SEARCH · Engineering Papers
Results for “surface energetics”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Selective dehydra-decyclization of cyclic ethers to conjugated dienes over zirconia
ZrO 2 provides high selectivity (>90%) to conjugated pentadienes through the dehydra-decyclization of C-5 cyclic ethers, even at high conversions. 1,3-Pentadiene was the major product in both reaction of 2-methyltetrahydrofuran and tetrahydropyran over ZrO 2 . The reaction of 3-methyltetrahydrofuran produced nearly stoichiometric amounts of isoprene. Other catalysts, including TiO 2 , γ-Al 2 O 3 , and H-ZSM-5, were generally much less selective and produced a mixture of diene isomers. A combination of TPD and steady-state measurements revealed that both piperylenes are exclusively produced through primary catalytic pathways from 2-methyltetrahydrofuran, avoiding any isomerization once formed. First-principle calculations on ZrO 2 imply the presence of an energetically favored, surface isomerization of ring-opened intermediates to conjugated alkenolates that selectively dehydrate to conjugated dienes, providing high selectivity to the desired products. Finally, the stabilization of the conjugated alkenolate is key for understanding the ability of ZrO 2 to selectively produce conjugated dienes from cyclic ethers, without the need for the thermo-limited diene isomerization.
Cation-Dependent Multielectron Kinetics of Metal Oxide Splitting
We report direct electrolytic extraction of metals from metal oxides is a promising process for the sustainable production of metals. In this work, we elucidate the inherent thermodynamic driving forces behind the reduction of metal oxides to metals (M-OER). It is shown that the thermodynamics of M-OER can be systematically tuned via the interactions of oxygen with the participating metal cations as a function of metal–oxygen covalency, oxygen–oxygen covalency, and metal–oxygen ionicity. We screen both group 1 elements and metals that are able to exist in the +2 oxidation state for M-OER thermodynamics. Li, Fe, and Co are identified as having low thermodynamic overpotentials for electrolytic extraction from their metal oxides due to interactions between oxygen and these metals being neither too strong (covalent) nor too weak (ionic). We further show that the bulk formation energies are predictive of M-OER reaction energetics on surfaces by developing unified design principles for tuning the thermodynamics of these reduction reactions both in bulk oxides and on surfaces.
Unraveling Electronic Trends in O* and OH* Surface Adsorption in the MO 2 Transition-Metal Oxide Series
Understanding the bond strength of O* and OH* intermediates to metal-oxide surfaces is key to predicting the catalytic activity in oxygen-based electrochemistry. Here, we uncover highly non-linear trends in O* and OH* adsorption energies across the 3d, 4d, and 5d series of MO 2 transition-metal (TM) oxide surfaces computed within Hubbard- U corrected density functional theory (DFT + U ). Investigating the electronic structure with crystal orbital Hamiltonian populations (COHP) of the relevant metal–oxygen bonds reveals that the spin-dependent coupling strength between metal-d and oxygen-2p atomic orbitals together with the extent of filling of bonding and anti-bonding orbitals are the primary contributors to the adsorption energy. Importantly, we show that the integrated COHP obtained purely from bulk calculations is a highly accurate descriptor for surface adsorption energetics that captures trends across the group 5–12 TM oxide series within 0.19–0.36 eV. Our results suggest a pathway to prediction of adsorption energies for an arbitrary metal–ligand catalyst system.
Multifaceted aerosol effects on precipitation
Aerosols have been proposed to influence precipitation rates and spatial patterns from scales of individual clouds to the globe. However, large uncertainty remains regarding the underlying mechanisms and importance of multiple effects across spatial and temporal scales. Here, in this study, we review the evidence and scientific consensus behind these effects, categorized into radiative effects via modification of radiative fluxes and the energy balance, and microphysical effects via modification of cloud droplets and ice crystals. Broad consensus and strong theoretical evidence exist that aerosol radiative effects (aerosol–radiation interactions and aerosol–cloud interactions) act as drivers of precipitation changes because global mean precipitation is constrained by energetics and surface evaporation. Likewise, aerosol radiative effects cause well-documented shifts of large-scale precipitation patterns, such as the intertropical convergence zone. The extent of aerosol effects on precipitation at smaller scales is less clear. Although there is broad consensus and strong evidence that aerosol perturbations microphysically increase cloud droplet numbers and decrease droplet sizes, thereby slowing precipitation droplet formation, the overall aerosol effect on precipitation across scales remains highly uncertain. Global cloud-resolving models provide opportunities to investigate mechanisms that are currently not well represented in global climate models and to robustly connect local effects with larger scales. This will increase our confidence in predicted impacts of climate change.
Energetics of silicon in the bulk and near surfaces of tungsten: a first-principles study
Abstract Siliconization of the tokamak walls is a candidate method to improve plasma confinement in fusion tokamaks containing tungsten plasma facing components (W PFCs). To understand the interactions of silicon (Si) with W, the Si behavior in bulk W, and near three low-index W surfaces ((100), (110) and (111)) has been investigated using first-principles density functional theory. In bulk W, Si interstitial atoms have a low solution ability and high mobility, and Si atoms can be strongly trapped by W vacancies. The interaction between two Si adatoms is responsible for the stability of adatom superstructures on W surfaces, consistent with previous experimental observation (Tsong and Casanova 1981 Phys. Rev. Lett. 47 113). Although the coverage dependence of Si adsorption and diffusion energetics on surfaces is related to surface orientation, the W(110) surface has lower Si adsorption affinity and higher Si diffusivity than either the W(111) or W(100) surfaces. The most stable Si adatom superstructure on W surfaces is: square c(2 × 2) pattern on W(100) covered with 0.5 ML Si; rectangular c(4 × 2) pattern on W(110) with 0.25 ML Si; and rhombus p(1 × 1) pattern on W(111) with 1 ML Si. The coverage dependence of Si mobility on/toward W surfaces is generally related to the stability of the Si superstructures as a function of coverage on each surface. Interestingly, Si adatoms prefer to transport below the surface and into W subsurface by an exchange mechanism with W atoms, indicating the likelihood of epitaxial growth of W silicide layers on W surfaces during the operation of W PFCs.
Data–driven and constrained optimization of semi–local exchange and nonlocal correlation functionals for materials and surface chemistry
Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Herein, we present an empirical approach to simultaneously optimize semi-local exchange and nonlocal correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.
Variations in proton transfer pathways and energetics on pristine and defect-rich quartz surfaces in water: Insights into the bimodal acidities of quartz
Hypothesis. Understanding the mechanisms of proton transfer on quartz surfaces in water is critical for a range of processes in geochemical, environmental, and materials sciences. The wide range of surface acidities (>9 pKa units) found on the ubiquitous mineral quartz is caused by the structural variations of surface silanol groups. Molecular scale simulations provide essential tools for elucidating the origin of site-specific surface acidities. Simulations. Here, we used density-functional tight-binding-based molecular dynamics combined with rare-event metadynamics simulations to probe the mechanisms of deprotonation reactions from ten representative surface silanol groups found on both pristine and defect-rich quartz (1 0 1) surfaces with Si vacancies. Findings. The results show that deprotonation is a highly dynamic process where both the surface hydroxyls and bridging oxygen atoms serve as the proton acceptors, in addition to water. Deprotonation of embedded silanols through intrasurface proton transfer exhibited lower pKa values with less H-bond participation and higher energy barriers, suggesting a new mechanism to explain the bimodal acidity observed on quartz surface. Defect sites, recently shown to comprise a significant portion of the quartz (1 0 1) surface, diversify the coordination and local H-bonding environments of the surface silanols, changing both the deprotonation pathways and energetics, leading to a wider range of pKa values (2.4 to 11.5) than that observed on pristine quartz surface (10.4 and 12.1).
Ab Initio Structures and Energetics of Hydrated Flat and Terrace-Step Surfaces of Forsterite (Mg 2 SiO 4 )
Forsterite (Mg 2 SiO 4 ), a model divalent metal silicate mineral, has been extensively studied in the context of mineral carbonation. Although dissolution is a key step in this process, the mechanisms by which forsterite dissolves under high CO 2 conditions remain poorly understood. Atomistic simulations could aid in exploring these mechanisms, but it is essential first to understand the structures and energetics of the relevant forsterite surfaces. We present an ab initio study of the structure and surface energy at 0 K of the flat $(010), (110), (001), (111), (021), (101)$ and $(120)$ faces of forsterite using the density functional PBE Hamiltonian and a plane-wave basis set. Dry surfaces became stabilized upon hydration through the formation of bonds between surface Mg and O from water, as well as by the formation of hydrogen bonds. According to surface energy values, the stability order of the hydrated forsterite faces was found to be $(120) < (101) < (021) < (111) < (001) < (110) < (010)$. We also investigated the energetics of the terrace-step $(0\bar{41})$ surface as a model site for forsterite dissolution. Among all the facets, the $(0\bar{41})$ surface is the least stable termination in water. Hydration of Mg atoms on the $(0\bar{41})$ surface increases their susceptibility to dissolution. The presence of a step and its hydration destabilizes the terraces, making step retreat more likely than a dissolution front advancing along the [010] direction. This research will support future simulations to investigate forsterite dissolution in water under CO 2 -rich conditions.
Revealing Local and Directional Aspects of Catalytic Active Sites by the Nuclear and Surface Electrostatic Potential
This work examines the prospects of using the electrostatic potential, V(r), as a descriptor in heterogeneous catalysis. In particular, the subatomic spatial resolution of the property allows for analysis of both directionality and confinement effects in surface adsorption. This feature of V(r) is used to identify adsorption sites, orientations, and energetics for metal surfaces, particles, and nanoclusters upon interactions with catalytically relevant intermediates. The use of V(r) in assessing the 3D nature of catalytic sites in low-temperature and electrocatalysis is highlighted, and future directions in catalysis design are discussed. Ultimately, we provide a critical analysis of the use of V(r) in the predictions of local adsorption susceptibilities, and we address its limitations. The link between V(r) and other established descriptors in catalysis are motivated via physical relations and theoretical derivations; close ties are established between V(r) and the d-band center (ε d ), as well as the surface site stability (BE M ). In conclusion, by comparing the performance of V(r) evaluated on isodensity contours, i.e., the surface electrostatic potential, to that of V(r) evaluated at the nucleus of an atom, we investigate the application space for a directional and an atom-localized version of the V(r) descriptor for catalyst design.
Regulating surface potential maximizes voltage in all-perovskite tandems
The open circuit voltage (V OC ) deficit in perovskite solar cells (PSCs) is greater in wide bandgap (>1.7 eV) cells than in ~1.5 eV perovskites. Quasi-Fermi level splitting (QFLS) measurements reveal V OC -limiting recombination at the electron transport layer (ETL) contact. This, we find, stems from inhomogeneous surface potential and poor perovskite-ETL energetic alignment. Common monoammonium surface treatments fail to address this; instead we introduce diammonium molecules to modify the perovskite surface states and achieve a more uniform spatial distribution of surface potential. Using 1,3-propane diammonium (PDA), QFLS increases by 90 meV, enabling 1.79 eV PSCs with a certified 1.33 V V OC , and > 19% power conversion efficiency (PCE). Incorporating this layer into a monolithic all-perovskite tandem, we report a record V OC of 2.19 V (89% of the Detailed Balance V OC limit) and > 27% PCE (26.3% certified quasi-steady-state). Furthermore, these tandems retain more than 86% of their initial PCE after 500 hrs operation.
Facet-dependent structure and dissociation of water at pristine IrO 2 /water interfaces
Understanding the microscopic structure of water at metal oxide interfaces is crucial for advancing electrocatalysis. IrO 2 , specifically, has shown exceptional activity for electrochemical water oxidation, but we currently lack a fundamental understanding of how the surface structure of IrO 2 impacts water reactivity. In this work, we developed a machine learning potential trained to first-principles accuracy for modeling IrO 2 /water interfaces across different facets: (110), (100), (101), and (001). Using extensive machine learning molecular dynamics simulations, we investigated the spontaneous dissociation of water molecules at these interfaces. Our results reveal a distinct dissociation probability trend: (110) > (100) ≈ (101) > (001), which we attribute primarily to the reaction thermodynamics of surface water dissociation. A strong correlation is observed between the surface Ir–O bond distances and the dissociation probabilities, highlighting the role of surface geometry in modulating reactivity. As a consequence, the interfacial solvation structures and hydrogen bonding environments are dynamically tuned by the varying water dissociation capabilities across facets. This work elucidates how water dissociation energetics depend on surface orientation and interfacial structure, offering atomistic insights into manipulating reaction chemistry at electrocatalytic interfaces.
Methods and systems for evaluating a target using pulsed, energetic particle beams
A method for evaluating a target, the target having a surface, includes pulsing a defined, energetic particle beam through the surface and into the target such that particle energy deposition from the particle beam is concentrated in a subsurface target volume within a target medium of the target. The deposited particle energy induces a thermoelastic expansion of the target medium in the target volume that generates a corresponding acoustic wave. The method further includes detecting the acoustic wave from the target medium.
Stability-limiting heterointerfaces of perovskite photovoltaics
Optoelectronic devices consist of heterointerfaces formed between dissimilar semiconducting materials. The relative energy level alignment between contacting semiconductors determinately affects the heterointerface charge injection and extraction dynamics. For perovskite solar cells (PSCs), the heterointerface between the top perovskite surface and a charge-transporting material (CTM) is often treated for defect passivation to improve PSC stability and performance. However, such surface treatments could also affect the heterointerface energetics. Here we show that surface treatments may induce a negative work function shift (i.e. more n-type), which activates halide migration to aggravate PSC instability. Therefore, despite the beneficial effects of surface passivation, this detrimental side effect limits the maximum stability improvement attainable for PSCs treated in these ways. Furthermore, this trade-off between the beneficial and detrimental effects should guide further work on improving PSC stability via surface treatments.
Investigating a novel magnetic MAX phase nitride and its (001)-surfaces
We report magnetic MAX phases including their surfaces exhibit promising functional properties for magneto-electronic devices and self-monitoring smart coatings. By employing an integrated ab-initio approach, here we investigate a new member of the MAX phase, Mn 2 AuN. This compound satisfies the chemical, mechanical and dynamical stability criteria, leading to a possibility of its synthesis, and exhibits electronic and elastic anisotropy. The identified ferromagnetic configuration (with high Curie temperature) is the most energetically favorable among the five different spin configurations i.e., non-magnetic, ferromagnetic, and three anti-ferromagnetic. From the coatings perspective, the surface properties of Mn 2 AuN(001) terminations are investigated considering the four possible surface termination models. Remarkably, the magnetism does not get vanished even after cleaving the bulk unit cell into the (001)-surfaces. While evaluating the surface energetics in different chemical potentials, the N-001 terminated surface comes out to be the most stable termination contrasting with carbide MAX phases and is the only termination that exhibits the magnetic characteristics.
PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction
Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.
First-Principles Insights into the Thermocatalytic Cracking of Ammonia-Hydrogen Blends on Fe(110). 2. Kinetics
Ammonia (NH 3 ) is an energy-rich molecule that is routinely synthesized from nitrogen (N 2 ) and hydrogen (H 2 ). NH 3 ’s more favorable physical properties compared to H 2 suggests it may offer a way to more conveniently store, transport, and, when needed, extract H 2 via thermal decomposition. However, the high kinetic barrier and endoergicity to decompose to H 2 and N 2 require high temperatures. The standard reaction free energy indicates nearly 100% thermodynamic conversion to the diatomic molecules only at ~673 K and higher. However, even at these temperatures, a catalyst, e.g., iron (Fe), is needed for favorable kinetic conversion. Here, in this study, we explore via density functional theory the kinetics of NH 3 decomposition on the most stable facet of body-centered cubic Fe, namely, (110), under typical high-temperature and finite-pressure operando conditions. We predict coverage-dependent energetics of elementary surface reactions, often neglected in atomic-scale modeling. From these models, we find the recombinative desorption of adsorbed N as N 2 is rate-determining at 573.15–773.15 K and even at an extreme case of 1173.15 K. From microkinetic modeling, we find that the steady-state turnover frequencies (TOFs) for N 2 and H 2 generation rates (r$_{H_2}$) depend exponentially on temperature. The catalyst achieves a steady-state TOF of 36.4 s –1 and an r$_{H_2}$ of 0.107 μmol cm –2 s –1 for a feed of 1.8 bar NH 3 with 0.2 bar H 2 at 1173.15 K. However, at 773.15 K, with the same feed composition and velocity, the steady-state TOF and r$_{H_2}$ decrease to 0.14 s –1 and 4.10 × 10 –4 μmol cm –2 s –1 , respectively, as the process is significantly hindered by slow N 2 desorption. Although at first glance counterintuitive, our simulations suggest that surface modifications that reduce Fe’s reactivity toward NH x species should enhance its overall NH 3 decomposition activity.
Characterizing Surface Ice-Philicity Using Molecular Simulations and Enhanced Sampling
The formation of ice, which plays an important role in diverse contexts ranging from cryopreservation to atmospheric science, is often mediated by solid surfaces. Although surfaces that interact favorably with ice (relative to liquid water) can facilitate ice formation by lowering nucleation barriers, the molecular characteristics that confer ice-philicity to a surface are complex and incompletely understood. To address this challenge, here we introduce a robust and computationally efficient method for characterizing surface ice-philicity that combines molecular simulations and enhanced sampling techniques to quantify the free energetic cost of increasing surface–ice contact at the expense of surface–water contact. Using this method to characterize the ice-philicity of a family of model surfaces that are lattice matched with ice but vary in their polarity, we find that the nonpolar surfaces are moderately ice-phobic, whereas the polar surfaces are highly ice-philic. In contrast, for surfaces that display no complementarity to the ice lattice, we find that ice-philicity is independent of surface polarity and that both nonpolar and polar surfaces are moderately ice-phobic. Furthermore, our work thus provides a prescription for quantitatively characterizing surface ice-philicity and sheds light on how ice-philicity is influenced by lattice matching and polarity.