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Chemoresistive sensors based on multiwalled carbon nanotubes (MWCNTs) functionalized with SnO{sub 2} nanocrystals (NCs) have great potential for detecting trace gases at low concentrations (single ppm levels) at room temperature, because the SnO{sub 2} nanocrystals act as active sites for the chemisorption of gas molecules, and carbon nanotubes (CNTs) act as an excellent current carrying platform, allowing the adsorption of gas on SnO{sub 2} to modulate the resistance of the CNTs. However, uniform conjugation of SnO{sub 2} NCs with MWCNTs is challenging. An effective atomic layer deposition based approach to functionalize the surface of MWCNTs with SnO{sub 2} NCs, resulting in a novel CH{sub 4} sensor with 10 ppm sensitivity, is presented in this paper. Scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy, and Raman spectroscopy were implemented to study the morphology, elemental composition, and the crystal quality of SnO{sub 2} functionalized MWCNTs. High resolution TEM images showed that the crystal quality of the functionalizing SnO{sub 2} NCs was of high quality with clear lattice fringes and the dimension almost three times smaller than shown thus far in literature. A lift-off based photolithography technique comprising bilayer photoresists was optimized to fabricate SnO{sub 2} functionalized MWCNTs-based chemoresistor sensor, which at room temperature can reliably sense below 10 ppm of CH{sub 4} in air. Such low level gas sensitivity, with significant reversible relative resistance change, is believed to be the direct result of the successful functionalization of the MWCNT surface by SnO{sub 2} NCs.
The work functions of various amorphous and crystalline transparent conducting oxides (TCO5) were measured using Kelvin probe. The films, made by pulsed laser deposition, exhibited varying work functions dependent on the composition and deposition parameters. Tin oxide showed the largest work functions of the oxides measured, while zinc oxide showed the lowest. Binary and ternary combinations of the basis TCOs showed intermediate work functions dependent on the endpoint components. Amorphous TCO5, important in OPV and other technological applications, exhibited similar work functions to their crystalline counterparts. UV/ozone treatment of TCOs temporarily increased the work function, consistent with proposed defect mechanisms associated with near-surface changes in carrier content and Fermi level. Finally, a method for facile adjustment of the work function of commercial TCOs by atomic layer deposition (ALD) capping layers was presented, illustrated by the growth of zinc oxide layers on commercial crystalline ITO films.
Gluonic pole matrix elements explain the appearance of single spin asymmetries (SSA) in high-energy scattering processes. They involve a combination of operators which are odd under time reversal (T-odd). Such matrix elements appear in principle both for parton distribution functions and parton fragmentation functions. We show that for parton fragmentation functions these gluonic pole matrix elements vanish as a consequence of the analytic structure of scattering amplitudes in Quantum Chromodynamics. Furthermore, this result is important in the study of the universality of transverse momentum dependent (TMD) fragmentation functions.
We present a summary of a recent workshop held at Duke University on Partonic Transverse Momentum in Hadrons: Quark Spin-Orbit Correlations and Quark-Gluon Interactions. The transverse momentum dependent parton distribution functions (TMDs), parton-to-hadron fragmentation functions, and multi-parton correlation functions, were discussed extensively at the Duke workshop. In this paper, we summarize first the theoretical issues concerning the study of partonic structure of hadrons at a future electron-ion collider (EIC) with emphasis on the TMDs. We then present simulation results on experimental studies of TMDs through measurements of single spin asymmetries (SSA) from semi-inclusive deep-inelastic scattering (SIDIS) processes with an EIC, and discuss the requirement of the detector for SIDIS measurements. The dynamics of parton correlations in the nucleon is further explored via a study of SSA in D (`D) production at large transverse momenta with the aim of accessing the unexplored tri-gluon correlation functions. The workshop participants identified the SSA measurements in SIDIS as a golden program to study TMDs in both the sea and valence quark regions and to study the role of gluons, with the Sivers asymmetry measurements as examples. Such measurements will lead to major advancement in our understanding of TMDs in the valence quark region, and more importantly also allow for the investigation of TMDs in the sea quark region along with a study of their evolution.
The hydrogenation of furfural to furfuryl alcohol on Pd(111), Cu(111) and Pt(111) is studied with both standard Density Functional Theory (DFT)-GGA functionals and with van der Waals-corrected density functionals. VdW-DF functionals, including optPBE, optB88, optB86b, and Grimme's method, are used to optimize the adsorption configurations of furfural, furfuryl alcohol, and related intermediates resulting from hydrogenation of furfural, and the results are compared to corresponding values determined with GGA functionals, including PW91 and PBE. On Pd(111) and Pt(111), the adsorption geometries of the intermediates are not noticeably different between the two classes of funcfionals, while on Cu(111), modest changes are seen in both the perpendicular distance and the orientation of the aromatic ring with respect to the planar surface. In general, the binding energies increase substantially in magnitude as a result of van der Waals contributions on all metals. In contrast, however, dispersion effects on the kinetics of hydrogenation are relatively small. It is found that activation barriers are not significantly affected by the inclusion of dispersion effects, and a Brensted-Evans-Polanyi relationship developed solely from PW91 calculations on Pd(111) is capable of describing corresponding results on Cu(111) and Pt(111), even when the dispersion effects are included. Finally, the reaction energies and barriers derived from the dispersion-corrected and pure GGA calculations are used to plot simple potential energy profiles for furfural hydrogenation to furfulyl alcohol on the three considered metals, and an approximately constant downshift of the energetics due to the dispersion corrections is observed.
This paper presents the development of a new exchange–correlation functional from the point of view of machine learning. Using atomization energies of solids and small molecules, we train a linear model for the exchange enhancement factor using a Bayesian approach which allows for the quantification of uncertainties in the predictions. A relevance vector machine is used to automatically select the most relevant terms of the model. We then test this model on atomization energies and also on bulk properties. The average model provides a mean absolute error of only 0.116 eV for the test points of the G2/97 set but a larger 0.314 eV for the test solids. In terms of bulk properties, the prediction for transition metals and monovalent semiconductors has a very low test error. However, as expected, predictions for types of materials not represented in the training set such as ionic solids show much larger errors.
Despite the great success that theoretical approaches based on density functional theory have in describing properties of solid compounds, accurate predictions of the enthalpies of formation ({Delta}H{sub f}) of insulating and semiconducting solids still remain a challenge. This is mainly due to incomplete error cancellation when computing the total energy differences between the compound total energy and the total energies of its elemental constituents. In this paper we present an approach based on GGA + U calculations, including the spin-orbit coupling, which involves fitted elemental-phase reference energies (FERE) and which significantly improves the error cancellation resulting in accurate values for the compound enthalpies of formation. We use an extensive set of 252 binary compounds with measured {Delta}H{sub f} values (pnictides, chalcogenides, and halides) to obtain FERE energies and show that after the fitting, the 252 enthalpies of formation are reproduced with the mean absolute error MAE = 0.054 eV/atom instead of MAE {approx} 0.250 eV/atom resulting from pure GGA calculations. When applied to a set of 55 ternary compounds that were not part of the fitting set the FERE method reproduces their enthalpies of formation with MAE = 0.048 eV/atom. Furthermore, we find that contributions to the total energy differences coming from the spin-orbit coupling can be, to a good approximation, separated into purely atomic contributions which do not affect {Delta}H{sub f}. The FERE method, hence, represents a simple and general approach, as it is computationally equivalent to the cost of pure GGA calculations and applies to virtually all insulating and semiconducting compounds, for predicting compound {Delta}H{sub f} values with chemical accuracy. We also show that by providing accurate {Delta}H{sub f} the FERE approach can be applied for accurate predictions of the compound thermodynamic stability or for predictions of Li-ion battery voltages.
This paper will describe a formalism for using correlation functions between different grid cells as the basis for determining coarse-grained hydrodynamic equations for modeling the behavior of mesoscopic fluid systems. Configurations from a molecular dynamics simulation or other atomistic simulation are projected onto basis functions representing grid cells in a continuum hydrodynamic simulation. Equilibrium correlation functions between different grid cells are evaluated from the molecular simulation and used to determine the evolution operator for the coarse-grained hydrodynamic system. The formalism is demonstrated on a discrete particle simulation of diffusion with a spatially dependent diffusion coefficient. Correlation functions are calculated from the particle simulation and the spatially varying diffusion coefficient is recovered using a fitting procedure.
A series of rhodium (III) bis(quinolinyl)benzene (bisq x ) complexes was researched as candidates for the homogeneous partial oxidation of methane. Density functional theory (DFT) (M06 with Poisson continuum solvation) was used to investigate a variety of (bisq x ) ligand candidates involving different functional groups to determine the impact on RhIII(bisq x ) catalyzed methane functionalization. The free energy activation barriers for methane C–H activation and Rh–methyl functionalization at 298 K and 498 K were determined. DFT studies predict that the best candidate for catalytic methane functionalization is Rh III coordinated to unsubstituted bis(quinolinyl)benzene (bisq). Support is also found for the prediction that the η 2 -benzene coordination mode of (bisq x ) ligands on Rh encourages methyl group functionalization by serving as an effective leaving group for S N 2 and S R 2 attack.
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. In addition, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.
Microbially mediated biogeochemical processes are catalyzed by enzymes that control the transformation of carbon, nitrogen, and other elements in environment. The dynamic linkage between enzymes and biogeochemical species transformation has, however, rarely been investigated because of the lack of analytical approaches to efficiently and reliably quantify enzymes and their dynamics in soils and sediments. Herein, we developed a signature peptide-based technique for sensitively quantifying dissimilatory and assimilatory enzymes using nitrate-reducing enzymes in a hyporheic zone sediment as an example. Moreover, the measured changes in enzyme concentration were found to correlate with the nitrate reduction rate in a way different from that inferred from biogeochemical models based on biomass or functional genes as surrogates for functional enzymes. This phenomenon has important implications for understanding and modeling the dynamics of microbial community functions and biogeochemical processes in environments. Our results also demonstrate the importance of enzyme quantification for the identification and interrogation of those biogeochemical processes with low metabolite concentrations as a result of faster enzyme-catalyzed consumption of metabolites than their production. The dynamic enzyme behaviors provide a basis for the development of enzyme-based models to describe the relationship between the microbial community and biogeochemical processes.
Modern life is enabled by a complex and interdependent web of critical functions, including energy, communications, transportation, food, and water. Automation has significantly reduced or replaced human interactions in the delivery of these functions, resulting in a web of goods and services that are made available 24/7 only through unique and intentional deployments of microprocessors, software, and firmware technologies. The prospect of cyber-enabled sabotage of these processes disrupts traditional risk determination models. Critical Function Assurance (CFA) is a foundational approach to identifying, prioritizing, and mitigating the risk that is inherent in the delivery of critical functions that depend on digital technology. It provides rapid focus to what matters most and illuminates elements and areas of risk that otherwise are often overlooked. This focus enables effective application of available security resources and optimizes security strategy and policy efforts. This paper introduces CFA to decision makers and risk executives (including CEOs, COOs, CFOs, and CISOs) whose organizations support and deliver the critical functions that underpin national defense, societal health and safety, and a vibrant economy.