Search Results (594)

Particle Swarm Optimization (PSO) is a metaheuristic optimization technique based on population behavior, inspired by the movement of a flock of birds or a school of fish. In this method, particles move in a search space to find the global minimum of an objective function. In this work, a modified PSO algorithm written in Fortran 90 is proposed. The optimized structures obtained with this algorithm are compared with those obtained using the basin-hopping (BH) method written in Python (3.10), and complemented with density functional theory (DFT) calculations using the Gaussian 09 software. Additionally, the results are compared with the structural parameters reported from single crystal X-ray diffraction data for carbon clusters ${\mathrm{C}}_n$(n = 3–5), and tungsten–oxygen clusters, ${\mathrm{WO}}_n^{m-}$(n = 4–6, $m=2,4,6$). The PSO algorithm performs the search for the minimum energy of a harmonic potential function in a hyperdimensional space $\in {\mathbb{R}}^{3N}$ (where N is the number of atoms in the system), updating the global best position ( $g_{best}$) and local best position ( $p_{best}$), as well as the velocity and position vectors for each swarm cluster. A good approximation of the optimized structures and energies of these clusters was obtained, compared to the geometric optimization and single-point electronic energies calculated with the BH and DFT methods in the Gaussian 09 software. These results suggest that the PSO method, due to its low computational cost, could be useful for approximating a molecular structure associated with the global minimum of potential energy, accelerating the prediction of the most stable configuration or conformation, prior to ab initio electronic structure calculation. Full article

Waste drilling fluids represent a complex gel–colloidal system containing structurally stable polymeric networks and heavy-metal ions that can cause tremendous damage to the ecosystem. The current disposal methods, like solidification/landfills, formation reinjection, and chemical treatment, commonly suffer from high secondary pollution risks, poor resource recovery, and incomplete detoxification. This paper developed a photocatalytic approach to complex gel system treatment by hydrothermally synthesizing a novel sulfur-doped, oxygen-vacancy-modified 3D flower-like xS-BiOBr₀.₅Cl₀.₅ structure which effectively narrowed the bandgap of BiOX and thus significantly enhanced its catalytic activity. The chemical composition, morphology, specific surface areas, and bandgaps of the materials were characterized. The photocatalytic performance and cyclic stability of the materials were measured, and 0.5S-BiOBr_0.5Cl_0.5 showed the best photocatalytic performance. The rhodamine B(RhB) degradation and polymer degradation efficiencies of 0.5S-BiOBr_0.5Cl_0.5 were up to 91% and 79%, respectively, while the Hg(II), Cr(VI), and Cr(III) reduction efficiencies of the material were up to 48.10%, 96.58%, and 96.41%, respectively. The photocatalytic mechanism of the xS-BiOBr_0.5Cl_0.5 materials was evaluated through an oxygen vacancy analysis, active species capture experiments, and density functional theory (DFT) computations. Overall, the xS-BiOBr_0.5Cl_0.5 materials can provide a low-cost and harmless treatment method for waste drilling fluids and promote the “green” development of oil and gas. Full article

Heteroatom doping is an effective strategy for improving the sodium storage performance of hard carbon. However, the use of sulfur and fluorine codoped carbon materials as anodes for sodium-ion batteries has not been reported. Here, an S-infused/S, F-codoped PVDF-derived carbon SFC5 was prepared by one-step carbonization of PVDF and synchronously used as an anode for a sodium-ion battery. The prepared SFC5 containing 10.11 at% S and 9.54 at% F is a short-range ordered amorphous carbon with a microporous structure. Owing to the structural advantages of S, F codoping, and the high specific capacity of S, SFC5 exhibited an outstanding sodium storage performance of 365 mAh g⁻¹ after 200 cycles at 50 mA g⁻¹ and 212 mAh g⁻¹ after 500 cycles at 400 mA g⁻¹. Moreover, theoretical calculations based on density functional theory (DFT) verify that S and F codoping can considerably reduce the Na⁺ adsorption energy and increase the electronic conductivity of SFC5. The current study presents a viable and facile approach to prepare high-performance, low-cost anode materials for SIBs, supported by empirical evidence and theoretical computations. Full article

Carbon dioxide is naturally present in the Earth’s atmosphere and plays a role in regulating and balancing the planet’s temperature. However, due to various human activities, the amount of carbon dioxide is increasing beyond safe limits, disrupting the Earth’s natural temperature regulation system. Today, CO₂ is the most prevalent greenhouse gas; as its concentration rises, significant climate change occurs. Therefore, there is a need to utilize anthropogenically released carbon dioxide in valuable fuels, such as formic acid (HCOOH). Single-atom catalysts are widely used, where a single metal atom is anchored on a surface to catalyze chemical reactions. In this study, we investigated the potential of Cu@Phosphorene as a single-atom catalyst (SAC) for CO₂ reduction using quantum chemical calculations. All computations for Cu@Phosphorene were performed using density functional theory (DFT). Mechanistic studies were conducted for both bimolecular and termolecular pathways. The bimolecular mechanism involves one CO₂ and one H₂ molecule adsorbing on the surface, while the termolecular mechanism involves two CO₂ molecules adsorbing first, followed by H₂. Results indicate that the termolecular mechanism is preferred for formic acid formation due to its lower activation energy. Further analysis included charge transfer assessment via NBO, and interactions between the substrate, phosphorene, and the Cu atom were confirmed using quantum theory of atoms in molecules (QTAIM) and non-covalent interactions (NCI) analysis. Ab initio molecular dynamics (AIMD) calculations examined the temperature stability of the catalytic complex. Overall, Cu@Phosphorene appears to be an effective catalyst for converting CO₂ to formic acid and remains stable at higher temperatures, supporting efforts to mitigate climate change. Full article

Supports used in heterogeneous metallic catalysts serve as a structural skeleton across which metallic nanoparticles are dispersed, but specific properties of the supports may also determine the behavior of these nanoparticles in catalytic processes. For example, it is known that among various properties of crystalline alkaline earth metal oxides serving as supports, the ability of their surface sites to donate electrons, that is their basicity, has an influence on the characteristics of the adsorbed metal. In the present work, the influence of MeO (Me = Mg, Ca, and Sr) basicity on the adsorption of Pb on the (100) surface of MeO crystals is studied by means of a dispersion-corrected density functional theory (DFT-D) computational method. The DFT-D calculations have characterized essential structural parameters, energetics, and the distribution of the electron charge for the Pb atoms and Pb dimers adsorbed at the regular O²⁻ and defective F_s centers of MeO(100). It has been observed that an increase in the basicity of MeO(100) in the sequence MgO < CaO < SrO results in a more energetically favorable effect of Pb adsorption, a stronger interaction between Pb and the surface, and a greater amount of electron charge acquired by the adsorbed Pb atoms and dimers. These findings contribute to a better understanding of how support basicity may modulate certain characteristics of MeO-supported metallic catalysts containing Pb as an additive. From a computational viewpoint, this work shows that the inclusion of spin–orbit relativistic correction in the DFT-D calculations leads to a significant reduction in the strength of the interaction between Pb and MeO(100), but it does not change the aforementioned trend in the strength of this interaction as a function of support basicity. Full article

In this study, we aimed to determine the chemical constituents of M. seguinii, which led to the isolation and identification of 26 compounds. Three new prenylated dihydrobenzofurans [myrsinoic acids I (1), J (2), and K (3)] and a new flavonoid glycoside, mearnsetin 3-O-α-L-arabinopyranoside (4), were discovered, and the absolute configuration of the known compound, myrsinoic acid B (5), was re-established. To ensure the structural accuracy of these compounds, comprehensive spectroscopic analyses were performed, including one- and two-dimensional nuclear magnetic resonance spectroscopy, mass spectrometry, and circular dichroism spectroscopy. In addition, computational analysis methods such as density functional theory (DFT)-based Electronic Circular Dichroism (ECD) simulations and Gauge-Including Atomic Orbitals (GIAOs) ¹H and ¹³C NMR chemical shift calculations with DP4+ probability analysis were utilised to further support the structural assignments. Full article

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y₂O₃ interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies ($E^{Seg}$) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining $E^{Seg}$. Moreover, the segregation behavior at a specific Fe/Y₂O₃ interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y₂O₃ interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations. Full article

Laser cladding rapid solidification technique is an effective strategy for manufacturing ultra-high-strength martensitic stainless steels (UHS-MSS). Due to super-saturation solution strengthening of interstitial atoms (IAs), martensitic stainless steels containing IAs exhibit excellent ultra-high strength and toughness and have high tolerance for oxygen impurities. Hence, studying the specific speciation and structural characteristics of IAs is of great significance for guiding laser cladding of ultra-high-strength steels. Herein, we use density functional theory (DFT) computations to analyze the stable occupancies of IAs and their interactions in body-centered cubic iron (BCC Fe). The findings show that single IAs prefer to occupy octahedral sites over tetrahedral sites. Therefore, octahedral sites are selected as the optimal sites for the following double IAs study. For homo IAs, C-C and N-N configurations exhibit greater stability at long-range distances, whereas O-O demonstrate optimal stability at intermediate distances. Crucially, hetero IAs configurations are more stable compared to single IAs and homo IAs, exhibiting a synergistic effect. Especially, the C-O combination shows the highest stability and strongest bonding character. Meanwhile, the dissociation behavior of O indicates that C-O and N-O have higher dissociation temperatures than single O, further verifying the synergistic effect of hetero IAs. This provides a theoretical basis for understanding the interstitial solution strengthening of laser cladding UHS-MSS. Full article

A one-pot synthetic methodology that combines an Ugi-Zhu three-component reaction (UZ-3CR) with a cascade sequence (intermolecular aza Diels–Alder cycloaddition/intramolecular N-acylation/decarboxylation/dehydration) using microwave-heating conditions, ytterbium (III) triflate (Yb(OTf)₃) as the catalyst, and chlorobenzene (for the first time in a multi-component reaction (MCR)) as the solvent, was developed to synthesize twelve new fluorinated-pyrrolo[3,4-b]pyridin-5-ones containing a 4-amino-7-chloroquinoline moiety, yielding 50–77% in 95 min per product, with associated atom economies around 88%, also per product. Additionally, by in vitro tests, compounds 19d and 19i were found to effectively stop early SARS-CoV-2 replication, IC₅₀ = 6.74 µM and 5.29 µM, at 0 h and 1 h respectively, while cell viability remained above 90% relative to the control vehicle at 10 µM. Additional computer-based studies revealed that the most active compounds formed strong favorable interactions with important viral proteins (M_pro, NTDα and NTDo) of coronavirus, supporting a two-pronged approach that affects both how the virus infects the cells and how it replicates its genetic material. Finally, quantum chemistry analyses of non-covalent interactions were performed from Density-Functional Theory (DFT) to better understand how the active compounds hit the virus. Full article

Background/Objectives: Understanding the stabilization mechanisms of amino acid conformations in different solvent environments is crucial for elucidating biomolecular interactions and crystallization processes. This study presents a comprehensive computational investigation of glycine, the simplest amino acid, in both its canonical and zwitterionic forms when interacting with dimethyl sulfoxide (DMSO) molecules. Methods: Using density functional theory (DFT) calculations at the B3LYP/6-311++G(d,p) level with empirical dispersion corrections, we examined the conformational landscape of glycine–DMSO clusters with one and two DMSO molecules, as well as implicit solvent calculations, and compared them with analogous water clusters. Results: Our results demonstrate that while a single water molecule is insufficient to stabilize the zwitterionic form of glycine, one DMSO molecule successfully stabilizes this form through specific interactions between the S=O and the methyl groups of DMSO and the NH₃⁺ and the oxoanion group of zwitterionic glycine, respectively. Topological analysis of the electron density using QTAIM and NCI methods reveals the nature of these interactions. When comparing the relative stability between canonical and zwitterionic forms, we found that two DMSO molecules significantly reduce the energy gap to approximately 12 kJ mol⁻¹, suggesting that increasing DMSO coordination could potentially invert this stability. Implicit solvent calculations indicate that in pure DMSO medium, the zwitterionic form becomes more stable below 150 K, while remaining less stable at room temperature, contrasting with aqueous environments where the zwitterionic form predominates. Conclusions: These findings provide valuable insights into DMSO’s unique role in biomolecular stabilization and have implications for protein crystallization protocols where DMSO is commonly used as a co-solvent. Full article

The growing need for rapid screening of adsorption energies in organic materials has driven substantial progress in developing various architectures of equivariant graph neural networks (eGNNs). This advancement has largely been enabled by the availability of extensive Density Functional Theory (DFT)-generated datasets, sufficiently large to train complex eGNN models effectively. However, certain material groups with significant industrial relevance, such as aromatic compounds, remain underrepresented in these large datasets. In this work, we aim to bridge the gap between limited, domain-specific DFT datasets and large-scale pretrained eGNNs. Our methodology involves creating a specialized dataset by segregating aromatic compounds after a targeted ensemble extraction process, then fine-tuning a pretrained model via approaches that include full retraining and systematically freezing specific network sections. We demonstrate that these approaches can yield accurate energy and force predictions with minimal domain-specific training data and computation. Additionally, we investigate the effects of augmenting training datasets with chemically related but out-of-domain groups. Our findings indicate that incorporating supplementary data that closely resembles the target domain, even if approximate, would enhance model performance on domain-specific tasks. Furthermore, we systematically freeze different sections of the pretrained models to elucidate the role each component plays during adaptation to new domains, revealing that relearning low-level representations is critical for effective domain transfer. Overall, this study contributes valuable insights and practical guidelines for efficiently adapting deep learning models for accurate adsorption energy predictions, significantly reducing reliance on extensive training datasets. Full article

Barium titanate (BaTiO₃) has long been recognized as a promising photocatalyst for solar-driven water splitting due to its unique ferroelectric, piezoelectric, and electronic properties. This review provides a comprehensive analysis of atomistic simulation studies of BaTiO₃, highlighting the role of density functional theory (DFT), ab initio molecular dynamics (MD), and classical all-atom MD in exploring its photocatalytic behavior, in line with various experimental findings. DFT studies have offered valuable insights into the electronic structure, density of state, optical properties, bandgap engineering, and other features of BaTiO₃, while MD simulations have enabled dynamic understanding of water-splitting mechanisms at finite temperatures. Experimental studies demonstrate photocatalytic water decomposition and certain modifications, often accompanied by schematic diagrams illustrating the principles. This review discusses the impact of doping, surface modifications, and defect engineering on enhancing charge separation and reaction kinetics. Key findings from recent computational works are summarized, offering a deeper understanding of BaTiO₃’s photocatalytic activity. This study underscores the significance of advanced multiscale simulation techniques for optimizing BaTiO₃ for solar water splitting and provides perspectives on future research in developing high-performance photocatalytic materials. Full article

Calcium carbonate (CaCO₃) has attracted considerable attention owing to its structural versatility and broad applications in materials science and geochemistry. In this study, we employed Density Functional Theory (DFT) simulations to systematically investigate the structural, elastic, and dynamic properties of trigonal CaCO₃ under hydrostatic pressures ranging from 0 to 1.2 GPa. The optimized lattice constants closely align with previous theoretical and experimental values, thereby confirming the reliability of the computational approach. Mechanical stability was validated across the entire pressure range, with elastic constants and moduli demonstrating gradual increases under compressive strain. Elastic anisotropy was rigorously quantified using universal anisotropy indices, three-dimensional surface visualizations, and directional projections of elastic moduli. These analyses revealed pronounced pressure-dependent anisotropy. Furthermore, optical properties, including refractive indices and dielectric functions, were analyzed to clarify pressure-induced variations in electromagnetic interactions. These findings offer valuable insights into the pressure behavior of CaCO₃, advancing its potential applications in advanced functional materials and geophysical research. Full article

Surface and sub-surface atomic configurations are critical for catalysis as they host the active sites governing electrochemical processes. This study employs density functional theory (DFT) calculations and Monte Carlo simulations combined with the cluster-expansion approach to investigate atom distribution and Pt segregation in Pt-Fe alloys across varying Pt/Fe ratios. Our simulations reveal a strong tendency for Pt atoms to segregate to the surface layer while Fe atoms enrich the sub-surface region. Crucially, the calculations predict the stability of a periodic Pt₂Fe alloy surface model, characterized by specific defect structures, at low platinum content and low annealing temperatures. Electronic structure analysis indicates that forming this Pt₂Fe surface alloy lowers the d-band center of Pt atoms, weakening CO adsorption and thereby enhancing resistance to CO poisoning. Although defect-induced strains can modulate the d-band center, crystal orbital Hamilton population (COHP) analysis confirms that such strains generally strengthen Pt-CO interactions. Therefore, the theoretical design of Pt₂Fe alloy surfaces and controlling defect density are predicted to be effective strategies for enhancing catalyst resistance to CO poisoning. This work highlights the advantages of periodic Pt₂Fe surface models for anti-CO poisoning and provides computational guidance for designing efficient Pt-based electrocatalysts. Full article

Access to clean water is a pressing global concern and membrane technologies play a vital role in addressing this challenge. Thin-film composite membranes prepared via interfacial polymerization (IPol) using meta-phenylenediamine (MPD) and trimesoyl chloride (TMC) exhibit excellent separation performance, but face limitations such as fouling and low hydrophilicity. This study investigated the interaction between MPD and sulfonated zinc phthalocyanine, Zn(SO₂⁻)₄Pc, as a potential strategy for enhancing membrane properties. Using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT), we analyzed the optimized geometries, electronic structures, UV–Vis absorption spectra, FT-IR vibrational spectra, and molecular electrostatic potentials of MPD, Zn(SO₂⁻)₄Pc, and their complexes. The results show that MPD/Zn(SO₂⁻)₄Pc exhibits reduced HOMO-LUMO energy gaps and enhanced charge delocalization, particularly in aqueous environments, indicating improved stability and reactivity. Spectroscopic features confirmed strong interactions via hydrogen bonding and π–π stacking, suggesting that Zn(SO₂⁻)₄Pc can act as a co-monomer or additive during IPol to improve polyamide membrane functionality. A conformational analysis of MPD/Zn(SO₂⁻)₄Pc was conducted using density functional theory (DFT) to evaluate the impact of dihedral rotation on molecular stability. The 120° conformation was identified as the most stable, due to favorable π–π interactions and intramolecular hydrogen bonding. These findings offer computational evidence for the design of high-performance membranes with enhanced antifouling, selectivity, and structural integrity for sustainable water treatment applications. Full article