Density Functional Model Research Articles

Diagnosing the Electrostatic Shielding Mechanism for Dendrite Suppression in Aqueous Zinc Batteries.

Aqueous zinc electrolytes offer the potential for cheaper rechargeable batteries due to their safe compatibility with the high capacity metal anode; yet, they are stymied by irregular zinc deposition and consequent dendrite growth. Suppressing dendrite formation by tailoring the electrolyte is a proven approach from lithium batteries; yet, the underlying mechanistic understanding that guides such tailoring does not necessarily directly translate from one system to the other. Here, it is shown that the electrostatic shielding mechanism, a fundamental concept in electrolyte engineering for stable metal anodes, has different consequences for the plating morphology in aqueous zinc batteries. Operando electrochemical transmission electron microscopy is used to directly observe the zinc nucleation and growth under different electrolyte compositions and reveal that electrostatic shielding additive suppresses dendrites by inhibiting secondary zinc nucleation along the (100) edges of existing primary deposits and encouraging preferential deposition on the (002) faces, leading to a dense and block-like zinc morphology. The strong influence of the crystallography of Zn on the electrostatic shielding mechanism is further confirmed with Zn||Ti cells and density functional theory modeling. This work demonstrates the importance of considering the unique aspects of the aqueous zinc battery system when using concepts from other battery chemistries.

Adsorption, Single‐Molecule Manipulation, and Self‐Assembly of Borazine on Ag(111)

AbstractThe interaction of borazine with metal supports and the concomitant surface chemistry play important roles in the synthesis of hexagonal boron nitride and the assembly of BN‐doped carbon nanostructures, thus making adsorbed borazine an intriguing model system. Herein, the first real space characterization of individual borazine molecules and highly ordered borazine self‐assemblies on solid supports, combining scanning tunneling microscopy (STM), scanning tunneling spectroscopy, X‐ray photoelectron spectroscopy, and complementary density functional theory modeling is reported. Specifically, a weak, nondissociative adsorption of borazine with the ring aligned in parallel to the surface plane is observed on Ag(111) upon low temperature deposition. Borazine is found to favor hollow adsorption sites, which guide the assembly of intricate borazine assemblies including a porous, chiral honeycomb‐like network, and dense‐packed monolayer films. Additionally, a modification of the borazine adsorption configuration by STM‐based manipulation is demonstrated. Dehydrogenation of individual molecules by voltages pulses yields an upright standing borazine fragment bound via B to Ag. This study thus provides a comprehensive, single‐molecule level characterization of borazine adsorption and surface chemistry on a characteristic coinage metal support and may serve as a reference for advanced low‐dimensional materials based on functionalized borazines or including BN units as dopants.

Fabrication of zwitterionic magnetic microporous organic network for efficient extraction of fluoroquinolone antibiotics from meat samples.

A zwitterionic magnetic microporous organic network (MMON-SO3H-NH2) with numerous amino and sulfonic acid ion-pare binding sites was designed and synthesized for efficient magnetic solid-phase extraction (MSPE) of fluoroquinolones (FQs) from meat samples. The core-shell MMON-SO3H-NH2 offered large specific surface area, rapid magnetic responsiveness, good stability, and multiple binding sites for FQs. The density functional theory and independent gradient model evaluations confirmed hydrogen bonding, π-π and ion-pair interactions between MMON-SO3H-NH2 and FQs. Under the optimal conditions, the established MMON-SO3H-NH2-MSPE-HPLC-UV method gave wide linear range (0.15-1000μg L-1), low limits of detection (0.05-4.5μg L-1) and limits of quantitation (0.15-13μg L-1), and high enrichment factors (82.1-99.6) using 3mg of adsorbent. This work demonstrates that the preparation of zwitterionic MONs is an efficient way to promote the extraction performance of MONs for zwitterionic targets and provides an effective sample pretreatment method for enriching and monitoring FQs in complex food matrices.

Critical Sample-Size Analysis for Uncertainty Aerodynamic Evaluation of Compressor Blades with Stagger-Angle Errors

Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the amounts of data collected as to compressor blades of aero-engines are mostly limited due to the expensive and time-consuming tests. In this paper, we develop a preconditioner-based data-driven polynomial chaos (PDDPC) method that can efficiently deal with uncertainty propagation of limited amounts of sampled data. The calculation accuracy of a PDDPC method is closely related to the sample size of collected data. Therefore, the influence of sample size on this PDDPC method is investigated using a nonlinear test function. Subsequently, we consider the real manufacturing errors in stagger angles for compressor blades. Under three different operating conditions, the PDDPC method is applied to investigate the effect of stagger-angle error on UQ results of multiple aerodynamic parameters of a two-dimensional compressor blade. The results show that as the sample-size of measured data increases, UQ results regarding aerodynamic performance obtained by the PDDPC method gradually converge. There exists a critical sample size that ensures accurate UQ analysis of compressor blades. The probability information contained in the machining error data is analyzed through Kullback–Leibler divergence, and the critical sample size is determined. The research results can serve as a valuable reference for the fast and cheap UQ analysis of compressor blades in practical engineering.

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.

Defect-driven nanostructuring of low-nuclearity Pt-Mo ensembles for continuous gas-phase formic acid dehydrogenation

Supported metal clusters comprising of well-tailored low-nuclearity heteroatoms have great potentials in catalysis owing to the maximized exposure of active sites and metal synergy. However, atomically precise design of these architectures is still challenging for the lack of practical approaches. Here, we report a defect-driven nanostructuring strategy through combining defect engineering of nitrogen-doped carbons and sequential metal depositions to prepare a series of Pt and Mo ensembles ranging from single atoms to sub-nanoclusters. When applied in continuous gas-phase decomposition of formic acid, the low-nuclearity ensembles with unique Pt3Mo1N3 configuration deliver high-purity hydrogen at full conversion with unexpected high activity of 0.62 molHCOOH molPt−1 s−1 and remarkable stability, significantly outperforming the previously reported catalysts. The remarkable performance is rationalized by a joint operando dual-beam Fourier transformed infrared spectroscopy and density functional theory modeling study, pointing to the Pt-Mo synergy in creating a new reaction path for consecutive HCOOH dissociations.

Reveal the main factors and adsorption behavior influencing the adsorption of pollutants on natural mineral adsorbents: Based on machine learning modeling and DFT calculation

Montmorillonite, as a natural mineral adsorption material that has high research value in water pollution treatment. However, the adsorption capacity varies depending on the type of pollutant and the properties of the montmorillonite material, and the factors controlling adsorption are not yet clear. Herein, we investigated the adsorption behavior of pollutants on montmorillonite materials using density functional theory (DFT) calculations and machine learning modeling. Furthermore, it explores the main factors influencing their adsorption. The machine learning results indicate that the gradient boosting decision tree (GBDT) model exhibits a better fit to the experimental data compared to the other five machine learning models (R2 = 0.79). The higher pH levels and larger relative molecular mass of pollutants have a positive impact on montmorillonite adsorption. However, an increase in the proportion of oxygen atoms in the adsorbent material and longer hydrothermal preparation time show a trend of initially positive and then negative effects on the predicted results. The influence of pH on the adsorption capacity of montmorillonite adsorbents was further analyzed using density functional theory (DFT). Density functional theory (DFT) studies reveal that montmorillonite primarily removes protonated sulfamethoxazole (SMZ) through hydrogen bonding (N-H…O) interactions, accompanied by van der waals (O-O) and ionic bond (C-O…Al) forces under different pH conditions. The partial density of states (PDOS) reveals that the LUMO orbital of montmorillonite has a higher electron accepting ability than the HOMO orbital of SMZ (p orbital peak is greater than the S orbital) in the actual electron transfer process. This study provided some support for the investigation of montmorillonite-based adsorbent materials through the combination of machine learning and theoretical calculations.

Study on deformation theory and resonance frequency of circular-plane PVC actuator

The polyvinyl chloride (PVC) gel actuator has the advantages of low mass density, large actuation strain, good compliance, and simple fabrication. It deforms when excited by a voltage, and the deformation amplitude of the actuator is higher when an alternating current voltage of a specific frequency causes the actuator to resonate. In this paper, a circular-plane actuator based on PVC gel film and flexible electrodes was developed. Then, a theoretical model is established based on the Yeoh strain energy density function model and the mechanical analysis of deformation region-boundary constraints, the key parameters affecting the deformation are obtained. Subsequently, finite element simulation tests were done on the basis of this model, and the values of uniaxial stretching were highly consistent with the previous actual experimental results, which verified the reasonableness of the model and simulation method. Then, we focus on the analysis of the resonance frequencies of PVC actuators with different plasticizer content, electrode diameter, and pre-stretching degree. Finally, we compare the differences in time-domain characteristics of PVC actuators in the resonant and non-resonant states.

Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms.

The present study is dedicated to the problem of electrochemical analysis of multicomponent mixtures, such as milk. A combination of cyclic voltammetry facilities and machine learning techniques made it possible to create a pattern recognition system for the detection of antibiotic residues in skimmed milk. A multielectrode sensor including copper, nickel, and carbon fiber was fabricated for the collection of electrochemical data. Processes occurring at the electrode surface were discussed and simulated with the help of molecular docking and density functional theory modeling. It was assumed that the antibiotic fingerprint reveals a potential drift of electrodes, owing to complexation with metal ions present in milk. The gradient boosting algorithm showed the best efficiency in training the machine learning model. High accuracy was achieved for the recognition of antibiotics in milk. The elaborated method may be incorporated into existing milking systems at dairy farms for monitoring the residue concentrations of antibiotics.

Analysis of wall-modelled particle/mesh PDF methods for turbulent parietal flows

Abstract Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh formulations of the probability density function (PDF) model. This is translated into three objectives. The first objective is to assess the existing an-elastic wall-boundary condition and present new validation results. The second objective is to analyse the impact of the interpolation of the mean fields at particle positions on their dynamics. The third objective is to investigate the spatial error affecting covariance estimators when they are extracted on coarse volumes. All these developments allow to ascertain that the key dynamical statistics of wall-bounded flows are properly captured even for coarse spatial resolutions.

A machine learning-assisted study on organic solvents in electrolytes for expanding the electrochemical stable window of zinc-ion batteries

Currently, inherent deficiencies of water-based electrolytes, such as a narrow electrochemical stable window (ESW), lead to low operating voltage and insufficient energy density of zinc-ion batteries (ZIBs). Incorporating organic electrolytes into ZIBs is an effective strategy for expanding the ESW but the exploration on introducing organic solvent into zinc electrolyte is still scarce. In this work, the ESWs of 307 organic solvents in ZIBs were investigated assisted by machine learning (ML) methods. Four ML models were employed to predict the oxidation potentials (OPs) of organic solvents for zinc electrolytes. Among them, Gradient Boosting Regression (GBR) and Gaussian Process Regression (GPR) exhibit exceptional performance and achieve remarkable prediction results. Specifically, GBR model displays a highest R2 score of 0.905, an absolute error of 0.258 and an absolute percentage error of 8.30% on test set. The effect of selected features on the prediction results was investigated and the features with significant impact on the prediction of OP were summarized. ESWs (OPs) of six non-aqueous zinc electrolytes using three distinct organic solvents were measured by experimental methods and there is a notable agreement between measured ESW (OP) and the solvent OP computed by Density Functional Theory and ML models in general. Furthermore, Zn//Zn symmetrical batteries assembled with these electrolytes demonstrate remarkable cycling stability, showcasing their potential applications in ZIBs. This work develops ML models that can efficiently predict a large number of organic solvent OP for ZIBs, and provides a useful guidance for developing advanced non-aqueous and hybrid zinc electrolytes.

A Route to Understanding the Ethane Adsorption Selectivity of the Zeolitic Imidazolate Framework-8 in Ethane-Ethylene Mixtures.

Ethylene production has a negative environmental impact, with its separation step being one of the major contributors of pollution. This has encouraged the search for energy-efficient alternatives, among which the adsorptive separation of ethane and ethylene stands out. ZIF-8 is a molecular sieve that is potentially useful for this purpose. It is selective to ethane, an exceptional property that remains unexplained. Furthermore, the adsorption of ethane and ethylene above room temperature, such as at steam cracking process outlet temperatures, has not been addressed either. This work aims to fill this knowledge gap by combining experiments at very low volumetric fillings with density-functional theory modelling methods. Adsorption isotherms of ethane and ethylene on ZIF-8 at pressures below 0.3 bar and 311 K, 333 K, and 363 K were measured using zero-length column chromatography. The low-pressure domain of the isotherms contains information on the interactions between the adsorbate molecules and the adsorbent. This favors the understanding of their macroscopic behavior from simulations at the atomic level. The isosteric enthalpy of adsorption of ethane remained constant at approximately -10 kJ/mol. In contrast, the isosteric enthalpy of adsorption of ethylene decreased from -4 kJ/mol to values akin to those of ethane as temperature increased. ZIF-8 selectivity to ethane, estimated from ideal adsorbed solution theory, decreased from 2.8 to 2.0 with increasing pressure up to 0.19 bar. Quantum mechanical modelling suggested that ethylene had minimal interactions with ZIF-8, while ethane formed hydrogen bonds with nitrogen atoms within its structure. The findings of this research are a platform for designing new systems for the adsorptive separation of ethane and ethylene and thus, reducing the environmental impact of ethylene production.

Confinement‐Induced Phonon Softening and Hardening in Sb2Te3 Thin Films

AbstractScaling effects in Sesqui‐chalcogenides are of major interest to understand and optimize their performance in heavily scaled applications, including topological insulators and phase‐change devices. A combined experimental and theoretical study is presented for molecular beam epitaxy‐grown films of antimony‐telluride (Sb2Te3). Structural,vibrational, optical, and bonding properties upon varying confinement are studied for thicknesses ranging from 1.3 to 56 nm. In ultrathin films, the low‐frequency coherent phonons of A1g1 symmetry are softened compared to the bulk (64.5 cm−1 at 1.3 nm compared to 68 cm−1 at 55.8 nm). A concomitant increase of the high‐frequency A1g2 Raman mode is seen. X‐ray diffraction analyses unravel an accompanying out of plane stretch by 5%, mainly stemming from an increase in the Te‐Te gap. This conclusion is supported by density functional theory slab models, which reveal a significant dependency of chemical bonding on film thickness. Changes in atomic arrangement, vibrational frequencies, and bonding extend over a thickness range much larger than observed for other material classes. The finding of these unexpectedly pronounced thickness‐dependent effects in quasi‐2D material Sb2Te3 allows tuning of the film properties with thickness. The results are discussed in the context of a novel bond‐type, characterized by a competition between electron localization and delocalization.

Density functional and force field modeling of multi-walled WS2 nanotubes

A technique for constructing force fields based on the use of genetic algorithms is proposed. It is used to generate a force field designed to simulate multi-walled WS2 nanotubes, which is beyond the capabilities of ab initio methods. The resulting force field parameterization was successfully tested on single, double, and triple-walled nanotubes, which structure was determined using density functional calculations. The developed force field was applied to model the structure and stability of chiral and achiral multi-walled WS2 nanotubes. The interwall distances found by simulation are in good agreement with the results of recent measurements of these parameters for synthesized nanotubes. An analysis was made of the deviation of the shape of nanotubes from the cylindrical pattern.

Influence of the chemical kinetics on the prediction of turbulent non-premixed jet CH_4 flames

The present work focuses on the five different chemical mechanisms coupled with probability density function (PDF) model to represent the local extinction and re-ignition flame characteristics of the well-known Sandia Flames D–F. These five mechanisms span from the Foundational Fuel Chemistry Model (FFCM) mechanism involving 38 species to the Glarborg mechanism involving 150 species. The coupled computational fluid dynamics (CFD) and transported-PDF method are used for the turbulence modeling, and the reaction–diffusion manifolds (REDIMs) are used as an advanced technique for the simplification of chemical kinetics and to speed up the numerical computation. It is demonstrated that these chemical mechanisms have an ability to represent the degree of local extinction and re-ignition accurately. Furthermore, the sensitivity analysis shows that the degree of local extinction is very sensitive to only several key elementary reactions, and an analysis on the turbulence–chemistry interaction investigates the influence of these elementary reactions.

Practical approach to thermal conductivity calculations of small SiO2 samples

We compute the thermal conductivity of amorphous SiO2 combining density functional theory calculations and lifetime models. The supercell we use to represent amorphous SiO2 is small, 78 atoms. We show that this rudimentary model is nevertheless sufficient to reproduce the temperature dependence of the thermal conductivity from 0.1 K to 1000 K if we use a lifetime model at low frequencies accounting for Rayleigh scattering, the absorption of phonons by two levels systems and structural relaxations. The necessary conditions needed to observe the plateau in the thermal conductivity around 10 K are also discussed according to this model.

Photophysical Exploration of Two Isomers of Octaethyltrioxopyrrocorphin.

The introduction of three β-oxosubstituents to octaethylporphyrin by means of an oxidation/rearrangement reaction generates the trioxopyrrocorphin chromophore. Pyrrocorphins (hexahydroporphyrins) are generally nonaromatic, but we recently demonstrated trioxopyrrocorphins to possess considerable aromatic character. This contribution explores the photophysical characteristics of these unusual chromophores. In agreement with density functional theory modeling, the UV-vis and magnetic circular dichroism spectra of the two─out of the four possible─triketone regioisomers investigated conform to the Gouterman model of porphyrinoid optical spectra, in alignment with their aromaticity. Their excited-state dynamics shed further light on the degree to which β-oxo substitutions tune the photophysical properties of porphyrinoids. Introduction of β-oxo functionalities increases the rate and yield of intersystem crossing and shortens the triplet state lifetime. Unexpectedly, the singlet oxygen generation yield of both pyrrocorphins remains relatively high, with modes of distortion from planarity likely enhancing triplet energy transfer. This work thus expands our understanding of a rare class of porphyrinoids and further characterizes them as sustaining aromatic porphyrinic π-systems. Our findings suggest triple β-oxo substitution as a viable route toward the development of novel, high-singlet oxygen yield porphyrinic photosensitizers.

Atomic-scale imaging of ytterbium ions in lead halide perovskites.

Lanthanide-doped lead halide perovskites have demonstrated great potential for photoelectric applications. However, there is a long-standing controversy about the existence of lanthanide ions, e.g., whether the doping of Ln3+ is successful or not; the substituting sites of Ln3+ in lead halide perovskites are unclear. We directly identify the doped Yb3+ in CsPbCl3 perovskites by using the state-of-the-art transmission electron microscopy and three-dimensional atom probe tomography at atomic scale. Different from the previous assumptions and/or results, we evidence that Yb3+ simultaneously replace Pb2+ and occupy the lattice interstitial sites. Furthermore, we directly observe the cluster phenomenon of CsPbCl3 single crystal at near atomic scale. Density functional theory modeling further confirms and explains the mechanisms of our findings. Our findings thus provide an atomic-level understanding of the doping mechanism in perovskites and will stimulate a further thinking of the doping effect on the performance of perovskites.

Magneto-Plasmonic Nanoparticles Generated by Laser Ablation of Layered Fe/Au and Fe/Au/Fe Composite Films for SERS Application

Magneto-plasmonic nanoparticles were fabricated using a 1064 nm picosecond-pulsed laser for ablation of Fe/Au and Fe/Au/Fe composite thin films in acetone. Nanoparticles were characterized by electron microscopy, ultraviolet-visible (UV-VIS) absorption, and Raman spectroscopy. Hybrid nanoparticles were arranged on an aluminum substrate by a magnetic field for application in surface-enhanced Raman spectroscopy (SERS). Transmission electron microscopy and energy dispersive spectroscopy analysis revealed the spherical core-shell (Au-Fe) structure of nanoparticles. Raman spectroscopy of bare magneto-plasmonic nanoparticles confirmed the presence of magnetite (Fe3O4) without any impurities from maghemite or hematite. In addition, resonantly enhanced carbon-based bands were detected in Raman spectra. Plasmonic properties of hybrid nanoparticles were probed by SERS using the adsorbed biomolecule adenine. Based on analysis of experimental spectra and density functional theory modeling, the difference in SERS spectra of adsorbed adenine on laser-ablated Au and magneto-plasmonic nanoparticles was explained by the binding of adenine to the Fe3O4 structure at hybrid nanoparticles. The hybrid nanoparticles are free from organic stabilizers, and because of the biocompatibility of the magnetic shell and SERS activity of the plasmonic gold core, they can be widely applied in the construction of biosensors and biomedicine applications.

A Monolayer Carbon Nitride on Au(111) with a High Density of Single Co Sites.

Carbon nitrides that expose atomically dispersed single-atom metals in the form of M-N-C (M = metal) sites are attractive earth-abundant catalyst materials that have been demonstrated in electrocatalytic conversion reactions. The catalytic performance is determined by the abundance of N-doped sites and the type of metal coordination to N, but challenges remain to synthesize pristine carbon nitrides with a high concentration of the most active sites and prepare homogeneously doped materials that allow for in-depth characterization of the M-N-C sites and quantitative evaluation of their catalytic performance. Herein, we have synthesized and characterized a well-defined monolayer carbon nitride phase on a Au(111) surface that exposes an exceedingly high concentration of Co-N4 sites. The crystalline monolayer carbon nitride, whose formation is controlled by an on-surface reaction between Co atoms and melamine on Au(111), is characterized by a dense array of 4- and 6-fold N-terminated pockets, whereof only the 4-fold pocket is found to be holding Co atoms. Through detailed characterization using scanning tunneling microscopy, X-ray photoelectron spectroscopy, and density functional theory modeling, we determine the atomic structure and chemical state of the carbon nitride network. Furthermore, we show that the monolayer carbon nitride structure is stable and reactive toward the electrocatalytic oxygen reduction reaction in alkaline electrolyte, with a quantitative performance metric that significantly exceeds comparable M-N-C-based catalyst types. The work demonstrates that high-density active catalytic sites can be created using common precursor materials, and the formed networks themselves offer an excellent platform for onward studies addressing the characteristics of M-N-C sites.