Search Results (251)

Catalysis has entered everyday life through a range of technological processes that rely on different catalytic systems. The increasing demand for such systems requires rationalization of the use of their expensive components, such as noble-metal catalysts. As such, a catalyst with low noble-metal concentration, in which each one of the noble atoms is active, would reach the lowest price possible. Nevertheless, no clear reactivity descriptors have been outlined for this type of low-coordinated supported atom. Using DFT calculations, we consider three diverse systems as models of single-atom catalysts. We investigate monomers and bimetallic dimers of Ru, Rh, Pd, Ir, and Pt on MgO(001), Cu adatom on thin Mo(001)-supported films (NaF, MgO, and ScN), and single Pt adatoms on oxidized graphene surfaces. The reactivity of these metal atoms was probed by CO. In each case, we see the interaction through the donation–backdonation mechanism. In some cases, CO adsorption energies can be linked to the position of the d-band center and the adatom’s charge. A higher-lying d-band center and less-charged, supported single atoms bind CO more weakly. Also, in some cases, metal atoms that are less strongly bound to the substrate bind CO more strongly. The results suggest that the identification of common activity descriptor(s) for single metal atoms on foreign supports is a difficult task with no unique solution. However, it is also suggested that the stability of adatoms and strong anchoring to the support are prerequisites for the application of descriptor-based search to novel single-atom catalysts. Full article

Atomic charges are widely used to analyze molecular electronic structure and substituent effects, yet their numerical values and interpretations are inherently dependent on the adopted density partitioning scheme. Here, we adapt the Equivariant Atomic Contribution framework to molecular systems (EAC-qm), enabling prediction of atom-resolved continuous charge densities from which atomic charges are obtained as spatial moments. The predicted densities reproduce reference density functional theory results with high accuracy and preserve global charge conservation. To assess chemical interpretability, we examine charge responses in monosubstituted aromatic systems using Hammett substituent constants as external empirical references. Atomic charges derived from EAC-qm exhibit a strong linear association with Hammett parameters, compared with values obtained from traditional density partitioning approaches applied to the same electronic structures. These correlations indicate that density-derived charges respond systematically to established substituent electronic trends. Beyond scalar charges, atom-resolved dipole moments can be evaluated as first-order moments of the same continuous density representation. Illustrative examples for formaldehyde (H₂CO) and formamide (HCONH₂) show that local dipole vectors provide directional information about intra-atomic polarization that is not captured by point-charge models. Overall, the results suggest that machine-learned continuous electron densities provide a representation-consistent basis for constructing atom-centered electronic descriptors with chemical interpretability. Full article

Here we perform a detailed information-theoretic (IT) analysis of atomic electron densities in the periodic table, from hydrogen (Z = 1) to lawrencium (Z = 103). By use of the Shannon entropy, the Fisher information and the disequilibrium functionals in both position and momentum spaces as fundamental descriptors of the atomic densities, the periodic table can be represented in a three-dimensional information space as a continuous, highly ordered manifold. The analysis shows that chemical periodicity naturally emerges as a helicoidal manifold (reminiscent of a helix) at the coordinates of a 3D theoretic-information space (Shannon, Fisher, Disequilibrium), with each period forming one segment within the continuous global trajectory. We find information-theoretic signatures of shell structure, sub-shell filling, and electron-configuration anomalies, such as the familiar irregularities seen in chromium and copper. Therefore, the helicoidal character emerges naturally and is not imposed a priori. Further, through the uncertainty principle of the complementary analysis in momentum space, more insights are gained by exposing maximal information-theoretic differentiation for lighter atoms and compression among heavy elements. Notably, momentum-space analysis reveals that hydrogen occupies a natural intermediate position between helium and lithium based on kinetic energy distribution—contrasting with IT position-space results that emphasize hydrogen’s unique delocalized electron density. Indeed, the 3D IT representation of the elements in position space aligns with the view that H does not belong to either the alkali metals or the halogens, but rather stands as a unique, standalone element. This complementary perspective provides new quantitative support for understanding hydrogen’s dual chemical nature, providing new quantitative insight into ongoing debates about hydrogen’s optimal periodic table position. Furthermore, by considering triadic relationships and complexity properties in relation to the López–Mancini–Ruiz (LMC) and Fisher–Shannon (FS) functionals, we show that atomic complexity increases monotonically along with nuclear charge, and we provide a quantitative measure of how organized atomic electron densities are distributed throughout the periodic system. Based on our IT analyses, the fundamental character of periodicity could be addressed by employing helicoidal representations that highlight the characteristics of hydrogen, while simultaneously preserving the autonomy of the blocks of elements. Full article

Polypyrrole-based functional composites are increasingly explored and extensively adopted for energy storage, sensing, and environmental applications due to their tunable electronic properties, chemical versatility, and mechanical stability. However, rational optimization of these composites requires a unified understanding of electronic, mechanical, thermal, and chemical behavior at the atomic scale, which underlies their multifunctional behavior, and remains fragmented. Notably, Density Functional Theory (DFT) provides indispensable atomistic insight into the electronic, mechanical, thermal, and chemical interactions that govern the performance of multifunctional materials. To bridge these gaps, this review presents a comprehensive assessment of recent DFT and time-dependent DFT (TD-DFT) studies that elucidate the electronic, mechanical, thermal, and chemical characteristics of polypyrrole and its hybrid composites. Key theoretical descriptors, including electronic structure modulation, charge transfer behavior, adsorption energetics, interfacial binding energies, hydrogen bond formation, and charge redistribution, are critically assessed to establish structure–property relationships across diverse functional systems. Considerable attention is given to interfacial interactions, doping strategies, and composite architectures that govern durability, conductivity, and chemical stability. By consolidating current atomistic insights and identifying existing limitations, this review provides a coherent framework for rational material design. Notably, it presents the first systematic quantification of dopant steric effects in PPy multifunctional composites, linking atomistic-scale modifications to the optimization of functional properties in next-generation applications. Full article

Accurate prediction of wear behavior in polymer nanocomposites remains challenging due to the coupled influence of operating conditions and evolving surface morphology. In this study, a surface-aware triboinformatics framework is proposed to predict the dry sliding wear behavior of multi-walled carbon nanotube (MWCNT) reinforced epoxy composites by integrating operating parameters with run-wise atomic force microscopy (AFM) surface descriptors. Wear experiments were conducted using a Taguchi L16 design by varying CNT content (0–0.75 wt.%), applied load (10–40 N), sliding speed (183–458 rpm), and sliding distance (500–1250 m). AFM-derived parameters, including Ra, Rq, Z-range, and surface area difference, were extracted from the worn surface corresponding to each experimental run. Multiple regression-based machine learning models were evaluated using leave-one-out cross-validation, with ensemble-based models providing the best predictive performance (R² > 0.85 with low RMSE and MAE). Feature importance and partial dependence analyses identified CNT content as the dominant factor controlling wear reduction, followed by Z-range and Ra, highlighting the critical role of surface damage severity. Neat epoxy exhibited a maximum wear loss of 0.444 mg, whereas the 0.75 wt.% CNT composite showed values as low as 0.003 mg under comparable conditions, corresponding to a reduction of approximately 99%. The proposed framework enables mechanistically interpretable wear prediction and supports the design of durable polymer composites, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

The relative position of the oxime group within pharmaceutically relevant pyridinium oximes is a pivotal factor that governs their intrinsic physicochemical properties and their biological reactivity. However, studies providing in-depth, molecular-level insight into these structure–reactivity relationships are still limited. In this work, we present an integrated experimental and computational study of N-methylpyridinium-2-aldoxime chloride (PAM2-Cl), N-methylpyridinium-3-aldoxime iodide (PAM3-I), and N-methylpyridinium-4-aldoxime iodide (PAM4-I), aimed at elucidating discrete differences in their ionization behavior, electronic structure, σ-donor properties, and nucleophilicity. The crystal structure of PAM3-I was determined by X-ray diffraction. Comparative structural and spectroscopic (UV–Vis, NMR, IR) analyses elucidated the structural and electronic effects arising from the position of the oxime group. Kinetic studies of substitution reactions with aquapentacyanoferrate(II) in aqueous solution enabled the determination of pentacyano(PAM)ferrate(II) formation and dissociation rate constants, coordination modes, pK_a values of the coordinated ligands, complex stability constants, and σ-donating capabilities. The DFT-based analysis of atomic charge distribution transcended experimental limitations, offering a new perspective on electronic structure-related properties. This study presents the first side-by-side, internally consistent structure–reactivity map across PAM2, PAM3, and PAM4 isomers that triangulates crystallography, UV–Vis-derived pK_a values, substitution kinetics, and DFT descriptors in a single framework. Full article

The dry sliding wear behaviour of nanoclay-filled bio-based epoxy composites was systematically investigated using a Taguchi L16 experimental design by varying nanoclay content (0–0.35 wt.%), normal load, sliding speed, and sliding time against an EN24 steel counterface. Wear loss, specific wear rate (SWR), frictional response, thermal rise, and energy-based descriptors were quantified, followed by mathematical and machine-learning (ML) based modelling. The results demonstrate that nanoclay addition significantly improves tribological performance up to an optimal content of 0.25 wt.%, beyond which wear instability increases. Compared with neat epoxy, the 0.25 wt.% nanoclay composite exhibited a reduction in steady-state coefficient of friction from ~0.53 to ~0.42, along with a 25–30% decrease in specific wear rate and the lowest energy-to-wear conversion efficiency, indicating more effective utilization of frictional energy. Taguchi analysis identified normal load as the dominant factor governing wear variation (~68% contribution), followed by sliding speed (~17%), while nanoclay content contributed ~5%. An energy-based wear model showed improved correlation with experimental wear volume (R² ≈ 0.93) compared to a classical Archard-type formulation. ML prediction using a random forest model with leave-one-out cross-validation achieved an R² ≈ 0.64 for SWR. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) analyses confirmed a transition from severe abrasive wear in neat epoxy to stable tribofilm formation at 0.25 wt.% nanoclay, followed by heterogeneous debris-mediated wear at higher filler content. The observed reduction in wear loss and frictional energy dissipation supports sustainable materials innovation aligned with SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production), while improved operational efficiency is consistent with SDG 7 (Affordable and Clean Energy). Full article

The search for new antibacterial agents is an important task due to the emergence of resistance to widely used drugs. Bromine-, chlorine-, and nitro-substituted phenyl ring azomethines with long alkyl chains (C₁₂, C₁₄, C₁₆, and C₁₈) were synthesized and characterized using several experimental methods (NMR and IR spectroscopy, elemental analysis, mass spectrometry). Antibacterial and antifungal activity was tested on several cultures; the synthesized compounds show activity at the level of some commercial antiseptics. Lipophilicity (an important descriptor for predicting biological properties) of the experimentally synthesized and isomeric molecules was determined by three different approaches: quantum chemistry, machine learning (GraphormerLogP model), and an atom contribution model (RDKit library). The quantum-chemical method can account for any spatial arrangements and can be considered the most accurate of the approaches used, but it requires significant computational time. The atom contribution model is the fastest of the methods used, but it gives underestimated results, and different isomers have exactly the same values, in contrast to the quantum chemistry results. Machine learning-based methods (GraphormerLogP) demonstrate acceptable accuracy, sensitivity to isomerism, and orders-of-magnitude higher throughput, making them an optimal tool for high-throughput screening. Full article

A promising route for removing antibiotics such as penicillin from wastewater is photocatalytic degradation under UV irradiation using TiO₂ nanoparticles. However, the microscopic mechanisms governing the initial degradation steps remain poorly understood. In particular, it is still unclear whether degradation preferentially occurs in solution or upon adsorption on the oxide surface, and which molecular sites are most vulnerable to attack in solution compared to those activated on the catalyst. In this work, we introduce a unified density functional theory approach that treats penicillin V (phenoxymethylpenicillin) consistently, both isolated in solution and adsorbed on an anatase TiO₂ nanocluster, enabling a direct comparison between solution-phase and surface-mediated degradation pathways. Within this framework, we analyze the adsorption configurations, energy-level alignment, charge-transfer pathways, UV-Vis absorption properties, local reactivity descriptors, and the initial steps leading to bond breaking. The results show that the direct photoexcitation of PenV followed by electron transfer to the oxide is less likely, due to the high energy of the pollutant’s excited states. In contrast, degradation initiated by the transfer of photogenerated holes from the catalyst to the adsorbed antibiotic appears more probable, driven by the smaller energetic offset and by the hybridization between molecular and oxide states. Overall, adsorption on the oxide surface appears to be more conducive to degradation, with the carbon atom in the β-lactam ring consistently identified as a susceptible site for attack across different environments. Full article

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔE_int ≈ −22.6 to −31.1 kcal·mol⁻¹), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔE_gap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity. Full article

This study investigates the synergistic effects of Cr–Zr and Mn–Zr additions on the microstructural evolution and mechanical properties of Al–6 wt.%Cu alloys. Alloys were designed with solute concentrations positioned below, near, and above their maximum solubility limits, and were evaluated under as-cast, T4, and T6 heat treatment conditions. Mechanical testing revealed distinct behavioral trends depending on the heat treatment: the T4 heat treatment condition generally exhibited superior hardness and yield strength, whereas the T6 heat treatment condition resulted in a slight reduction in hardness but facilitated a significant recovery in tensile strength and structural stability, particularly in alloys designed near the solubility limit. To elucidate the crystallographic origins of these mechanical variations, X-ray diffraction analysis was conducted to monitor changes in lattice parameters, dislocation density, and micro-strain. The results showed that T4 heat treatment induced lattice contraction and a decrease in dislocation density, suggesting that the high strength under T4 heat treatment conditions arises from lattice distortion caused by supersaturated solute atoms. Conversely, T6 aging led to lattice relaxation approaching that of pure aluminum, yet simultaneously triggered a re-accumulation of dislocation density and micro-strain due to the coherency strain fields surrounding precipitates, which effectively impede dislocation motion. Therefore, rather than proposing a single, definitive optimization condition, this study aims to secure foundational data regarding the correlation between these microstructural descriptors and mechanical behavior, providing a guideline for balancing the strengthening contributions in transition metal-modified Al–Cu alloys. Full article

Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an integrated approach that simultaneously quantifies morphology and viscoelasticity in the human osteosarcoma cell line MG-63. Stress–relaxation experiments and optical imaging of the same cells were performed using a custom-built system that couples Atomic Force Microscopy (AFM) with an inverted optical microscope. Morphometric parameters were extracted from cell contours, while viscoelastic properties were obtained by fitting AFM data to the Fractional Kelvin (FK) and Fractional Zener (FZ) models. Among the morphological descriptors, the Shape Complexity (SC) was proposed. It is derived from the Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which captures fine-scale contour features overlooked by conventional metrics. Experimental results show that, in MG-63 cells, higher SC values are associated with greater stiffness, indicating a correlation between cell shape complexity and cell stiffness. Furthermore, loading-rate analysis shows that the FZ model captures strain-rate-dependent stiffening more effectively than the FK model. This methodology provides a first approach to jointly analyzing quantitative morphological parameters and mechanical properties, underlining the importance of combined studies to achieve a comprehensive understanding of cell behavior. Full article

Electrochemical CO₂ reduction (CO₂RR) is a promising route to transform a major greenhouse gas into value-added fuels and chemicals. However, its deployment is still hindered by the sluggish activation of CO₂, poor selectivity toward multielectron products, and competition with the hydrogen evolution reaction (HER). Single-atom catalysts (SACs) have emerged as powerful materials to address these challenges because they combine maximal metal utilization with well-defined coordination environments whose electronic structure can be precisely tuned through metal–support interactions. This minireview summarizes current understanding of how structural, electronic, and chemical features of SAC supports (e.g., porosity, heteroatom doping, vacancies, and surface functionalization) govern the adsorption and conversion of key CO₂RR intermediates and thus control product distributions from CO to CH₄, CH₃OH and C₂⁺ species. Particular emphasis is placed on selectivity descriptors (e.g., coordination number, d-band position, binding energies of *COOH and *OCHO) and on rational design strategies that exploit curvature, microenvironment engineering, and electronic metal–support interactions to direct the reaction along desired pathways. Representative SAC systems based primarily on N-doped carbons, complemented by selected examples on oxides and MXenes are discussed in terms of Faradaic efficiency (FE), current density and operational stability under practically relevant conditions. Finally, the review highlights remaining bottlenecks and outlines future directions, including operando spectroscopy and data-driven analysis of dynamic single-site ensembles, machine-learning-assisted DFT screening, scalable mechanochemical synthesis, and integration of SACs into industrially viable electrolyzers for carbon-neutral chemical production. Full article

The objective of this study was to use Density Functional Theory (DFT) calculations to examine how boron doping modulates the electronic properties of graphene quantum dots (GQDs) and their interaction with the Hg²⁺ ion. Boron doping decreases the HOMO-LUMO gap and increases the GQD’s electrophilic character, facilitating charge transfer to the metal ion. The adsorption energy results were negative, indicating electronic stabilization of the combined systems, without implying thermodynamic favorability, with the GQD@3B_Hg²⁺ system being the strongest at −349.52 kcal/mol. The analysis of global parameters (chemical descriptors) and the study of non-covalent interactions (NCIs) supported the affinity of Hg²⁺ for doped surfaces, showing that the presence of a single boron atom contributes to clear attractive interactions. In general, configurations doped with 1 or 2 boron atoms exhibit satisfactory performance, demonstrating that boron doping effectively modulates the reactivity and adsorption properties of GQD for efficient Hg²⁺ capture. Full article

The accurate determination of bond lengths is fundamental to understanding molecular geometry and the physicochemical behavior of chemical compounds. However, obtaining these measurements is often challenging, as both experimental techniques and advanced quantum-chemical methods are complex, computationally demanding, and costly to apply across diverse molecular systems. In this work, we present a novel graph-theoretical model for predicting bond lengths in flavonoid molecules based on molecular descriptors derived from atomic and topological parameters. By integrating atomic electronegativity with graph-based descriptors, such as the weighted second-order neighborhood, the proposed model predicts the bond lengths of luteolin with a coefficient of determination of $R^2=0.990$. This approach offers a computationally efficient and highly accurate alternative to conventional experimental and theoretical methods, providing a practical framework for bond length estimation when experimental data are unavailable. Full article