Alexander M. Imre

Quantitative low-energy electron diffraction [LEED I ( V )] is a powerful method for surface-structure determination, based on a direct comparison of I ( V ) data between experiment and computations for a structure model. As the diffraction intensities I are highly sensitive to subtle structural changes, local structure optimization is essential for assessing the validity of a structure model and finding the best-fit structure. Even when using the tensor-LEED approximation—which accelerates optimization by applying a perturbative treatment of small deviations from a reference structure—the optimization process is computationally expensive. In the new tensor-LEED implementation presented in this work, the problem of surface-structure optimization is reformulated using a tree-based data structure, which helps to avoid redundant function evaluations. Intensities are computed on the fly, eliminating limitations of previous algorithms using grid-based search with precomputed values. This enables the use of state-of-the-art optimization algorithms. Implemented in Python with the JAX library, the method provides access to gradients of the R factor and supports execution on graphics processing units (GPUs). These developments reduce the computing time by more than an order of magnitude.

Quantitative low-energy electron diffraction (LEED I ( V ) or LEED I ( E ) ), which evaluates the diffraction intensities I as a function of the electron energy, is a versatile technique for the study of surface structures. The technique is based on optimizing the agreement between experimental and calculated intensities. Today, the most commonly used measure of agreement is Pendry’s R factor R P . While R P has many advantages it also has severe shortcomings, as it is a noisy target function for optimization and very sensitive to small offsets of the intensity. Furthermore, R P = 0 , which is meant to imply perfect agreement between two I ( E ) curves, can also be achieved by qualitatively very different curves. We present a modified R factor R S , which can be used as a direct replacement for R P , but avoids these shortcomings. We also demonstrate that R S is as good as R P or better in steering the optimization to the correct result in the case of imperfections in the experimental data, while another common R factor, R ZJ (suggested by Zanazzi and Jona) is worse in this respect.

Low-energy electron diffraction (LEED) is a widely used technique in surface-science laboratories. Yet, it is rarely used to its full potential. The quantitative information about the surface structure, contained in the modulation of the intensities of the diffracted beams as a function of incident electron energy, LEED I ( V ) , is underutilized. To acquire these data, only minor adjustments would be required in most experimental setups, but existing analysis software is cumbersome to use and often computationally inefficient. The ViPErLEED (Vienna package for Erlangen LEED) project lowers these barriers, introducing a combined solution for user-friendly data acquisition, extraction, and computational analysis. These parts are discussed in three separate publications. Here, the focus is on the computational part of ViPErLEED, which performs highly automated LEED- I ( V ) calculations and structural optimizations. Minimal user input is required, and the functionality is significantly enhanced compared to existing solutions. Computation is performed by embedding the existing Erlangen tensor-LEED package (). ViPErLEED manages additional parallelization, monitors convergence, and processes all input and output. This makes LEED I ( V ) more accessible to new users while minimizing the potential for errors and the manual labor. Added functionalities include intelligent structure-dependent defaults for most calculation parameters, automatic detection of bulk and surface symmetries and their relationship, automated search procedures that preserve the symmetry and speed up convergence, adjustments to the code to handle larger systems than before, as well as parallelization and optimization. Modern file formats are used as input and output, and there is a direct interface to the atomic simulation environment (ASE) package. The software is implemented primarily in (version ≥ 3.7 ) and provided as an open-source package (GNU GPLv3 or any later version). A structure determination of the α -Fe 2 O 3 ( 1 1 ¯ 02 ) − ( 1 × 1 ) surface is presented as an example for the application of the software.

As part of the Vienna package for Erlangen LEED, low-energy electron diffraction (ViPErLEED) project, computer programs have been developed for facile and user-friendly data extraction from movies of LEED images. The programs make use of some concepts from astronomical image processing and analysis. As a first step, flat-field and dark-frame corrections reduce the effects of inhomogeneities of the camera and screen. In a second step, for identifying all diffraction maxima (“spots”), it is sufficient to manually mark and label a single spot or very few spots. Then the program can automatically identify all other spots and determine the distortions of the image. This forms the basis for automatic spot tracking (i.e., following the spots as they move across the LEED screen) and intensity measurement. Even for complex structures with hundreds to a few thousand diffraction beams, this step takes less than a minute. The package also includes a program for further processing of these I ( V ) curves (averaging of equivalent beams, manual and/or automatic selection, smoothing) as well as several utilities. The software is implemented as a set of plugins for the public-domain image processing program and provided as an open-source package.

The investigation of inhomogeneous surfaces, where various local structures co-exist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × m) Ti-rich surfaces at (110)-oriented SrTiO3 by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the heterogeneous SrTiO3(110)-(2×m) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. The approach could be extended to other complex metal oxides featuring large coexisting surface reconstructions.

In2O3-based catalysts have shown high activity and selectivity for CO2 hydrogenation to methanol; however, the origin of the high performance of In2O3 is still unclear. To elucidate the initial steps of CO2 hydrogenation over In2O3, we have combined X-ray photoelectron spectroscopy and density functional theory calculations to study the adsorption of CO2 on the In2O3(111) crystalline surface with different terminations, namely, the stoichiometric, reduced, and hydroxylated surface. The combined approach confirms that the reduction of the surface results in the formation of In adatoms and that water dissociates on the surface at room temperature. A comparison of the experimental spectra and the computed core-level shifts (using methanol and formic acid as benchmark molecules) suggests that CO2 adsorbs as a carbonate on all three surface terminations. We find that the adsorption of CO2 is hindered by hydroxyl groups on the hydroxylated surface.

Protein-based underwater adhesives of marine organisms exhibit extraordinary binding strength in high salinity based on utilizing a variety of molecular interaction mechanisms. These include acid-base interactions, bidentate bindings or complex hydrogen bonding interactions, and electrochemical manipulation of interfacial bonding. In this Perspective, we briefly review recent progress in the field, and we discuss how interfacial electrochemistry can vary interfacial forces by concerted tuning of surface charging, hydration forces, and tuning of the interfacial ion concentration. We further discuss open questions, controversial findings, and new paths into understanding and utilizing redox-proteins and derived polymers for enhancing underwater adhesion in a complex salt environment.

Catechol reaction mechanisms form the basis of marine mussel adhesion, allowing for bond formation and cross-linking in wet saline environments. To mimic mussel foot adhesion and develop new bioadhesive underwater glues, it is essential to understand and learn to control their redox activity as well as their chemical reactivity. Here, we study the electrochemical characteristics of functionalized catechols to further understand their reaction mechanisms and find a stable and controllable molecule that we subsequently integrate into a polymer to form a highly adhesive hydrogel. Contradictory to previous hypotheses, 3,4-dihydroxy-L-phenylalanine is shown to follow a Schiff-base reaction whereas dopamine shows an intramolecular ring formation. Dihydrocaffeic acid proved to be stable and was substituted onto a poly(allylamine) backbone and electrochemically cross-linked to form an adhesive hydrogel that was tested using a surface forces apparatus. The hydrogel’s compression and dehydration dependent adhesive strength have proven to be higher than in mussel foot proteins (mfp-3 and mfp-5). Controlling catechol reaction mechanisms and integrating them into stable electrochemically depositable macroscopic structures is an important step in designing new biological coatings and underwater and biomedical adhesives.

Function and properties at biologic as well as technological interfaces are controlled by a complex and concerted competition of specific and unspecific binding with ions and water in the electrolyte. It is not possible to date to directly estimate by experiment the interfacial binding energies of involved species in a consistent approach, thus limiting our understanding of how interactions in complex (physiologic) media are moderated. Here, we employ a model system utilizing polymers with end grafted amines interacting with a negatively charged mica surface. We measure interaction forces as a function of the molecule density and ion concentration in NaCl solutions. The measured adhesion decreases by about 90%, from 0.01 to 1 M electrolyte concentration. We further demonstrate by molecular resolution imaging how ions increasingly populate the binding surface at elevated concentrations, and are effectively competing with the functional group for a binding site. We demonstrate that a competing Langmuir isotherm model can describe this concentration-dependent competition. Further, based on this model we can quantitatively estimate ion binding energies, as well as binding energy relationships at a complex solid|liquid interface. Our approach enables the extraction of thermodynamic interaction energies and kinetic parameters of ionic species during monolayer level interactions at a solid|liquid interface, which to-date is impossible with other techniques.