Linear Combination Of Contributions Research Articles

Interactions of graphene with oxidants in a mixed atmosphere: synergistic effects of O2/H2O and O2/CO2 on gasification reactivity and kinetics.

The gasification of carbon with O2, CO2, and H2O oxidants plays an important role in several energy-based applications. As most of the industrial gasification processes are conducted under mixed-atmosphere conditions, the oxidation of carbon in binary oxidant mixtures becomes crucially important. Using reactive force-field (ReaxFF) potentials, extensive MD simulations were carried out on the oxidation behavior of graphene in mixed O2/H2O and O2/CO2 environments for a range of gas compositions and temperatures. A graphene sheet with a line defect comprising of eight and four-membered rings was used as the starting carbon structure. In addition to enhanced carbon gasification with oxygen additions, MD simulations showed synergistic interactions between different oxidants and their net influence on the overall reactivities. The gasification levels achieved under the binary system were higher than the linear combination of contributions from individual oxidants. The addition of ∼40% O2 in the binary mix was identified as the region with the highest reactivity during the initial stages of gasification. The oxidation reactions with oxygen were found to start instantaneously in the presence of H2O or CO2 instead of the usual initial delay. A very fast reaction kinetics was also observed in the initial stages in the presence of oxygen. Our results show that the gasification reactions under H2O and CO2 started at lower temperatures than O2 thereby creating a partially oxidized structure. Due to the presence of a large number of activation sites, very high rates of gasification were achieved with oxygen. These findings could help identify optimal oxidant compositions towards maximizing carbon gasification and minimizing CO2 emissions.

Double Asymptotic Structures of Topologically Interlocked Molecules

The mean square size of topologically interlocked molecules (TIMs) is presented as a linear combination of contributions from the backbone and subcomponents. Using scaling analyses and extensive molecular dynamics simulations of polycatenanes as a typical example of TIMs, we show that the effective exponent ν(m) for the size dependence of the backbone on the monomer number of subcomponent m is asymptotic to a value ν (∼0.588 in good solvents) with a correction of m-0.47, which is the same as for the covalently linked polymer. However, the effective exponent for the size dependence of subcomponents on m is asymptotic to the same value ν but with a new correction of m-1.0. The different corrections to the scaling on the backbone and subcomponent structure induce a surprising double asymptotic behavior for the architecture of the TIMs. The scaling model that takes into account the double asymptotic behavior is in good quantitative agreement with the simulation result that the effective exponent for the size dependence of TIMs on m increases with the subcomponent number n. The full scaling functional form of the size dependence on m and n for polycatenanes in a good solvent is well described by a simple sum of two limiting behaviors with different corrections.

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

The recently developed Deep Potential [Phys. Rev. Lett. 120 (2018) 143001 [27]] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the proper treatment of locality and symmetry properties of each component of the network. In this paper, we leverage its network structure to effectively represent the mapping from the atomic configuration to the electron density in Kohn-Sham density function theory (KS-DFT). By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the self-consistent electron density as the linear combination of contributions from many local clusters. The network is constructed to satisfy the translation, rotation, and permutation symmetries, and is designed to be transferable to different system sizes. We demonstrate that using a relatively small number of training snapshots, with each snapshot containing a modest amount of data-points, Deep Density achieves excellent performance for one-dimensional insulating and metallic systems, as well as systems with mixed insulating and metallic characters. We also demonstrate its performance for real three-dimensional systems, including small organic molecules, as well as extended systems such as water (up to 512 molecules) and aluminum (up to 256 atoms).

Non-universal gaugino mass models under the lamppost of muon (g-2)

In unified $\mathcal{N}=1$ supergravity scenario the gaugino masses can be non-universal. The patterns of these non-universalities are dictated by the vacuum expectation values of non-singlet chiral super-fields in visible sector. Here, we have analysed the model independent correlations among the gaugino masses with an aim to explain the $[1\div 3]\sigma$ excess of muon (g-2) ($\Delta a_\mu$). We have also encapsulated the interconnections among other low and high scale parameters, compatible with the collider constraints, Higgs mass, relic density and flavour data. We have noted that the existing non-universal models are not capable enough to explain $\Delta a_\mu$ at $[1\div 2]\sigma$ level. In the process, we have also shown the impact of recent limits from the searches for disappearing track and long lived charged particles at the LHC. These are the most stringent limits so far ruling out a large parameter space allowed by other constraints. We have also performed model guided analysis where gaugino masses are linear combination of contributions coming from singlet and non-singlet chiral super-fields. Here, a new mixing parameter has been introduced. Following the earlier methodology, we have been able to constrain this mixing parameter and pin down the promising models on this notion.

On the spread of changes in marine low cloud cover in climate model simulations of the 21st century

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.

Frequency shift or intensity shift? The origin of spectral changes in vibrational spectra

It has been shown that the frequency shift for a single band can be reconstructed by a linear combination of contributions from adjacent bands with fixed positions. Assuming discrete distribution of the peaks in a specified spectral range and an appropriate response function one can generate diverse patterns of spectral changes. This diversity was clearly manifested in the corresponding two-dimensional (2D) asynchronous contour plots that are very sensitive to the pattern of spectral variations in the input data. In the condensed phases the shape of spectral envelopes results from the superposition of numerous bands. Hence, in most cases the apparent ‘frequency shift’ actually means the ‘intensity shift’ due to variations in population of the species absorbing at different wave numbers.

Hydration Thermodynamics Using the Reference Interaction Site Model: Speed or Accuracy?

We report a method to dramatically improve the accuracy of hydration free energies (HFE) calculated by the 1D and 3D reference interaction site models (RISM) of molecular integral equation theory. It is shown that the errors in HFEs calculated by RISM approaches using the Gaussian fluctuations (GF) free energy functional are not random, but can be decomposed into linear combination of contributions from different structural elements of molecules (number of double bonds, number of OH groups, etc.). Therefore, by combining RISM/GF with cheminformatics, one can develop an accurate method for HFE prediction. We call this approach the structural description correction model (SDC) ( Ratkova et al. J. Phys. Chem. B 2010 , 114 , 12068 ). In this work, we investigated the prediction quality of the SDC model combined with 1D and 3D RISM approaches. In parallel, we analyzed the computational performance of these two methods. The SDC model parameters were obtained by fitting against a training set of 53 simple organic molecules. To demonstrate that the values of these parameters were transferable between different classes of molecules, the models were tested against 98 more complex molecules (including 38 polyfragment compounds). The results show that the 3D RISM/SDC model predicts the HFEs with very good accuracy (RMSE of 0.47 kcal/mol), while the 1D RISM approach provides only moderate accuracy (RMSE of 1.96 kcal/mol). However, a single 1D RISM/SDC calculation takes only a few seconds on a PC, whereas a single 3D RISM/SDC HFE calculation is approximately 100 times more computationally expensive. Therefore, we suggest that one should use the 1D RISM/SDC model for large-scale high-throughput screening of molecular hydration properties, while further refinement of these properties for selected compounds should be carried out with the more computationally expensive but more accurate 3D RISM/SDC model.

Thermoelectric Effect across the Metal−Insulator Domain Walls in VO2 Microbeams

We report on measurements of Seebeck effect in single-crystal VO(2) microbeams across their metal-insulator phase transition. One-dimensionally aligned metal-insulator domain walls were reversibly created and eliminated along single VO(2) beams by varying temperature, which allows for accurate extraction of the net contribution to the Seebeck effect from these domain walls. We observed significantly lower Seebeck coefficient in the metal-insulator coexisting regime than predicted by a linear combination of contributions from the insulator and metal domains. This indicates that the net contribution of the domain walls has an opposite sign from that of the insulator and metal phases separately. Possible origins that may be responsible for this unexpected effect were discussed in the context of complications in this correlated electron material.

Decomposition of Protein Experimental Compressibility into Intrinsic and Hydration Shell Contributions

The experimental determination of protein compressibility reflects both the protein intrinsic compressibility and the difference between the compressibility of water in the protein hydration shell and bulk water. We use molecular dynamics simulations to explore the dependence of the isothermal compressibility of the hydration shell surrounding globular proteins on differential contributions from charged, polar, and apolar protein-water interfaces. The compressibility of water in the protein hydration shell is accounted for by a linear combination of contributions from charged, polar, and apolar solvent-accessible surfaces. The results provide a formula for the deconvolution of experimental data into intrinsic and hydration contributions when a protein of known structure is investigated. The physical basis for the model is the variation in water density shown by the surface-specific radial distribution functions of water molecules around globular proteins. The compressibility of water hydrating charged atoms is lower than bulk water compressibility, the compressibility of water hydrating apolar atoms is somewhat larger than bulk water compressibility, and the compressibility of water around polar atoms is about the same as the compressibility of bulk water. We also assess whether hydration water compressibility determined from small compound data can be used to estimate the compressibility of hydration water surrounding proteins. The results, based on an analysis from four dipeptide solutions, indicate that small compound data cannot be used directly to estimate the compressibility of hydration water surrounding proteins.

Thermalization of fast cesium5D3∕2atoms in collisions with ground-state cesium atoms

We have investigated collisions involving fast, excited Cs atoms produced by photodissociating Cs{sub 2} molecules with a pulsed dye laser. The velocities of the atoms in the 5D state formed by the process Cs{sub 2}(X {sup 1}{sigma}{sub g}{sup +})+({Dirac_h}/2{pi}){omega}{sub pump}{yields}Cs{sub 2}{sup *}{yields}Cs(5D)+Cs(6S) are much greater than typical thermal velocities associated with the cell temperature. Using a narrow-band cw probe laser to observe the increased Doppler broadening of the 5D{sub 3/2}{yields}5F{sub 5/2} excitation line shape, we are able to monitor the time evolution of the velocity distribution of these 5D atoms. We analyze the data using a model that predicts the time-dependent excitation line shape of the fast atoms. Because the photons used to dissociate the molecules have a well-defined energy, the velocity distribution of the excited atoms in the early time after they are produced can be fairly well determined. Over time, velocity-changing collisions with ground-state Cs atoms cause the velocity distribution of excited atoms to approach the thermal limit. An analysis based on the strong-collision model leads to a prediction that the observed line shape at intermediate times will be a linear combination of contributions from distinct 'fast' and 'thermalized' atomic populations. By fitting our data to this model,more » a rate coefficient for velocity-changing collisions of fast Cs(5D{sub 3/2}) atoms with ground-state Cs atoms has been determined. The result k{sub VCC}=(6.1{+-}1.2)x10{sup -10} cm{sup 3} s{sup -1} corresponds to an effective velocity-changing collision cross section of {sigma}{sub VCC}{sup Cs,eff}=(1.2{+-}0.2)x10{sup -14} cm{sup 2}.« less

Cutting performance and microstructure of high speed steels: Contributions of matrix strengthening and undissolved carbides

While it is accepted that both the hot strength of the matrix and the amount of undissolved carbides are important for the cutting performance of high speed steels, the relative weights of their contributions are unknown. In this work, they are separately identified and a model is presented that provides a quantitative prediction of tool life (solely in uninterrupted cutting) on the basis of microstructural and compositional data over a wide range of alloy compositions and cutting speeds. The model seems to describe the individual contributions to tool life well enough to serve as a guide in alloy development. The model has been developed using 13 different steels, spanning the entire range of customary compositions. It is based on the following parameters: volume fractions and compositions of undissolved carbides; precipitates formed during tempering (secondary hardening) and during operation (tertiary precipitates); and, finally, residual solute in the matrix. Tool life is modeled as a linear combination of contributions from the undissolved carbides and from the precipitate population, including a contribution due to the action of Co, and with an additional term due to solute strengthening of the matrix. The weight factors are determined by multiple linear regression analysis. They reflect the relative importance of each contributing factor, and their dependence on cutting speed can be interpreted in terms of the change in operative wear mechanism with tool temperature.