Density Functional Model Research Articles

Quinoline derivatives as corrosion inhibitors for mild steel in acid medium: Deeper insights from experimental, ab initio density functional theory modeling, and in silico ecotoxicity investigations

Two (E)-2-(((2-((7-chloroquinolin-4-yl)amino)ethyl)imino)methyl)-4-R-phenol Schiff bases derivatives CMN (R = NO2) and CMP (R = H) were successfully prepared. Then, CMN and CMP were evaluated as corrosion inhibitors for 1020 mild steel in 1.0 mol L–1 HCl using different methods. Gravimetric experiments have shown that CMN and CMP reach efficiencies of 80 and 91% at 1.45 mmol L–1. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) measurements have indicated the formation of a protective adsorbed film on the mild steel surface. Electrochemical Impedance Spectroscopy (EIS), Linear Polarization Resistance (LPR), Potentiodynamic Polarization (PP), and Electrochemical Frequency Modulation (EFM) have also shed some light on electrochemical behavior of compounds. These data have depicted better polarization resistance and lower corrosion current density in the presence of both organic molecules, also proving that the process is controlled by charge transfer and that CMN and CMP are mixed-type corrosion inhibitors. ab initio Density Functional Theory (DFT) investigated how two quinoline-based molecules (CMN and CMP) prevent corrosion on iron surfaces. Simulations revealed CMP bonds more strongly to iron than CMN, leading to better protection. Simulations also showed that these inhibitor molecules form a protective layer on the iron, slowing down the movement of corrosive substances. Environment risk assessment through QSAR-based models have indicated that the corrosion inhibitors do not have the potential to bioaccumulate in fish. Still, the acute and chronic toxicity values at the different trophic levels investigated call for attention for environmentally friendly structural optimization.

Density Functional Theory-Fed Phase Field Model for Semiconductor Nanostructures: The Case of Self-Induced Core–Shell InAlN Nanorods

Density Functional Theory-Fed Phase Field Model for Semiconductor Nanostructures: The Case of Self-Induced Core–Shell InAlN Nanorods

Inclusive quasielastic neutrino-nucleus scattering with energy density functional nuclear models* *Supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2018R1A5A1025563, 2023R1A2C1003177, IBS-R031-D1)

In this study, we calculated the inclusive charged-current neutrino-nucleus scattering from 40Ar in the quasielastic region. To explore the effect of uncertainties stemming from the nuclear structure, we used the KIDS (Korea-IBS-Daegu-SKKU) nuclear energy density functional and Skyrme force models, namely SLy4, SkI3, and MSk7. These models were selected to have distinct behavior in terms of the density dependence of the symmetry energy and the effective mass of the nucleon. In the charged-current neutrino scattering, the single- and double-differential cross sections were calculated for various kinematics. Total cross sections are reported as a function of the incident neutrino energy. The theoretical cross sections were compared with experimental data, and the roles of the effective mass and symmetry energy were investigated in terms of charged-current neutrino-nucleus scattering.

Thin Film Resistance Gating by Redox Charge Exchange: Evidence for a Quantum Transition State.

Field effect transistors (FETs) and related devices have enabled tremendous advances in electronics, as well as studies of fundamental phenomena. FETs are classically actuated as fields charge/discharge materials, thereby modifying their resistance. Here, we develop charge exchange transistors (CETs) that comprise thin films whose resistance is modified by quantum charge exchange processes, e.g., redox and bonding. We first use CETs to probe the metallocene-thin film interaction during cyclic voltammetry. Remarkably, CETs reveal transient resistance peaks associated with charge transfer during both oxidation and reduction. Our data combined with kinetics and density functional theory modeling are consistent with a multistep redox pathway, including the formation/destruction of a quantum transition state that overlaps molecule + thin film band states. As a further proof-of-principle demonstration, we also use CETs to monitor n-alkanethiol self-assembly on thin Au films in real-time. CETs exhibit monotonic resistance increase consistent with previously reported fast-then-slow kinetics attributed to thiol-thin film bond formation (charge localization) and etching and/or molecule reorganization.

Density functional model of threshold voltage shifts at High-K/Metal gates

A density functional analysis of oxide dipole layers used to set the threshold voltages in high-K/metal CMOS gate stacks is given in terms of the band alignments and chemical trends of these component oxide layers. The oxides SrO, La2O3, HfO2 and Al2O3 are found to have similar band gaps and form a ‘staircase’ of band alignments, allowing them to shift the metal electrode Fermi level in both n-type and p-type directions. This analysis supersedes previous largely empirical models based on metal oxide ion densities or electronegativity scales.

Graphics processing unit/artificial neural network-accelerated large-eddy simulation of swirling premixed flames

Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially, particularly for those combustion models involving direct coupling of chemistry and flow such as the transported probability density function model. In the current study, an integrated graphics processing unit-artificial neural network (GPU-ANN) framework is introduced to comply with heavy computational costs while maintaining high fidelity. Within this framework, a GPU-based solver is employed to solve partial differential equations and compute thermal and transport properties, and an ANN is utilized to replace the calculation of reaction rates. Large eddy simulations of two swirling flames provide a robust validation, affirming and extending the GPU-ANN approach's applicability to challenging scenarios. The simulation results demonstrate a strong correlation in the macro flame structure and statistical characteristics between the GPU-ANN approach and the traditional central processing unit (CPU)-based solver with DI. This comparison indicates that the GPU-ANN approach is capable of attaining the same degree of precision as the conventional CPU-DI solver, even in more complex scenarios. In addition, the overall speed-up factor for the GPU-ANN approach is over two orders of magnitude. This study establishes the potential groundwork for widespread application of the proposed GPU-ANN approach in combustion simulations, addressing various and complex scenarios based on detailed chemistry, while significantly reducing computational costs.

Impact of Peripheral Hydrogen Bond on Electronic Properties of the Primary Acceptor Chlorophyll in the Reaction Center of Photosystem I.

Photosystem I (PS I) is a photosynthetic pigment-protein complex that absorbs light and uses the absorbed energy to initiate electron transfer. Electron transfer has been shown to occur concurrently along two (A- and B-) branches of reaction center (RC) cofactors. The electron transfer chain originates from a special pair of chlorophyll a molecules (P700), followed by two chlorophylls and one phylloquinone in each branch (denoted as A-1, A0, A1, respectively), converging in a single iron-sulfur complex Fx. While there is a consensus that the ultimate electron donor-acceptor pair is P700+A0-, the involvement of A-1 in electron transfer, as well as the mechanism of the very first step in the charge separation sequence, has been under debate. To resolve this question, multiple groups have targeted electron transfer cofactors by site-directed mutations. In this work, the peripheral hydrogen bonds to keto groups of A0 chlorophylls have been disrupted by mutagenesis. Four mutants were generated: PsaA-Y692F; PsaB-Y667F; PsaB-Y667A; and a double mutant PsaA-Y692F/PsaB-Y667F. Contrary to expectations, but in agreement with density functional theory modeling, the removal of the hydrogen bond by Tyr → Phe substitution was found to have a negligible effect on redox potentials and optical absorption spectra of respective chlorophylls. In contrast, Tyr → Ala substitution was shown to have a fatal effect on the PS I function. It is thus inferred that PsaA-Y692 and PsaB-Y667 residues have primarily structural significance, and their ability to coordinate respective chlorophylls in electron transfer via hydrogen bond plays a minor role.

Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels.

To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance in benchmark problems.

Statistical analysis of magnetic divertor configuration influence on H-mode transitions

DIII-D plasmas are compared for two upper divertor configurations: with the outer strike point on the small angle slot (SAS) divertor target and with the outer strike point on the horizontal divertor target (HT). Scanning the vertical distance between the magnetic null point and the divertor target over a range 0.10–0.16 m is shown to increase the threshold power, Pth , and edge plasma power, PLoss , for the low-to-high confinement (L–H) and H–L transitions respectively, by up to a factor of 1.4. The X-point height scans were performed at three L-mode core plasma line average electron densities, n¯e= 1.2, 2.2 and 3.6 ×1019m−3 , to investigate the density dependence of divertor magnetic configuration influence on Pth . The X-point height, Zx-pt , was further extended across the range 0.16–0.22 m with the more open HT divertor configuration, for which a clear decrease in Pth with increasing Zx-pt is observed. The dependence of Pth on divertor magnetic geometry is further investigated using a time-dependent probability density function (PDF) model and information geometry to elucidate the roles played by pedestal plasma turbulence and perpendicular velocity flows. The degree of stochasticity of the plasma turbulence is observed to be sensitive to the plasma heating rate. The calculated square of the information rate shows changes in the relative density fluctuations and perpendicular velocity PDFs begin 2–5 ms prior to the L–H transition for three plasmas; providing a crucial measurement of the dynamic timescale of external transport barrier formation. Additionally, both information length and rate provide potential predictors of the L–H transition for these plasmas.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties.

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Abstract 4165: Influence of halogens in squaraine dye on optoacoustic signal

Abstract Optoacoustic imaging has grown in clinical relevance due to inherent advantages in sensitivity, resolution, and contrast, compared to standard modalities, such as fluorescence. However, guiding principles for development of small molecule contrast agents specific for optoacoustic imaging is lacking. This study assesses the influence of specific structural features of small molecule dyes, specifically squaraine dyes, that result in optoacoustic activity. Through computational models, in vitro testing, and in vivo experimentation this study assesses the effects of halogens on small molecule squaraine dyes to increase optoacoustic signal. By using the aid of our computational model, we decorated the squaraine scaffold with heavy halogenation of squaraines increased optoacoustic activity (2.12 a.u. vs. 0.21 a.u., p<0.001). Density functional theory models suggest the origin of increased optoacoustic signal is due to increases in transition dipole moment which manifested experimentally as increased absorbance in the NIR wavelength range and decreased fluorescence quantum yield, respectively. This study provides an insight into the structure-function relationships that lead to guiding principles for designing optoacoustic-specific contrast agents. Further developments of squaraines and other types of optoacoustic agents will further increase relevance of optoacoustic imaging in a clinical setting. Citation Format: Kavita Belligund, William MacCuaig, Carly Wickizer, Yihan Shao, Lacey R. McNally. Influence of halogens in squaraine dye on optoacoustic signal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4165.

Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

Universal responses in nonmagnetic polar metals

We demonstrate that two phenomena, the kinetic magnetoelectric effect and the nonlinear Hall effect, are universal to polar metals as a consequence of their coexisting and contraindicated polarization and metallicity. We show that measurement of the effects provides a complete characterization of the nature of the polar metal, in that the nonzero response components indicate the direction of the polar axis, and the coefficients change signs on polarization reversal and become zero in the nonpolar phase. We illustrate our findings for the case of electron-doped PbTiO3 using a combination of density functional theory and model Hamiltonian-based calculations. Our model Hamiltonian analysis provides crucial insight into the microscopic origin of the effects, showing that they originate from inversion-symmetry-breaking-induced interorbital hoppings that correlate to an asymmetric charge density quantified by odd-parity charge multipoles. Our paper both heightens the relevance of the kinetic magnetoelectric and nonlinear Hall effects, and broadens the platform for investigating and detecting odd-parity charge multipoles in polar metals. Published by the American Physical Society 2024

Palladium Metal Nanocomposites Based on PEI-Functionalized Nitrogen-Doped Graphene Quantum Dots: Synthesis, Characterization, Density Functional Theory Modeling, and Cell Cycle Arrest Effects on Human Ovarian Cancer Cells.

In this study, the synthesis, characterization, density functional theory calculations (DFT), and effect of polyethylenimine (PEI)-functionalized nitrogen-doped graphene quantum dots (PEI N-GQDs) and their palladium metal nanoparticles nanocomposites (PdNPs/PEI N-GQDs) on cancer cells were extensively investigated. The focus also includes investigating their cytotoxic and apoptotic effects on ovarian cancer cells, which pose a serious risk to women's health and have high death rates from delayed diagnosis, inadequate response to treatment, and decreased survival. Graphene quantum dots and their palladium nanocomposites were differentially effective against ovarian cancer cell lines. In particular, the smaller particle size and morphology of PdNPs/PEI N-GQDs nanocomposites compared with PEI N-GQDs probably enhance their activity through highly improved uptake by cells. These findings emphasize the importance of particle size in composite drugs for efficient cancer treatment. DFT results revealed that the Pd-containing nanocomposite, with a smaller highest occupied molecular orbital-lowest unoccupied molecular orbital gap, exhibited higher reactivity and anticancer effects in human ovarian cancer cell line, OVCAR-3. Significantly, the application of nanocomposites to ovarian cancer cells initiated apoptosis, offering valuable insights into the intricate interplay between nanomaterials and cancer biology.

PKa as a Predictive Descriptor for Electrochemical Anion Adsorption

AbstractThe adsorption of anions onto metal surfaces is important in many applications including effective (electro)catalyst design, metal surface modification, and contaminant removal in wastewater treatment. In electrocatalysis, anions can be both reactive intermediates or site‐blocking spectators, where their adsorption strength therefore dictates the rate of reaction. In this work, we have measured the adsorption energy of a series of carboxylic acids on a Pt (111) single‐crystal electrode surface from aqueous solution. We find that the adsorption strength of the carboxylate anion is linearly correlated with its acid‐dissociation constant (pKa) and therefore the heterolytic O−H bond dissociation strength in solution. Using density functional theory modeling, we split the anion adsorption energy into a sum of the adsorption energy and electron affinity of a neutral (carboxyl) radical. Surprisingly, the adsorption energy of the carboxyl radicals are similar and therefore the large difference in electron affinity is what dictates anion adsorption strength; the greater the cost in energy to remove the electron from the anion upon adsorption, the weaker its binding. Therefore, at least within a class of anions with similar structure and surface binding atoms, both electron affinity and acidity are predictive descriptors of adsorption strength.

PKa as a Predictive Descriptor for Electrochemical Anion Adsorption.

The adsorption of anions onto metal surfaces is important in many applications including effective (electro)catalyst design, metal surface modification, and contaminant removal in wastewater treatment. In electrocatalysis, anions can be both reactive intermediates or site-blocking spectators, where their adsorption strength therefore dictates the rate of reaction. In this work, we have measured the adsorption energy of a series of carboxylic acids on a Pt (111) single-crystal electrode surface from aqueous solution. We find that the adsorption strength of the carboxylate anion is linearly correlated with its acid-dissociation constant (pKa) and therefore the heterolytic O-H bond dissociation strength in solution. Using density functional theory modeling, we split the anion adsorption energy into a sum of the adsorption energy and electron affinity of a neutral (carboxyl) radical. Surprisingly, the adsorption energy of the carboxyl radicals are similar and therefore the large difference in electron affinity is what dictates anion adsorption strength; the greater the cost in energy to remove the electron from the anion upon adsorption, the weaker its binding. Therefore, at least within a class of anions with similar structure and surface binding atoms, both electron affinity and acidity are predictive descriptors of adsorption strength.

Improving paragraph segmentation using BERT with additional information from probability density function modeling of segmentation distances

Paragraphs play a key role in writing and reading texts. Therefore, studies about dividing texts into appropriate paragraphs, or paragraph segmentation have gathered academic attention for a long time. Recent advancements in pre-trained language models have achieved state-of-the-art performances in various natural language processing fields, including paragraph segmentation. However, pre-trained language model based paragraph segmentation methods had a problem in that they could not consider statistical metadata such as how far each paragraph segmentation point should be apart from each other. Therefore we focused on combining paragraph segmentation distance and pre-trained language models so that both statistical metadata and state-of-the-art representation ability could be considered at the same time. We propose a novel model by modifying BERT, a state-of-the-art pre-trained language model, by adding segmentation distance information via probability density function modeling. Our model was trained and tested on the domain of the novel, and showed improved performance compared to baseline BERT and previous study, acquiring a mean of 0.8877 F1-score and 0.8708 MCC. Furthermore, our model showed robust performance regardless of the authors of the novels.

Carrier-induced formation of electrically active boron-interstitial clusters in irradiated boron-doped silicon

Excess minority carriers create boron-related recombination centers that degrade the efficiency of the non-particle-irradiated silicon solar cells. However, the carrier-induced reactions among the radiation-induced defects are poorly understood for devices exposed to particle radiation. This study investigates the structure, electronic properties, formation and annihilation mechanisms, and diffusion dynamics of the carrier-induced defects in particle-irradiated boron-doped silicon using density-functional modeling and junction spectroscopy. By revisiting the ground-state structures of the boron-di-interstitial clusters (BI2), we find that the calculated acceptor and donor levels of such defects agree well quantitatively with the carrier-induced deep-level transient spectroscopy (DLTS) hole emission signatures at 0.43 and 0.53 eV above the valence band edge (Ev), respectively. We also find that the formation of BI2 is thermally activated by an energy of 0.50 eV, which we explain theoretically by the reduction of the migration barrier of mono-interstitials to 0.53 eV in the presence of excess minority carriers. Moreover, we discover that the BI2 are potentially mobile with a migration barrier of 1.18 eV, contrary to the present understanding.

Revealing ultrafast phonon mediated inter-valley scattering through transient absorption and high harmonic spectroscopies

Processes involving ultrafast laser driven electron-phonon dynamics play a fundamental role in the response of quantum systems in a growing number of situations of interest, as evinced by phenomena such as strongly driven phase transitions and light driven engineering of material properties. To show how these processes can be captured from a computational perspective, we simulate the transient absorption spectra and high-harmonic generation signals associated with valley selective excitation and intraband charge-carrier relaxation in monolayer hexagonal boron nitride. We show that the multitrajectory Ehrenfest dynamics approach, implemented in combination with real-time time-dependent density-functional theory and tight-binding models, offers a simple, accurate, and efficient method to study ultrafast electron-phonon coupled phenomena in solids under diverse pump-probe regimes which can be easily incorporated into the majority of real-time software packages. Published by the American Physical Society 2024

Significant Oxygen Underestimation When Quantifying Barium-Doped SrTiO Layers by Atom Probe Tomography.

In this paper, the capability for quantifying the composition of Ba-doped SrTiO layers from an atom probe measurement was explored. Rutherford backscattering spectrometry and time-of-flight/energy elastic recoil detection were used to benchmark the composition where the amount of titanium was intentionally varied between samples. The atom probe results showed a significant divergence from the benchmarked composition. The cause was shown to be a significant oxygen underestimation (≳14 at%). The ratio between oxygen and titanium for the samples varied between 2.6 and 12.7, while those measured by atom probe tomography were lower and covered a narrower range between 1.4 and 1.7. This difference was found to be associated with the oxygen and titanium predominantly field evaporating together as a molecular ion. The evaporation fields and bonding chemistries determined showed inconsistencies for explaining the oxygen underestimation and ion species measured. The measured ion charge state was in excellent agreement with that predicted by the Kingham postionization theory. Only by considering the measured ion species, their evaporation fields, the coordination chemistry, the analysis conditions, and some recently reported density functional theory modeling for oxide field emission were we able to postulate a field emission and oxygen neutral desorption process that may explain our results.