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Ab initio calculations of exciton effects in optical spectra of an α-B{sub 12} crystal
The dielectric functions and energy loss spectrum of electrons of a rhombohedral α-B{sub 12} crystal are studied both in the single-particle and many-particle approximations using Bethe–Salpeter equations. The opposite roles of different contributions to exciton effects are discussed. The anisotropy of dielectric functions is shown, which demonstrates their high sensitivity with respect to the three-dimensional packing of icosahedrons. The influence of the coherent mixing of electronic and exciton states on the redistribution of oscillator strengths is found. The position of the plasmon of valence electrons and the high-frequency permittivity are found to be consistent with experimental data. The correlation between the distribution of oscillator strengths and features of the density of electronic states is discussed.
A microfluidic study of transient flow states in permeable media using fluorescent particle image velocimetry
Velocity fields in flow in permeable media are of great importance to many subsurface processes such as geologic storage of CO 2 , oil and gas extraction, and geothermal systems. Steady-state flow is characterized by velocity fields that do not change significantly over time. The flow field transitions to a new steady state once it experiences a disturbance such as a change in flow rate or in pressure gradient. This transition is often assumed to be instantaneous, which justifies the expression of constitutive relations as functions of instantaneous phase saturations. This work examines the evolution of velocity fields in a surrogate quasi-2D permeable medium using a microfluidic device, a microscopy system, and a high-speed camera. Tracer particles are injected into the medium along with DI water. The evolution of the velocity field is examined by tracing these particles in the captured images using the standard high-density particle image velocimetry algorithm founded on cross-correlation. The results suggest that the transition between steady states for an incompressible fluid takes a finite and non-negligible amount of time that is independent of the magnitude of the change in pressure gradient. The existence of transient states and the nature of the response during these states are readily interpreted by the principle of least action where flow gradually establishes an optimal configuration such that energy dissipation is 2 minimized. The findings provide evidence against the applicability of the assumption that flowing phases relax instantaneously to their steady states and, hence, against the accuracy of the classical multiphase extension of Darcy's law.
Prediction of Structures and Atomization Energies of Coinage Metals, (M)n, n < 20: Extrapolation of Normalized Clustering Energies to Predict the Cohesive Energy
The geometries of the group 11 coinage metals (n = 2-20) were optimized to determine the lowest energy isomers for each cluster size, singlets for even numbers and doublets for odd numbers. For copper and silver, 2-D (planar) geometries were favored up to n = 6. For gold, 2D (planar) geometries were favored up to n = 13. Normalized clustering energies were plotted as a function of cluster size (n-1/3, for n = 4-20) with various DFT functionals and the CCSD(T)-F12b method and were extrapolated to predict the bulk cohesive energy. In the case of copper and silver, there is excellent agreement between the cohesive energies predicted at the CCSD(T)-F12b level of theory and the experimental values. For gold, the CCSD(T)-F12b values needed to be corrected for spin-orbit relativistic effects to obtain good agreement with experiment. Electronic properties including the HOMO-LUMO gaps for the even clusters and the spin densities for the odd clusters were calculated. The lowest gap is predicted to occur for n = 16 where the HOMO and LUMO are very similar in shape.
Exploiting electricity market dynamics using flexible electrolysis units for retrofitting methanol synthesis
Here we investigate the economic viability of integrating flexible electrolysis units to produce hydrogen in methanol synthesis processes. Specifically, we investigate whether this approach can help reduce methanol production costs by strategically exploiting dynamics of electricity markets. Our study integrates high-fidelity process simulations, optimization tools, and microkinetic modeling (informed by density functional theory) to conduct detailed techno-economic analyses and to compare performance against traditional processes that use hydrogen produced via steam-methane reforming (SMR). We also use this approach to estimate the levelized cost of hydrogen (LCOH) as a function of time-varying electricity prices (from day-ahead and real-time prices) and of key techno-economic parameters. Our results show that the proposed electrification framework is cost-competitive under certain electricity market conditions. Specifically, we find that, when the electrolysis system is operated in flexible mode (and can respond to dynamics of electricity markets), the associated electricity cost nearly collapses to zero. Conversely, when the unit is not flexible (and cannot respond to markets), the electricity cost comprises 60% of the total cost. Our results also reveal that the LCOH of the flexible electrolysis system participating in real-time electricity markets is 31% lower than the LCOH obtained from SMR. Overall, this indicates that exploiting the dynamics of electricity markets can make hydrogen production cost-competitive and this can lead to viable alternatives to electrify methanol production and other hydrogen-based processes.
Key role of antibonding electron transfer in bonding on solid surfaces
The description of the chemical bond between a solid surface and an atom or a molecule is the fundamental basis for understanding surface reactivity and catalysis. Despite considerable research efforts, the physics that rules the strength of such chemical bonds remains elusive, especially on semiconductor surfaces. Widespread understandings are mostly based on the degree of filling of antibonding surface-adsorbate states that weaken the surface adsorption. The unoccupied antibonding surface-adsorbate states are often considered to have no effects on surface bonding. Here in this paper, we show that the energy levels of unoccupied antibonding surface-adsorbate states relative to the Fermi-level play a critical role in determining the trends in variations of surface adsorption energies. The electrons that would occupy those high-energy antibonding states are transferred to the Fermi-level, leading to an energy gain that largely controls surface bonding. To illustrate this picture, as a validating case, we study the hydrogen evolution reaction (HER) catalyzed by MoS 2 from density functional theory calculations. We find that the majority of antibonding surface-hydrogen states are positioned well above the Fermi-energy. A clear linear relationship between the energy gain from antibonding electron transfer and the adsorption energy is identified for hydrogen binds to either molybdenum or sulfur atoms at different sites. The antibonding-electron transfer energy can thus serve as a primary catalytic activity descriptor. The emerging picture identifies the origin of HER on MoS 2 , which is related to the empty in-gap states induced by sulfur vacancies or edges. Under this picture, the effects of surface inhomogeneity (e.g., defects, step edges) on surface bonding strength can be understood. This antibonding electron transfer picture also offers a physically different explanation for the well-known d-band theory for hydrogen adsorption on transition metal surfaces. The results provide guidelines for understanding and optimizing catalyst performance and designing new solid catalysts.
Localizing Tungsten Single Atoms around Tungsten Nitride Nanoparticles for Efficient Oxygen Reduction Electrocatalysis in Metal–Air Batteries
Combining isolated atomic active sites with those in nanoparticles for synergizing complex multistep catalysis is being actively pursued in the design of new electrocatalyst systems. However, these novel systems have been rarely studied due to the challenges with synthesis and analysis. Herein, a synergistically catalytic performance is demonstrated with a 0.89 V (vs reversible hydrogen electrode) onset potential in the four-step oxygen reduction reaction (ORR) by localizing tungsten single atoms around tungsten nitride nanoparticles confined into nitrogen-doped carbon (W SAs/WNNC). Through density functional theory calculations, it is shown that each of the active centers in the synergistic entity feature a specific potential-determining step in their respective reaction pathway that can be merged to optimize the intermediate steps involving scaling relations on individual active centers. Impressively, the W SAs/WNNC as the air cathode in all-solid-state Zn-air and Al-air batteries demonstrate competitive durability and reversibility, despite the acknowledged low activity of W-based catalyst toward the ORR.
Background optimization of powder electron diffraction for implementation of the e-PDF technique and study of the local structure of iron oxide nanocrystals
The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as pair distribution function (PDF) (also known as reduced density function) analysis. The PDF profiles are derived from background-corrected powder electron diffraction patterns (the e-PDF technique). Due to the strong Coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron–specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least-square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.
Achieving High‐Performance and 2000 h Stability in Anion Exchange Membrane Fuel Cells by Manipulating Ionomer Properties and Electrode Optimization
The primary function of the ionomers that are incorporated into fuel cell electrode catalyst layers is to provide pathways for ion transport between the catalyst active sites and the electrolyte. This is influenced by many variables, including the ion-exchange capacity, water uptake, and molecular weight. In anion exchange membrane fuel cells (AEMFCs), controlling ionomer water uptake is particularly important and tailoring this property in each electrode is an important consideration when looking to maximize cell performance. In this study, three poly(norbornene) tetrablock copolymer ionomers with a range of physical properties are synthesized and incorporated into AEMFC anode and cathode electrodes. Systematic electrode engineering with these ionomers allows the peak power density to be increased by 100% (1.6 W cm -2 → 3.2 W cm -2 ) and the current density at 0.2 V to be increased by 59% (5.9 A cm -2 → 9.4 A cm -2 ). Moreover, the top-performing electrode configuration is tested in an operating AEMFC at the US Department of Energy defined current density of 600 mA cm -2 for 2000 h, showing a record-low voltage decay rate of 15.36 µV h -1 – only 3.65% –a over 2000 h. Here, this work sets a new bar for AEMFCs, reporting the best combination of performance and durability of any AEMFC to date.
A Green's function quantum average atom model
A quantum average atom model is reformulated using Green's functions. This allows integrals along the real energy axis to be deformed into the complex plane. The advantage being that sharp features such as resonances and bound states are broadened by a Lorentzian with a half-width chosen for numerical convenience. An implementation of this method therefore avoids numerically challenging resonance tracking and the search for weakly bound states, without changing the physical content or results of the model. A straightforward implementation results in up to a factor of 5 speed-up relative to an optimized orbital based code.
Computer-aided design of stability enhanced nicotinamide cofactor biomimetics for cell-free biocatalysis
Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.
Tuning the carrier concentration to improve the thermoelectric performance of CuInTe{sub 2} compound
The electronic and transport properties of CuInTe{sub 2} chalcopyrite are investigated using density functional calculations combined with Boltzmann theory. The band gap predicted from hybrid functional is 0.92 eV, which agrees well with experimental data and leads to relatively larger Seebeck coefficient compared with those of narrow-gap thermoelectric materials. By fine tuning the carrier concentration, the electrical conductivity and power factor of the system can be significantly optimized. Together with the inherent low thermal conductivity, the ZT values of CuInTe{sub 2} compound can be enhanced to as high as 1.72 at 850 K, which is obviously larger than those measured experimentally and suggests there is still room to improve the thermoelectric performance of this chalcopyrite compound.
CC2 oscillator strengths within the local framework for calculating excitation energies (LoFEx)
In a recent work [P. Baudin and K. Kristensen, J. Chem. Phys. 144, 224106 (2016)], we introduced a local framework for calculating excitation energies (LoFEx), based on second-order approximated coupled cluster (CC2) linear-response theory. LoFEx is a black-box method in which a reduced excitation orbital space (XOS) is optimized to provide coupled cluster (CC) excitation energies at a reduced computational cost. In this article, we present an extension of the LoFEx algorithm to the calculation of CC2 oscillator strengths. Two different strategies are suggested, in which the size of the XOS is determined based on the excitation energy or the oscillator strength of the targeted transitions. The two strategies are applied to a set of medium-sized organic molecules in order to assess both the accuracy and the computational cost of the methods. The results show that CC2 excitation energies and oscillator strengths can be calculated at a reduced computational cost, provided that the targeted transitions are local compared to the size of the molecule. Finally, to illustrate the potential of LoFEx for large molecules, both strategies have been successfully applied to the lowest transition of the bivalirudin molecule (4255 basis functions) and compared with time-dependent density functional theory.
Ultrafast Dynamics of Photoexcited Charge Carriers in In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As Superlattices under Femtosecond Laser Excitation
The results of experimental studies of the time dynamics of photoexcited charge carriers in In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As superlattices grown by molecular-beam epitaxy on a GaAs substrate with a metamorphic buffer are reported. On the basis of the results of the numerical simulation of band diagrams, the optimal thickness of the In{sub 0.52}Al{sub 0.48}As barrier layer (4 nm) is chosen. At this thickness, the electron wave functions in In{sub 0.53}Ga{sub 0.47}As substantially overlap the In{sub 0.52}Al{sub 0.48}As barriers. This makes it possible to attain a short lifetime of photoexcited charge carriers (τ ~ 3.4 ps) at the wavelength λ = 800 nm and the pumping power 50 mW without doping of the In{sub 0.53}Ga{sub 0.47}As layer with beryllium. It is shown that an increase in the wavelength to λ = 930 nm (at the same pumping power) yields a decrease in the lifetime of photoexcited charge carriers to τ ~ 2 ps. This effect is attributed to an increase in the capture cross section of trapping states for electrons with lower energies and to a decrease in the occupancy of traps at lower excitation densities.
Evaluation of WRF simulation of deep convection in the US Southern Great Plains
The Southern Great Plains (SGP) exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Here, seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) model applied at convection permitting resolution with the Mibrandt-Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization RADAR, gridded data sets and observations at the DoE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios >1 indicate WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mmhr –1 over the domain. Detailed analyses of the ten days with highest spatial coverage of cREF >30 dBZ show spatially similar reflectivity fields and high RR in both RADAR data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (> 25 mmhr –1 ) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.
Structure of chalcogen overlayers on Au(111): Density functional theory and lattice-gas modeling
Ordering of different chalcogens, S, Se, and Te, on Au(111) exhibit broad similarities but also some distinct features, which must reflect subtle differences in relative values of the long-range pair and many-body lateral interactions between adatoms. We develop lattice-gas (LG) models within a cluster expansion framework, which includes about 50 interaction parameters. These LG models are developed based on density functional theory (DFT) analysis of the energetics of key adlayer configurations in combination with the Monte Carlo (MC) simulation of the LG models to identify statistically relevant adlayer motifs, i.e., model development is based entirely on theoretical considerations. The MC simulation guides additional DFT analysis and iterative model refinement. Given their complexity, development of optimal models is also aided by strategies from supervised machine learning. The model for S successfully captures ordering motifs over a broader range of coverage than achieved by previous models, and models for Se and Te capture the features of ordering, which are distinct from those for S. More specifically, the modeling for all three chalcogens successfully explains the linear adatom rows (also subtle differences between them) observed at low coverages of ~0.1 monolayer. The model for S also leads to a new possible explanation for the experimentally observed phase with a (5 × 5)-type low energy electron diffraction (LEED) pattern at 0.28 ML and to predictions for LEED patterns that would be observed with Se and Te at this coverage.
Etched Silicon Planar CRL Optics for the High-Energy X-ray Diffraction Beamlines 11-ID-B and 11-ID-C at the APS
We report the focusing of high-energy X-rays is most commonly achieved by commercially available compound refractive lenses (CRL), which are added together in larger structures. There is a need for high-energy X-ray beamlines to enable focusing in only one dimension down to a few micrometers to study layered materials and thin films. High-energy X-rays above 50 keV require often 100 lenses or more, therefore it is more efficient to etch the complete arrays of lenses into a monolithic substrate like a silicon wafer. Custom lens arrays were fabricated for the high-energy x-ray beamlines 11-ID-B, and 11-ID-C at the Argonne National Laboratory. The fabrication required tuning parameters of the Bosch process to optimize the etching of deep vertical sidewalls at a reasonable speed without overheating. The beamline 11-ID-B is equipped with lens arrays of 85-110 lenses for 59 keV and 190-250 lenses for 87 keV photon energy, focusing the beam vertically below 5.0 µm at the sample positions between 1600 mm to 2100 mm. 11-ID-C has received arrays of 435 lenses that focus 106 keV X-rays to a vertical height of 2.0 µm at the sample position. In both cases, the flux density gain in the focal spot is larger than a factor of 20, relative to using slits to cut the beam size. The lens setup is permanently installed and can be moved in and out of the beam during the experiment without further alignment. The lenses have enabled operando diffraction studies on interfaces in batteries and the development of pair distribution function (PDF) analysis using grazing incidence geometry.
Manufacturing of Fabric Electrodes using a High-Throughput Screening Platform for Redox Flow Batteries
The objective of this project is to establish a new manufacturing methodology with machine learning- based high-throughput screening for the design and development of hierarchical structured, high-performance fabric electrodes for redox flow batteries (RFBs). The end goal of the project is to design and manufacture fabric electrodes for RFB applications that can provide 250 mA/cm2 current density operation for 100-cycles with 80% average energy efficiency. This was accomplished by first examining the structure-performance-property linkages of the electrodes provided by our partner, AvCarb. The electrodes’ microstructure was characterized by determining their pore size distribution, tortuosity, specific surface area, and porosity. The ohmic, charge transfer and mass transfer resistances were then calculated using electrochemical impedance spectroscopy. Carbon cloth electrodes showed the greatest resistance, which was dominated by charge transfer resistance, which we believe is related to the surface functionalization. Full cell cycling was used in order to determine the area specific resistance and energy efficiency of the cells. All of this experimental data and the results of the mathematical model (to increase the amount of inputs with parametric sweeping) were used to develop a machine learning-based model for the design of high-performance fabric electrodes. Using the results from the machine learning tool, optimized electrodes were fabricated by AvCarb. The ohmic, charge transfer and mass transfer resistances for these new electrodes were measured, and both performed better than any of the initial samples which had been provided by AvCarb.