Matic Poberžnik

Co substitution in barium hexaferrite nanoplatelets stabilizes a previously unreported T′-hexaferrite phase with Co-site vacancies. Combined STEM and first-principles modelling reveal its defect structure and thermodynamic stability.

Molecular-level interactions of naturally occurring catechol derivatives with ferrite surfaces were investigated using permanently magnetic barium hexaferrite nanoplatelets and superparamagnetic iron oxide nanoparticles. Catechol (1,2-benzenediol) and guaiacol (2-methoxyphenol) were selected as representative aromatic ligands relevant to catechol- and guaiacyl-containing natural compounds. First-principles calculations indicated facile proton transfer from the ligands to surface oxygen or hydroxyl sites and predicted strong H-bonding between the resulting surface-bound species and neighbouring surface groups. Consistent with these predictions, spectroscopic and thermal analyses together with zeta-potential measurements support a surface-complexation mechanism in which the complexes are stabilized by hydrogen bonds, also involving the ortho-phenol or methoxy group and an adjacent surface hydroxyl group. Ligand adsorption did not significantly affect the magnetic behaviour of either ferrite. Both nanoparticle systems remained chemically stable after the catechol adsorption at pH 10, while barium hexaferrite nanoplatelets were also stable under near-neutral conditions. In contrast, catechol derivatives accelerated the decomposition of barium hexaferrite nanoplatelets at pH 3. Moreover, barium hexaferrite nanoplatelets catalyzed the oxidation of catechols by molecular oxygen in aqueous dispersions. These results link proton transfer and hydrogen-bond-assisted surface complexation with the stability of ferrite nanoparticles and reactivity toward catechol ligands in aqueous environments.

Local atomic displacements underpin ferroelectric polarization in perovskites such as (K0.5Na0.5)NbO3 (KNN), yet experimentally observed niobium (Nb) off-centering exceeds predictions from structural models. Here, we employ atomic-resolution scanning transmission electron microscopy (STEM), quantitative four-dimensional (4D)-STEM charge-density mapping, and density functional theory (DFT) calculations to directly measure Nb displacements and identify intrinsic point defects, including oxygen and alkali vacancies. Our results show that these defects locally perturb Nb off-centering by tens of picometers, correlating with increased local polarization. Integrated differential phase contrast (iDPC) imaging confirms the presence of defects, while DFT calculations demonstrate that oxygen vacancies alone locally perturb and amplify Nb off-centering, thereby influencing polarization fields. These findings suggest that intrinsic point defects significantly contribute to the enhanced local polarization observed in KNN, providing insight into defect-stabilized polar order in lead-free ferroelectric materials.

Local atomic displacements underpin ferroelectric polarization in perovskites such as (K0.5Na0.5)NbO3 (KNN), yet experimentally observed niobium (Nb) off-centering exceeds predictions from structural models. Here, we employ atomic-resolution scanning transmission electron microscopy (STEM), quantitative four-dimensional (4D)-STEM charge-density mapping, and density functional theory (DFT) calculations to directly measure Nb displacements and identify intrinsic point defects, including oxygen and alkali vacancies. Our results show that these defects locally perturb Nb off-centering by tens of picometers, correlating with increased local polarization. Integrated differential phase contrast (iDPC) imaging confirms the presence of defects, while DFT calculations demonstrate that oxygen vacancies alone locally perturb and amplify Nb off-centering, thereby influencing polarization fields. These findings suggest that intrinsic point defects significantly contribute to the enhanced local polarization observed in KNN, providing insight into defect-stabilized polar order in lead-free ferroelectric materials.

An algorithm for generating a uniform distribution of points on a hypersphere is presented. For a given dimension and a specified number of points, it produces a well-separated set of points (a spherical code) on the unit hypersphere. The algorithm can also account for possible symmetries in the hyperspace. Since there is no analytical solution to this problem, a numerical approach is proposed. To optimize computational efficiency, the algorithm first performs a quasi-optimal initialization of the point positions by proposing an analytic fit of the inverse cumulative distribution function. These positions are then recursively updated by minimizing their Riesz interaction energy under the imposed symmetry constraints, followed by a refinement of the individual scalar products. The method is implemented in a Fortran code that is first applied without symmetries to reproduce known spherical codes, and in some cases, even discovers improved ones. Examples incorporating symmetries, permutations, and fixed vector components are also provided to demonstrate the algorithm’s versatility.

A key aspect in the design of mixed polar-organic layers and metallic or semiconducting devices is the dielectric constant of the organic aggregate, which modulates the surface work function and band alignments. In simple electrostatic models, a monolayer is treated as an array of point dipoles whose depolarization depends only on an effective molecular polarizability. However, the absence of a unified framework in quantum-chemical computational studies leaves the reliability of the point dipole model uncertain. In this work, we demonstrate the breakdown of the point dipole approximation for highly packed aggregates and propose an alternative heuristic model that relies on a second molecular parameter: the molecular size. The performance of this approach is validated through comparison with computational methods based on Density Functional Theory (DFT) and second-order Møller-Plesset perturbation theory (MP2) calculations. Our extended dipole model provides robust estimates of depolarization effects, offering a rapid prescreening tool for selecting polar organic candidates in surface functionalization.

Stable ferrofluids of permanently magnetic nanoplatelets of barium hexaferrite (BHF NPLs) in 1-hexanol were obtained using phosphonic acid- and phosphonate ester-based polar ligands. These ligands with the different electron-withdrawing groups and alkyl chain lengths of the terminal chain and linker were synthesized. Their attachment to the surface of the BHF NPLs was studied for various conditions and followed by a combination of spectroscopic techniques, thermogravimetry, and electrokinetic measurements. The results confirmed the theoretically predicted surface condensation of the ligands onto the BHF NPLs surfaces at 120 °C in 1-hexanol, whereas at lower temperatures or in more polar solvents the ligands were mostly physisorbed. The NPL hybrids with chemisorbed ligands having surface densities of at least 0.4 molecules/nm2 formed stable ferrofluids in 1-hexanol. The hybridization of the BHF NPLs via the condensation reaction in 1-hexanol creates sufficient steric-solvation repulsion to overcome the magnetic dipolar attraction between the NPLs and stabilizes the NPLs in 1-hexanol. Due to the relatively low polarity of 1-hexanol, the ligands remain protonated and the ferrofluids have negligible electric conductivity. In addition, an increase in the saturation magnetization of such hybrid BHF NPLs was correlated with their superior sensitivity to a magnetic field compared to the core NPLs. The ferrofluids can be exploited for the development of novel magneto-optic sensors that can operate under an electric field.

Saddle points on high-dimensional potential energy surfaces (PES) play a determining role in the activated dynamics of molecules and materials. Building on approaches dating back more than 50 years, many open-ended transition-state search methods have been developed to follow the direction of negative curvature from a local minimum to an adjacent first-order saddle point. Despite the mathematical justification, these methods can display a high failure rate: using small deformation steps, up to 80% of the explorations can end up in a convex region of the PES, where all directions of negative curvature vanish, while if the deformation is aggressive, a similar fraction of attempts lead to saddle points that are not directly connected to the initial minimum. In high-dimension PES, these reproducible failures were thought to only increase the overall computational cost, without having any effect on the methods’ capacity to find all saddle points surrounding a minimum. Using activation-relaxation technique nouveau (ARTn), we characterize the nature of the PES around minima, considerably expanding on previous knowledge. We show that convex regions can lie on activation pathways and that not exploring beyond them can introduce significant bias in the saddle-point search. We introduce an efficient approach for traversing the convex regions, almost eliminating exploration failures, while multiplying by almost 10 the number of identified unique and connected saddle points as compared to the standard ARTn, thus underlining the importance of correctly handling convex regions for completeness of saddle point explorations.