Chad Priest

Estimating methane emissions in California's urban and rural regions using multitower observations

We present an analysis of methane (CH_4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH_4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH_4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on seasonally varying California-specific CH_4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH_4 emissions are 2.42u2009±u20090.49u2009Tgu2009CH_4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64u2009Tgu2009CH_4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model-measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH_4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH_4 emissions, in agreement with CARBs inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of Californias CH_4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).

Structural Evolution of Tcn (n = 4-20) Clusters from First-Principles Global Minimization.

We explore the structural evolution of Tcn (n = 4-20) clusters using a first-principles global minimization technique, namely, basin-hopping from density functional theory geometry optimization (BH-DFT). Significantly more stable structures have been found in comparison with previous models, indicating the power of DFT-based basin hopping in finding new structures for clusters. The growth sequence and pattern for n from 4 to 20 are analyzed from the perspective of geometric shell formation. The binding energy per atom, relative stability, and magnetic moments are examined as a function of the cluster size. Several magic sizes of higher stability and symmetry are discovered. In particular, we find that Tc19 prefers an Oh symmetry structure, resembling a piece of a face-centered-cubic metal, and its electrostatic potential map shows interesting features that indicate special reactivity of the corner atoms.

Solvation of the Ca2UO2(CO3)3 Complex in Seawater from Classical Molecular Dynamics

Uranium from the sea provides a long-time supply guarantee of nuclear fuels for centuries to come, and the neutral Ca2UO2(CO3)3 complex has been shown to be the dominant species of uranium in seawater. However, the solvation and structure of the Ca2UO2(CO3)3 complex in seawater have been unclear. Herein we simulate the Ca2UO2(CO3)3 complex in a model seawater solution via classical molecular dynamics. We find that Na(+) and Cl(-) ions interact very differently with the neutral Ca2UO2(CO3)3 complex in seawater. Especially, one Na(+) ion is closely associated with the Ca2UO2(CO3)3 complex, thereby effectively making the complex have a +1 charge, while Cl(-) ions are much farther away. Hence, this work reveals the important role of Na(+) ions in affecting the solvation of the Ca2UO2(CO3)3 complex in seawater, which has implications in designing ligands to attract the Ca2UO2(CO3)3 complex to the sorbent.

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF-STILT (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5u2009m/s), direction (< ~ 15°), and boundary layer height (< ~ 200u2009m) were small. However, random errors were large (~1.5–3.0u2009m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500u2009m for boundary layer height). Regression analysis of predicted and measured CO yielded near-unity slopes (i.e., within 1.0u2009±u20090.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF-STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

Hydrogen functionalisation of transition metal dichalcogenide monolayers from first principles

Abstract Two-dimensional transition metal dichalcogenide (TMD) monolayers are of great importance due to their unique properties and potential applications in fields such as electrocatalysis and optoelectronics. Although functionalisation of MoS2 has been recently studied through computation and experimentation, TMDs, such as MoSe2, WSe2, and WS2, have remained relatively unaddressed for chemical functionalisation. This study examines the effects brought about by the covalent functionalisation of MoSe2, WSe2, and WS2 by hydrogenation through first-principles density functional theory calculations. In particular, we examined the relationship between phase stability and surface functionalisation by comparing the stability of the 1T- and 2H phase at various hydrogen coverages. We found that the 1T phase became more stable than the 2H phase after a cross-over coverage: approximately 13, 11, and 18%, respectively, for MoSe2, WSe2, and WS2. The highest stability was achieved at close to 50% hydrogen coverage for the 1T phase. At this coverage, the 2H- to 1T-phase transition was found to be kinetically facile. We also found that the band gap of all three TMDs in the 1T phase can be tuned by varying the number of hydrogen coverage. This work shows that chemical functionalisation such as hydrogenation can be generally applied to tune the phase stability and electronic properties of TMD monolayers.

Uranyl–Glutardiamidoxime Binding from First-Principles Molecular Dynamics, Classical Molecular Dynamics, and Free-Energy Simulations

Exploring the structural interplay of ligands with uranyl can provide important knowledge for technology advances in uranium extraction from seawater. However, obtaining such chemical information is not an easy endeavor experimentally. From a plethora of computational methods, this work provides both microscopic insights and free-energy profiles of the binding between uranyl and deprotonated glutardiamidoxime (H2B) for which experimental structural information is not available, despite H2B being an important model ligand with an open-chain conformation for understanding aqueous uranium extraction chemistry. In our molecular dynamics (MD) simulations, we explicitly accounted for the water solvent as well as the Na+ and Cl- ions. We found that hydrogen bonding plays a critical role in dictating the binding configurations of B2- and HB- with uranyl. Simulated free energies of sequential ligand binding to form UO2B, [UO2B2]2-, and [UO2(HB)B]- show very good agreement with the experimental values, lending support to our structural insights. The potential of mean force simulations showed the common feature of an important intermediate state where one end of the ligand binds to uranyl while the other end is solvated in water. Bringing the loose end of the ligand to bind with uranyl has a free-energy barrier of 15-25 kJ/mol. The present work shows that the combined simulation approach can reveal key structural and thermodynamic insights toward a better understanding of aqueous complexation chemistry for uranium extraction from the sea.

Effect of Salt on the Uranyl Binding with Carbonate and Calcium Ions in Aqueous Solutions

The Ca2(UO2)(CO3)3 complex has been shown to be the dominant species of uranyl in different aqueous environments, and thermodynamic data of the complexation have been measured accurately recently. However, a detailed understanding of the binding processes with explicit consideration of the water molecules in the presence of common salt ions such as Na+ and Cl- has been lacking. Here we use classical molecular dynamics combined with umbrella sampling to map the complete binding processes and their free-energy profiles leading to formation of the Ca2(UO2)(CO3)3 complex from UO22+, CO32-, and Ca2+ in an aqueous NaCl solution to simulate the seawater conditions. We find that the presence of Na+ ions affects the binding between UO22+ and CO32- as well as between [(UO2)(CO3)3]4- and Ca2+ by changing the coordination mode of carbonate to UO22+. The free energies of binding from our simulations are in good agreement with the experimental data for both pure water and the NaCl solution. Our work shows that free-energy simulations based on classical molecular dynamics simulations can be a useful tool to examine the atomistic process of the ligand binding to form the Ca2(UO2)(CO3)3 complex under different aqueous environments and that the presence of common ions can impact the complexation chemistry of uranyl.

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Author(s): Jeong, S; Newman, S; Zhang, J; Andrews, AE; Bianco, L; Dlugokencky, E; Bagley, J; Cui, X; Priest, C; Campos-Pineda, M; Fischer, ML | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Nitrous oxide (N2O) is a potent long-lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N2O is the third largest contributor to the states anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top-down inverse modeling. Here we present the first annual (2013–2014) statewide top-down estimates of anthropogenic N2O emissions. Utilizing continuous N2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N2O could be an important component in meeting Californias emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability.