DFT-based Model Research Articles

Passivator materials based on the benzothiazole-sulfonamide hybrid: Synthesis, optical, electrochemical properties, and molecular modeling for perovskite solar cells

This study presents the synthesis of N-(4-(N-carbamimidoylsulfamoyl)phenyl)benzo[d]thiazole-2-carbohydrazonoyl cyanide (NCSBC), a novel passivator material for perovskite solar cells (PSCs). The structure was confirmed by FTIR, ¹H NMR, and ¹³C NMR, while Hall-effect measurements revealed a hole mobility of 1.15 × 10³ cm²/Vs. UV–Vis analysis showed absorption peaks at 295 and 422 nm, corresponding to transitions related to azo groups and aromatic rings in NCSBC. The compound exhibited an optical energy gap of 2.47 eV, consistent with DFT-based molecular modeling. Thermal stability and electrochemical properties further validated its suitability for photovoltaic applications. Incorporating NCSBC into PSCs with an FTO/c-TiO2/MAPbI3/NCSBC/spiroMeOTAD/Ag device configuration resulted in a power conversion efficiency (PCE) of 16.21 %, compared to 14.47 % for the control. This work highlights NCSBC's potential for cost-effective, high-efficiency defect passivation in PSCs.

Descriptors for Electrochemical CO2 Reduction in Imidazolium-Based Electrolytes.

Electrochemical CO2 reduction (CO2R) allows us to close the carbon cycle and store intermittent renewable energy into chemical products. Among these, syngas, a mixture of hydrogen and carbon monoxide, is particularly valuable due to its high market share and the low energy required for its electrocatalytic production. In addition to catalyst optimization, lately, electrolyte modifications to achieve a suitable CO/H2 ratio have also been considered. Ionic liquid (IL)-based electrolytes have enabled high faradaic efficiency toward CO, depending on the chemical properties of the IL. In this work, we rationalized through density functional theory (DFT) descriptors the competition between hydrogen evolution (HER) and CO2R on silver in imidazolium-based electrolytes, developing a DFT-based analytical model. The electrolyte anion regulates the concentration ratio between cationic and carbene species of ILs cation, respectively, between the 1-ethyl-3-methylimidazolium cation (EMIM+) and carbene (EMIM:) species and between the 1-butyl-3-methylimidazolium cation (BMIM+) and carbene (BMIM:). The latter species, if formed, hinders the CO2R by blocking the active sites or trapping CO2 in solution. In the case of weak Lewis base anions as fluorinated ones, EMIM+ (BMIM+) cations, which serve as cocatalysts in CO2R, are more abundant, allowing high CO partial current densities and high electrochemically active surface area. Applying the here-defined descriptors to ILs not yet tested makes it possible to predict the HER and CO2R selectivity on silver, thus enabling guidelines for designing better ILs for CO2R.

Design of Cu-based bimetals for ammonia catalytic combustion via DFT-based microkinetic modeling

Copper and its oxides possess excellent performance for ammonia catalytic combustion, but further improving the catalytic performance is still impeded by the limited understanding of the reaction mechanism. Herein, a density functional theory-based microkinetic study by considering the adsorbate-adsorbate interactions was performed to uncover the reaction mechanism under experimental conditions and further design the superior Cu-based bimetal catalysts. Our calculations demonstrate that the partially oxidized Cu(111) covered by oxygen atoms is the active phase. The ammonia consumption rate is not only constrained by strong adsorption of NH and O species but also suppressed by elementary reactions of NH coupling and O-assisted NH3 dehydrogenation from 400 to 800 K. Subsequently, two Cu-based bimetal catalysts, CuAg3 and CuAu3, cautiously designed by weakening the adsorption of NH and O species and reducing the activation barriers of the two rate-determining steps, possess superior catalytic performance than the copper in ammonia catalytic combustion. Overall, our studies not only reveal the reaction mechanism of ammonia catalytic combustion on Cu(111) but also provide important insights for the design of optimal catalysts including two predicted Cu-based bimetal catalysts.

Machine Learning for the Expedited Screening of Hydrogen Evolution Catalysts for Transition Metal-Doped Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides (TMDs) have gained attention as potent catalysts for the hydrogen evolution reaction (HER). The traditional trial-and-error methodology for catalyst development has proven inefficient due to its costly and time-intensive nature. To accelerate the catalyst development process, the Gibbs free energy of hydrogen adsorption ( Δ G H ∗ ), computed using the density functional theory (DFT), is widely used as the paramount descriptor for evaluating and predicting HER catalyst performance. However, DFT calculations for Δ G H ∗ are time-consuming and thus pose a challenge for high-throughput screening. Herein, we devise a predictive model for Δ G H ∗ within transition metal-doped TMD systems using a machine learning (ML) framework. We calculate DFT Δ G H ∗ values for 150 TM-doped MX2 (CrS2, MoS2, WS2, MoSe2, and MoTe2) and apply various ML algorithms. We validate the universality of our model by constructing 15 new external test sets. The prediction results show a high correlation coefficient of R 2 = 0.92 . Based on feature analysis, the three most important parameters are the number of valence electrons of the doped transition metal, the distance of the valence electrons of the doped transition metal, and the electronegativity of the doped transition metal. Our DFT-based ML model provides a useful guideline for the material development process through Δ G H ∗ prediction and facilitates the efficient design of transition metal dichalcogenide catalysts that exhibit superior HER activity.

Direct glyphosate soil monitoring at the triazine-based covalent organic framework with the theoretical study of sensing principle

Covalent organic frameworks (COFs) are emerging as promising sensing materials due to their controllable structure and function properties, as well as excellent physicochemical characteristics. Here, specific interactions between a triazine-based COF and a mass-used herbicide – glyphosate (GLY) have been utilized to design a disposable sensing platform for GLY detection. This herbicide has been extensively used for decades, however, its harmful environmental impact and toxicity to humans have been recently proven, conditioning the necessity for the strict control and monitoring of its use and its presence in soil, water, and food. Glyphosate is an organophosphorus compound, and its detection in complex matrices usually requires laborious pretreatment. Here, we developed a direct, miniaturized, robust, and green approach for disposable electrochemical sensing of glyphosate, utilizing COF's ability to selectively capture and concentrate negatively charged glyphosate molecules inside its nanopores. This process generates the concentration gradient of GLY, accelerating its diffusion towards the electrode surface. Simultaneously, specific COF-glyphosate binding catalyses the oxidative cleavage of the C–P bond and, together with pore nanoconfinement, enables sensitive glyphosate detection. Detailed sensing principles and selectiveness were scrutinized using DFT-based modelling. The proposed electrochemical method has a linear working range from 0.1 μM to 10 μM, a low limit of detection of 96 nM, and a limit of quantification of 320 nM. The elaborated sensing approach is viable for use in real sample matrices and tested for GLY determination in soil and water samples, without pretreatment, preparation, or purification. The results showed the practical usefulness of the sensor in the real sample analysis and suggested its suitability for possible out-of-laboratory sensing.

Extended Conductor-like Polarizable Continuum Solvation Model (CPCM-X) for Semiempirical Methods.

We have developed a new method to accurately account for solvation effects in semiempirical quantum mechanics based on a polarizable continuum model (PCM). The extended conductor-like polarizable continuum model (CPCM-X) incorporates a computationally efficient domain decomposition conductor-like screening model (ddCOSMO) for extended tight binding (xTB) methods and uses a post-processing approach based on established solvation models, like the conductor-like screening model for real solvents (COSMO-RS) and the universal solvent model based on solute electron density (SMD). According to various benchmarks, the approach performs well across a broad range of systems and applications, including hydration free energies, non-aqueous solvation free energies, and large supramolecular association reactions of neutral and charged species. Our method for computing solvation free energies is much more accurate than the current methods in the xtb program package. It improves the accuracy of solvation free energies by up to 40% for larger supramolecular association reactions to match even the accuracy of higher-level DFT-based solvation models like COSMO-RS and SMD while being computationally more than 2 orders of magnitude faster. The proposed method and the underlying ddCOSMO model are readily available for a wide variety of solvents and are accessible in xtb for use in various computational applications.

Unveiling the fates of nitro-transformation products in advanced oxidation process: A DFT-based kinetic model

Kinetic models are widely employed to investigate the reaction mechanisms and estimate kinetic parameters of advanced oxidation processes (AOPs), which are critical for assessing toxicity variations in these processes. However, conventional models often oversimplify the kinetics related to transformation products (TPs) due to the complex reactions involved in AOPs. Consequently, predicting the concentration profiles of TPs in conventional kinetic models is challenging. In this study, we aimed to overcome this challenge by developing a novel kinetic model that enriches the TPs-related elementary reactions in AOPs. Specifically, we mapped the entire reaction pathways from parent organic compounds through various intermediates to their TPs using density functional theory (DFT). We investigated the formation of nitro-TPs of phenolics, such as salicylic acid, hydroxybenzoic acid, phenol and 4-chlorophenol in sulfated radical-based AOPs in the presence of NO2−. The predicted concentrations of nitro-TPs obtained from our model exhibited a close match with those reported in previous research, thus confirming the accuracy of our predictions. Our research highlights the effectiveness of combining DFT and kinetic modeling, which facilitates a streamlined understanding of the mechanisms governing TP formation and decay in AOPs. This approach holds the potential to advance our comprehension of these complex reactions and may be beneficial in future studies of similar reactions.

Density-Functional Study of the Si/SiO2 Interfaces in Short-Period Superlattices: Vibrational States and Raman Spectra

Raman spectroscopy has proven its effectiveness as a highly informative and sensitive method for the nondestructive analysis of layered nanostructures and their interfaces. However, there is a lack of information concerning the characteristic phonon modes and their activity in Si/SiO2 nanostructures. In order to overcome this problem, the phonon states and Raman spectra of several Si/SiO2 superlattices (SL) with layer thicknesses varied within 0.5–2 nm are studied using DFT-based computer modeling. Two types of structures with different interfaces between crystalline silicon and SiO2 cristobalite were studied. A relationship between the phonon states of heterosystems and the phonon modes of the initial crystals was established. Estimates of the parameters of deformation potentials are obtained, with the help of which the shifts of phonon frequencies caused by elastic strains in the materials of the SL layers are interpreted. The dependence of intense Raman lines on the SL structure has been studied. Several ways have been proposed to use this information, both for identifying the type of interface and for estimating the structural parameters. The obtained information will be useful for the spectroscopic characterization of the silicon/oxide interfaces.

The central role of density functional theory in the AI age

Density functional theory (DFT) plays a pivotal role in chemical and materials science because of its relatively high predictive power, applicability, versatility, and computational efficiency. We review recent progress in machine learning (ML) model developments, which have relied heavily on DFT for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in a broader context for chemical and materials sciences. DFT-based ML models have reached high efficiency, accuracy, scalability, and transferability and pave the way to the routine use of successful experimental planning software within self-driving laboratories.

Density-Functional Study of the Si/SiO2 Interfaces in Short-Period Superlattices: Structures and Energies

The oxide-semiconductor interface is a key element of MOS transistors, which are widely used in modern electronics. In silicon electronics, SiO2 is predominantly used. The miniaturization requirement raises a problem regarding the growing of heterostructures with ultrathin oxide layers. Two structural models of interface between crystalline Si and cristobalite SiO2 are studied by using DFT-based computer modelling. The structures of several Si/SiO2 superlattices (SL), with layer thicknesses varied within 0.5–2 nm, were optimized and tested for stability. It was found that in both models the silicon lattice conserves its quasi-cubic structure, whereas the oxide lattice is markedly deformed by rotations of the SiO4 tetrahedra around axes perpendicular to the interface plane. Based on the analysis of the calculated total energy of SLs with different thicknesses of the layers, an assessment of the interface formation energy was obtained. The formation energy is estimated to be approximately 3–5 eV per surface Si atom, which is close to the energies of various defects in silicon. Elastic strains in silicon layers are estimated at 5–10%, and their value rapidly decreases as the layer thickens. The elastic strains in the oxide layer vary widely, in a range of 1–15%, depending on the interface structure.

DFT-Based Tight-binding model of vdW bilayer χ3 and β12 borophene

In this work, a tight-binding (TB) model with the Slater–Koster (SK) approximation is presented to describe the electronic properties of vdW bilayer χ3 and β12 borophene with AA stack. This model is created by using four orbitals of S, Px, Py, and Pz for each boron atom. By fitting the results of the TB model and density functional theory (DFT) calculations using the least-squares method and the Levenberg-Marquardt nonlinear fitting algorithm, the optimal Hamiltonian and the overlap matrix are calculated. This TB model well describes energy bands around high-symmetry k-points, by considering three nearest neighbors in each layer and a non-orthogonal basis set.

Self-consistent and detailed opacities from a non-equilibrium average-atom model.

Modern density functional theory (DFT) is a powerful tool for accurately predicting self-consistent material properties such as equations of state, transport coefficients and opacities in high energy density plasmas, but it is generally restricted to conditions of local thermodynamic equilibrium (LTE) and produces only averaged electronic states instead of detailed configurations. We propose a simple modification to the bound-state occupation factor of a DFT-based average-atom model that captures essential non-LTE effects in plasmas-including autoionization and dielectronic recombination-thus extending DFT-based models to new regimes. We then expand the self-consistent electronic orbitals of the non-LTE DFT-AA model to generate multi-configuration electronic structure and detailed opacity spectra. This article is part of the theme issue 'Dynamic and transient processes in warm dense matter'.

Unravelling the role of metal-metal oxide interfaces of Cu/ZnO/ZrO2/Al2O3 catalyst for methanol synthesis from CO2: Insights from experiments and DFT-based microkinetic modeling

Cu/ZnO/ZrO2/Al2O3 catalysts are widely explored for CO2 conversion to methanol due to their higher activity and stability. However, mechanistic understanding of the performance of such catalysts is lacking due to ambiguity on the actual active sites. This study focuses on unraveling the nature of different interfaces on Cu/ZnO/ZrO2/Al2O3 catalyst by coupling experiments, Density Functional Theory (DFT) simulations and a DFT-based reactor scale multi-site microkinetic model. Although DFT calculations suggested the ZrO2/Cu interface to be the CO2 adsorption site, the validated microkinetic model predicted the ZnO/Cu interface to be the crucial reaction center. Reaction pathway analysis showed that methanol is produced through the formate pathway near the reactor entrance, whereas, the carboxyl pathway dominates in the latter zones, emphasizing the occurrence of both CO2 and CO hydrogenation. This deeper understanding of the reaction behavior of such multicomponent catalysts will aid in designing better catalysts and optimizing reaction conditions and systems.

Toward Carbon Monoxide Methanation at Mild Conditions on Dual-Site Catalysts.

The catalytic carbon monoxide (CO) methanation is an ideal model reaction for the fundamental understanding of catalysis on the gas-solid interface and is crucial for various industrial processes. However, the harsh operating conditions make the reaction unsustainable, and the limitations set by the scaling relations between the dissociation energy barrier and dissociative binding energy of CO further increase the difficulty in designing high-performance methanation catalysts operating under milder conditions. Herein, we proposed a theoretical strategy to circumvent the limitations elegantly and achieve both facile CO dissociation and C/O hydrogenation on the catalyst containing a confined dual site. The DFT-based microkinetic modeling (MKM) reveals that the designed Co-Cr2/G dual-site catalyst could provide 4-6 orders of magnitude higher turnover frequency for CH4 production than the cobalt step sites. We believe that the proposed strategy in the current work will provide essential guidance for designing state-of-the-art methanation catalysts under mild conditions.

DFT-based theoretical model for predicting the loading and release of pH-responsive paracetamol drug

Here, we provide a theoretical framework that integrates quantum mechanical calculations with classical pKa theory to forecast the degree of interaction of drug molecules with carrier surfaces across the whole pH range. The drug loading and release of a pH-responsive drug delivery system is demonstrated using paracetamol drug carried using mesoporous silica surface with and without trimethylammonium (TA) functional group. The model is explained on the basis of possible combinations of surface (S) and drug (D) molecules as neutral (0) and deprotonated (1) pH-dependent states. The relative probabilities of these states depend on the pKa values of the drug as well as surface and the desired pH. Paracetamol, an analgesic and antipyretic drug, is required to be absorbed in small intestine and not in the stomach. It’s seen that Paracetamol is caught in the MSN-TA nano-vehicle when it goes throughthe acidic environment of the stomach and then released in the slightly basic pH of the intestine. The reported model from the literature is used for forecasting the loading and release pH for the Paracetamol using mesoporous silica surface.

NiCo/Al2O3 nanocatalysts for the synthesis of 5-amino-1-pentanol and 1,5-pentanediol from biomass-derived 2-hydroxytetrahydropyran

Al2O3-supported monometallic Ni, Co, and bimetallic Ni–Co nanocatalysts originated from layered double hydroxide precursors were synthesized by co-precipitation method, and used for the synthesis of useful 5-amino-1-pentanol (5-AP) and 1,5-pentanediol (1,5-PD) by reductive amination (RA) or direct hydrogenation of biofurfural-derived 2-hydroxytetrahydropyran (2-HTHP), respectively. In both reactions, the yield of the target products decreased monotonously with the increasing amounts of Co in the NiCo/Al2O3 catalysts, owing probably to the replacement of highly reactive Ni by Co component with inferior hydrogenation activity at the low reaction temperature of 60 °C. However, the incorporation of Co could improve the reducibility of the NiCo/Al2O3 bimetallic catalysts and promote the reaction stability of the catalysts, especially for Ni2Co1/Al2O3, in both reactions with over 180 h time-on-stream. Characterization of the catalysts before and after the reaction showed that the incorporating Co could inhibit the sintering of metal particles and hinder the surface oxidation of the more reactive Ni0 species, thanks to the formation of Ni–Co alloy in the bimetallic catalysts. DFT-based modeling of the reaction mechanisms is also performed, supporting the reaction pathway proposed previously and also the much higher activity of Ni in the RA of 2-HTHP as compared with Co.

Synthesis, crystal structure, and chromotropism properties of Dihalo N-2-ethanolpicolylamine copper(II)

Two copper (II) mononuclear complexes with the general formula [Cu(HL)X2], which L = N-2-ethanolpicolylamine and X = Cl, Br, were synthesized and characterized by elemental analysis infrared, electronic absorption spectroscopy techniques, and conductivity method. The distorted square pyramidal geometry of [Cu(HL)Cl2] was confirmed by the density functional theory level (DFT) calculation and X-ray crystallography. The complexes are soluble in polar solvents and are chromotropic. It was found both complexes have a similar identity in aqueous solution due to dissociation of halides and hydration. Their chromotropism behavior, including solvato-, thermos- and halochromism, was investigated using visible absorption spectroscopy. The original green complex displayed a reversible color change to blue in alkaline and almost colorless in acidic solutions. The pH effect on the visible absorption spectrum of the complex was studied that functions as pH-induced “off–on-off” switches triggered by the deprotonation of ligand in an alkaline aqueous solution and protonation along with loss of the ligand HL in an acidic medium. The complex also exhibited thermochromism and solvatochromism due to solvation and structural change. Further computational analysis of the electronic structure of the complex in different solvents was executed to gain a deeper insight into the properties associated with molecular orbitals. Its electronic spectrum was analyzed, and bands were assigned through the time-dependent density functional theory level (TDDFT) calculation, together with DFT-based molecular modeling

Towards a DFT-based layered model for TCAD simulations of MoS2

In this work, we employ the results of atomistic DFT calculation to extract useful parameters for the simulation of few-layers MoS2 structures with traditional TCAD tools. In particular, we focus on the charge distribution, which allows us to obtain a layered model for the dielectric constant, and on the effective densities of states in the conduction and valence bands taking into account the full 2D density of states. Using this model, we compute the capacitance of a metal–oxide–semiconductor structure and compare it to the one obtained employing a uniform model with averaged effective parameters.

DFT-based modeling of polypyrole/B12N12 nanocomposite for the photocatalytic applications

Boron-based nanocomposites considered as one of the most promising photocatalysts, have drawn significant attention in the degradation of pollutants in aquatic environments. In this regard, density functional theory calculations were carried out on the B12N12 nanocluster interacted with various lengths of (PPy)n oligomers (n = 3, 5, 7, and 9) to predict the optical, electronic, charge transfer properties as well as the optimum composition of obtained (PPy)n/B12N12 nanocomposites. It was found that the most stable nanocomposite corresponds to (PPy)3/B12N12, which was supported by its greatest adsorption energy (−59.460 kcal mol−1) in the gas phase. The calculations in the gas phase and water showed that water as a solvent has a key role in the interaction between B12N12 nanocluster and (PPy)n oligomers. The results revealed that the adsorption of (PPy)9 oligomer on the B12N12 nanocluster leads to the highest reduction (∼ 2.672 eV) in the energy gap (Eg) value, while the lowest reduction (∼ 1.475 eV) was related to (PPy)3/B12N12 nanocomposite. Moreover, the natural bond orbital analysis showed that the charge flows from (PPy)n oligomers to the B12N12 nanocluster. Polarizability (α0) and first hyperpolarizability (β0) values revealed that the adsorption of (PPy)9 oligomer on the surface of B12N12 nanocluster has the most considerable effect on the optical response of B12N12 nanocluster due to increasing the α0 and β0 values about 492.49 and 5070.07 a.u, respectively. The UV–Vis spectra analysis showed that the (PPy)9/B12N12 nanocomposite has the highest bathochromic shift (∼ 245.39 nm) among other nanocomposites. Finally, quantum theory of atoms in molecules analysis showed that (PPy)n oligomers interact with B12N12 nanocluster through the partial covalent interactions.

NMR-Based Configurational Assignments of Natural Products: How Floating Chirality Distance Geometry Calculations Simplify Gambling with 2N-1 Diastereomers.

Using NMR data, the assignment of the correct 3D configuration and conformation to unknown natural products is of pivotal importance in pharmaceutical and medicinal chemistry. In this report, we quantify the probability of configurational assignments to judge the quality of structural elucidations using Bayesian inference in combination with floating-chirality distance geometry simulations. Based on reference-free NOE/ROE data, residual dipolar couplings (RDCs), and residual quadrupolar couplings (RQCs) in various combinations, we demonstrate how the relative configurations of three natural compounds, namely, jatrohemiketal (1), artemisinin (2), and Taxol (3), can be unambiguously established without the necessity to carry out time-consuming DFT-based configurational and conformational analyses. Our results quantitatively describe how reliably molecular geometries can be inferred from experimental NMR data, thereby unequivocally unveiling remaining assignment ambiguities. The methodology presented here will dramatically reduce the risk of incorrect structural assignments based on the overinterpretation of incomplete data and DFT-based structure models in chemistry.