracting volume kinetics model, thereby validating our earlier hypothesis. Using the geometric volume contraction model, reaction kinetics analyses from Arrhenius plots reveal the activation energy barrier for the phase transition to be ~270±11 kJ/mol. This makes the activation energy barrier nearly identical to the oxidation of micron-sized Al particles.
Currently, the iron and steel industry is responsible for 7% of global CO 2 emissions. In this review, we summarize the operational principles of current emissions-intensive steelmaking technologies and review emerging low- and zero-carbon technologies that could substantially reduce emissions. Current technologies that are discussed include blast furnaces, electric arc furnaces, and smelting. Promising low-carbon routes include use of alternative reductants for ore processing (hydrogen direct reduction, hydrogen plasma-smelting, hydrogen smelting, and ammonia-based reduction), electrolytic iron production (with aqueous and molten oxide electrolytes) and biocarbon-based electric arc furnace operation. Advantages of each approach are presented, and remaining research hurdles are identified.
Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.