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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Detection of interstellar PN - The first phosphorus-bearing species observed in molecular clouds

Phosphorus nitride (PN) has been detected in the interstellar medium. The J = 2-1, 3-2, 5-4, and 6-5 rotational lines of this species have been observed toward Orion-KL, and the J = 2-1 transition in Sgr B2 and W51. The PN line profiles in Orion indicate that the molecule's emission arises from the 'plateau' or 'doughnut' region associated with the outflow from IRc2. The species is thus primarily present in hot, dense gas. Column densities derived for PN toward Orion-KL are (3-4) x 10 to the 13th/sq cm, but may be as high as 10 to the 14th/sq cm, if the species is located in a 10-arcsec region. These column densities imply a fractional abundance for PN in the Orion 'plateau' of (1-4) x 10 to the -10th. Such a large abundance for PN is not predicted by quiescent cloud ion-molecule chemistry and suggests that high-temperature processes are responsible for the synthesis of PN in the KL outflow.

Ziurys, L. M.↗
NASA NTRSDate not supplied

Organic-rich fluids in Pluto’s interior—A source of cryovolcanic activity

The highly variegated surface of Pluto revealed by the New Horizons spacecraft is composed of both volatile ices (N2, CH4, CO) and H2O ice, all of which are intrinsically colorless. The colors on Pluto reveal a non-ice component generally acknowledged to be a refractory complex organic material (tholins) produced by photolysis in the planet’s atmosphere [1], and by photolysis and radiolysis of the surface ices [2-4]. These processes have produced a range of colors from pale yellow to red to brown, and result in a variation of a factor of 10in albedo across the surface. Tholins are disordered polymer-like materials made of repeating chains of linked subunits and complex combinations of functional groups containing carbon. Nitrogen, oxygen, and other elements can be substituted in both the aliphatic and aromatic subunits. The detection of the spectral absorption of an ammoniated compound associated with surface exposures of red-colored H2O ice (herein, RAW) in the LEISA spectral mapping data from New Horizons suggests that a third source of a colored material is ejected from Pluto’s interior as a water-rich fluid(cryomagma) that rapidly freezes at the low temperature (~40K) of the surface [5,6]. The ammoniated compound is thought to be an ammonia hydrate or an ammoniated salt; the spectral data cover a limited wavelength range, resulting in ambiguity in the identification of the form of the ammonia. The distribution of RAW in the vicinity of tectonically stressed and deformed geological structures, particularly graben (fossae), is seen as the result of both fluid effusion and fountaining ejection of cryoclastic materials in Pluto’s Virgil Fossae region [5,6]. RAW is seen elsewhere on Pluto, both in association with graben complexes and on broad expanses of old surface, but primarily in zones of tectonic stress. The nature of the putative subsurface fluid is unknown, but it must have existed at relatively shallow depths such that the graben faulting could reach it. Geochemical modeling of fluids in the interiors of small planetary bodies in the outer Solar System[7] shows that ammonia-bearing H2O in contact with rock and metal components produce a rich chemical soup, including gases that can, in principle, help propel the fluid from shallow reservoirs through crustal fractures to reach the surface. An organic component of the fluid cryomagma is predicted to originate from thermal processing of the components [ref] and may be an example of one-pot synthesis of such complexes as amino acid precursors [ref].

Dale P Cruikshank↗
NASA NTRS2019

Integrating Remote Sensing Observations with NASA's GEOS-5 Modeling Framework in Support of Retrospective Analyses and Seasonal Prediction of Biosphere-Atmosphere CO2 Flux

The absorption of nearly half of all anthropogenic carbon dioxide (CO2) emissions by terrestrial and marine ecosystems has played a critical role in mitigating climate change. However, a persistent lack of understanding of these uptake processes impedes attribution, and thereby introduces large uncertainties into projections of the trajectory of carbon and climate in coming decades. A better understanding and quantification of the seasonal and interannual variability of carbon flux between the atmosphere and ocean and terrestrial biospheres is critically needed to improve predictions from Earth system models. We propose to bring together a diverse suite of remote sensing observations, mechanistic models, and NASA's Goddard Earth Observing System, version 5 (GEOS-5) model to address two objective 1) quantify the processes controlling the temporal variability of biosphere-atmosphere CO2 flux on seasonal and interannual timescales from 1989 to 2018 and 2) evaluating the predictability of the components of carbon flux on seasonal timescales. Because GEOS-5 has been developed to incorporate a variety of Earth system observations and includes a complete and physically consistent depiction of the atmosphere, land, and ocean carbon cycles, it represents a unique and ideal synthesis framework for this effort. By expanding the realism of processes that could be included in GEOS-5 seasonal forecasts, this effort supports NASA's goals toward an integrated Earth system modeling and prediction framework.We propose to integrate a number of observations into the GEOS-5 modeling system to refine retrospective estimates of both terrestrial and marine carbon flux and to test hypotheses regarding the effects of climate drivers on terrestrial carbon flux. This will include assimilating several satellite ocean color products in the NASA Ocean Biogeochemistry Model (NOBM) to improve the representation of ocean biology. We will also utilize remote sensing datasets to increase the temporal resolution of global land-use change estimates and fire emissions to better constrain the temporal variability of the terrestrial carbon cycle. This information will be incorporated into two NASA terrestrial biosphere models Catchment-CN, a dynamic vegetation model that is integrated within GEOS-5 and the Ecosystem Demography (ED) model, a core modeling resource supporting the Global Ecosystem Dynamics Investigation (GEDI) mission to map biomass. Fluxes will be evaluated through 1) comparison to top-down flux estimates inferred from surface CO2 observations and 2) comparison against available aircraft and satellite observations.By combining predictions of net ecosystem exchange and air-sea CO2 flux from land and ocean models with statistical predictions of land use change, fire, and fossil fuel emissions, we propose to create the first dynamical CO2 forecast on seasonal timescales. Because of the experimental nature of this endeavor, an important project objective will be to evaluate the limits to the predictability of different carbon cycle processes over lead times of 1-12 months using different satellite and surface data streams.

Ott, Lesley↗
DOE OSTI2023

Developing a microwave-driven reactor for ammonia synthesis: insights into the unique challenges of microwave catalysis

Rapid development of new ammonia (NH 3 ) synthesis techniques that enable modular, intermittent production is essential to actualizing NH 3 's potential as a clean energy carrier, since contemporary methods are configured to centralized, continuous production methods with high emissions and are incompatible with renewable sources. Here, in this mission, microwave-driven catalysis is promising for its ability to enhance reaction kinetics and apply targeted heating for efficient energy use. However, owing to an incomplete understanding of the interaction between microwave fields and catalyst beds, the development of such microwave-catalysis systems remains underexplored and challenging. This paper investigates the 10× scale-up of a microwave-based NH 3 synthesis reactor using numerical and experimental approaches, achieving the largest reported microwave-driven NH 3 reactor to date. Results elucidate phenomena unique to microwave processes that are only predictable through numerical modeling, including how a catalyst's dielectric properties influence microwave field distribution by affecting penetration depth and how energy utilization can be poor even with sufficient attenuation. These dynamics change with scale, constrain reactor geometry, and potentially hamper performance. Nonetheless, we demonstrate a production rate of 56.6 g NH$_3$ per day, the highest reported NH 3 synthesis rate for laboratory-scale alternative techniques; correspondingly, the benchmark energy efficiency achieved in this paper (45.6 g NH$_3$ kW h -1 ) is the highest reported for such reactors of sufficient scale. Even with this exemplary energy efficiency, energy losses were found in excess of 50%, an issue resolvable through scale and reactor design. The efficiencies imparted by microwaves were key in these achievements, warranting further investigation toward development of microwave-driven NH 3 systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Surface Passivation by Excess Sulfur for Controlled Synthesis of Large, Thin SnS Flakes

Tin sulfide (SnS) is part of the group IV chalcogenides (SnX, GeX; X: S, Se), a family of anisotropic layered materials considered for thin film photovoltaics, optoelectronics, and valleytronics, and predicted narrow bandgap multiferroic materials. Large ultrathin SnS flakes, suitable for a variety of applications, are challenging to synthesize because of the enhanced surface reactivity due to the open layer structure of SnS which gives rise to a competition between lateral and vertical growth. Here we investigate the effects of added sulfur on passivating the surface and modifying the balance between lateral expansion and thickening of SnS flakes in chemical vapor transport from a SnS precursor. In addition, we investigate the growth of SnS flakes and compare the results of synthesis from a pure SnS precursor with growth, in which a slight excess of sulfur is supplied in different ways. Our results demonstrate that small amounts of excess sulfur can profoundly affect the size and thickness distributions of SnS flakes. The largest and thinnest flakes are obtained if (i) traces of sulfur are added and (ii) the sulfur source consists either of small Sx fragments released at high temperatures from the reactor walls, or atomic S supplied by sublimation from SnS 2 . The likely mechanism for the observed growth modifications is a transient surface passivation of SnS flakes during growth, which reduces the reactivity of the top facet of the flakes, limits vertical growth, and thus gives rise to ensembles with increased lateral size and reduced thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Driving forces for particle-based crystallization: From experiments to theory and simulations

The multi-step crystallization processes involving the formation of stable building blocks that subsequently assemble into a crystal are ubiquitous in mineral formation and biomineralization and are particularly attractive in materials synthesis. Utilizing these pathways offers the approach to overcoming the restrictions on the expression of various crystal faces imposed by the interfacial energy during monomer-by-monomer growth to unlock the breadth of architectures with unique properties. Further, controlling particle-based crystallization proved challenging despite its promise due to the complex interdependence of interfacial forces and their non-linear dependence on synthesis parameters. Here, the status of the development of state-of-the-art approaches to measuring interparticle forces and predictive theoretical models of particle-based crystallization are reviewed.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Eruption Mechanisms for Loki, Io: Inferences Based on Galileo and Groundbased Data

A wealth of data about Loki, the most powerful volcano on Io, has been collected and here, we try to begin synthesis of these data. These data suggest periodic lava lake overturn. Further, these eruptions may be truly periodic, and therefore predictable. Additional information is contained in the original extended abstract.

Rathbun, J. A.↗
DOE OSTI2023

Architector for high-throughput cross-periodic table 3D complex building

Abstract Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector , a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector , which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗
DOE OSTI2021

Synthesis of Highly Active Pd@Cu–Pt/C Methanol Oxidation Electrocatalysts via Continuous, Co-Electroless Deposition

Controlled deposition of metals is essential for the creation of bimetallic catalysts having predictable composition and character. Continuous co-electroless deposition (co-ED) permits the creation of bimetallic catalysts with predictive control over composition. This method was applied to create a suite of Cu–Pt mixed-metal shell catalysts for use in methanol electrooxidation in direct methanol fuel cell applications (DMFCs). Enhanced performance of Cu–Pt compositions over Pt alone was predicted by existing computational studies in the literature. Experimental evidence from this study supports the bifunctional catalyst explanation for enhanced activity and confirms the optimum Cu:Pt ratio as Cu 3 Pt for this methanol electrooxidation. This ability to control the composition of a bimetallic shell can be extended to other systems where the ratio of two metals is critical for catalytic performance.

preparation↗
DOE OSTI2023

Soft matter roadmap

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

State changes: insights from the U.S. Long Term Ecological Research Network

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER)network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo(tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds(the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across adverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2026

Metalloborophenes: Structural Diversity and Emerging Properties of Metal–Boron Two‐Dimensional Frameworks

Metalloborophenes, an emerging subclass of 2D materials, have attracted growing attention owing to their exceptional structural diversity and highly tunable electronic and magnetic properties. Constructed from vacancy‐rich borophene frameworks stabilized by electron donation from incorporated metal atoms, metalloborophenes merge the chemical versatility of boron with the functional richness of metal dopants. The first experimental realization of Cu–borophene nanoribbons in 2024 marked a pivotal advance, confirming long‐standing theoretical predictions and revitalizing interest in this new frontier of boron‐based 2D chemistry. Despite this progress, most studies to date remain conceptual and theoretical, constrained by challenges in scalable synthesis, dopant precision, and substrate control. Computational investigations have revealed a broad landscape of stable metalloborophene structures, exhibiting metallic, semiconducting, and magnetic behavior across diverse dopant families, including alkali, alkaline‐earth, transition, and lanthanide elements. These tunable characteristics open promising avenues for applications in spintronics, catalysis, and hydrogen storage. This review provides a comprehensive overview of metalloborophenes, emphasizing the interplay between structure, stability, and functionality, and outlining future directions toward bridging predictive modeling with experimental realization of this rapidly evolving class of 2D materials.

2D materials↗
NASA NTRS1999

Solar Cycle Predictions

We currently lack a comprehensive model for the solar dynamo which might be used for predicting solar cycle activity levels. We can, nonetheless, predict these activity levels with some skill using a variety of techniques based on correlations between activity indicators at different times. A number of the techniques currently in use for predicting solar activity on a solar cycle time-scale have been tested with historical data. Some techniques, e.g., regression and curve fitting, work well as solar activity approaches maximum and provide a month by month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but only provide an estimate of the amplitude of the next cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. The combination of two uncorrelated geomagnetic precursor techniques provides a more accurate prediction for the amplitude of a solar activity cycle of a time well before activity minimum. A mathematical function dependent on the time of cycle initiation and the cycle amplitude can then be used to describe the level of solar activity month by month for the next cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between previous activity levels and this function. The success of the geomagnetic precursors in predicting the amplitude of the next solar cycle supports the idea of extended solar cycle in which the activity cycles overlap. While the current cycle is producing sunspots and active regions in the Sun's equatorial regions the next cycle is already actively influencing the geomagnetic field, The precise nature and source of this influence remains unclear.

Hathaway, D. H.↗
NASA NTRS2000

Status of Cycle 23 Forecasts

A number of techniques for predicting solar activity on a solar cycle time scale are identified, described, and tested with historical data. Some techniques, e.g,, regression and curve-fitting, work well as solar activity approaches maximum and provide a month- by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but provide an estimate only of the amplitude of the cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. A combination of two uncorrelated geomagnetic precursor techniques provides the most accurate prediction for the amplitude of a solar activity cycle at a time well before activity minimum. This precursor method gave a smoothed sunspot number maximum of 154+21 for cycle 23. A mathematical function dependent upon the time of cycle initiation and the cycle amplitude then describes the level of solar activity for the complete cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between recent activity levels and this function. This Combined Solar Cycle Activity Forecast now gives a smoothed sunspot maximum of 140+20 for cycle 23. The success of the geomagnetic precursors in predicting future solar activity suggests that solar magnetic phenomena at latitudes above the sunspot activity belts are linked to solar activity, which occurs many years later in the lower latitudes.

Hathaway, D. H.↗
DOE OSTI2021

Tunable Atomic Layer Deposition into Ultra-High-Aspect-Ratio (>60000:1) Aerogel Monoliths Enabled by Transport Modeling

Atomic layer deposition (ALD) modification of ultra-high-aspect-ratio structures (>10,000:1) is a powerful platform with applications in catalysis, filtration, and energy conversion. However, the deposition of conformal and tunable ALD coatings at these aspect ratios remains challenging, resulting in empirical tradeoffs between precursor utilization and reaction time. Here, we demonstrate tunable control of the ALD infiltration depth into an aerogel monolith (AM) and develop a reaction-diffusion model to accurately describe the coating process. Specifically, we investigate the ALD exposure time and precursor dose needed to conformally coat a silica AM with pore sizes of ~20 nm, a monolith thickness of ~2.5 mm, and aspect ratios exceeding 60,000:1. We demonstrate complete infiltration into the AM, which is quantified by elemental mapping. A reaction-diffusion model is developed, which accounts for multiple doses and precursor depletion in the ALD chamber during an exposure step. The experimentally validated model enables the prediction and tuning of infiltration depth into a tortuous, high-aspect-ratio structure such as an AM, allowing for the synthesis of rationally designed material architectures. Additionally, the model allows for co-optimization of the total deposition time and percentage of unreacted precursor, which are important for the manufacturability and sustainability of ALD processing. Lastly, we demonstrate that ultra-thin ALD Al 2 O 3 coatings can be used to stabilize silica AMs against structural degradation under high-temperature annealing conditions (700-800 °C), by limiting changes in surface area and monolith volume. As a result, this improved high-temperature stability has implications for numerous aerogel applications, including catalysis and thermal insulation.

14 SOLAR ENERGY↗
DOE OSTI2020

Temperature and Solvent Effects on H 2 Splitting and Hydricity: Ramifications on CO 2 Hydrogenation by a Rhenium Pincer Catalyst

The catalytic hydrogenation of carbon dioxide holds immense promise for applications in sustainable fuel synthesis and hydrogen storage. Mechanistic studies that connect thermodynamic parameters with the kinetics of catalysis can provide new understanding and guide predictive design of improved catalysts. Reported here are thermochemical and kinetic analyses of a new pincer-ligated rhenium complex ( tBu POCOP)Re(CO) 2 ( tBu POCOP = 2,6-bis(di-tert-butylphosphinito)phenyl) that catalyzes CO 2 hydrogenation to formate with faster rates at lower temperature. Because the catalyst follows the prototypical “outer sphere” hydrogenation mechanism, comprehensive studies of temperature and solvent effects on the H 2 splitting and hydride transfer steps are expected to be relevant to many other catalysts. Strikingly large entropy associated with cleavage of H2 results in a strong temperature dependence on the concentration of [( tBu POCOP)Re(CO) 2 H] – present during catalysis, which is further impacted by changing the solvent from toluene to tetrahydrofuran to acetonitrile. New methods for determining the hydricity of metal hydrides and formate at temperatures other than 298 K were developed, providing insight into how temperature can influence the favorability of hydride transfer during catalysis. These thermochemical insights guided the selection of conditions for CO 2 hydrogenation to formate with high activity (up to 364 h –1 at 1 atm or 3330 h –1 at 20 atm of 1:1 H 2 CO 2 ). In cases where hydride transfer is the highest individual kinetic barrier, entropic contributions to outer sphere H 2 splitting lead to a unique temperature dependence: catalytic activity increases as temperature decreases in tetrahydrofuran (200-fold increase upon cooling from 50 to 0 °C) and toluene (4-fold increase upon cooling from 100 to 50 °C). Furthermore, the ramifications on catalyst structure-function relationships are discussed, including comparisons between “outer sphere” mechanisms and metal–ligand cooperation mechanisms.

14 SOLAR ENERGY↗