Showing papers by "Fritz Haber Institute of the Max Planck Society published in 2017"

Machine learning in materials informatics: recent applications and prospects

Abstract: Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Understanding activity and selectivity of metal-nitrogen-doped carbon catalysts for electrochemical reduction of CO 2

Abstract: Direct electrochemical reduction of CO2 to fuels and chemicals using renewable electricity has attracted significant attention partly due to the fundamental challenges related to reactivity and selectivity, and partly due to its importance for industrial CO2-consuming gas diffusion cathodes. Here, we present advances in the understanding of trends in the CO2 to CO electrocatalysis of metal- and nitrogen-doped porous carbons containing catalytically active M–N x moieties (M = Mn, Fe, Co, Ni, Cu). We investigate their intrinsic catalytic reactivity, CO turnover frequencies, CO faradaic efficiencies and demonstrate that Fe–N–C and especially Ni–N–C catalysts rival Au- and Ag-based catalysts. We model the catalytically active M–N x moieties using density functional theory and correlate the theoretical binding energies with the experiments to give reactivity-selectivity descriptors. This gives an atomic-scale mechanistic understanding of potential-dependent CO and hydrocarbon selectivity from the M–N x moieties and it provides predictive guidelines for the rational design of selective carbon-based CO2 reduction catalysts. Inexpensive and selective electrocatalysts for CO2 reduction hold promise for sustainable fuel production. Here, the authors report N-coordinated, non-noble metal-doped porous carbons as efficient and selective electrocatalysts for CO2 to CO conversion.

Machine learning of accurate energy-conserving molecular force fields

Abstract: Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol-1 for energies and 1 kcal mol-1 A-1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

SchNet - a deep learning architecture for molecules and materials

Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics.

High performance platinum single atom electrocatalyst for oxygen reduction reaction

Abstract: For the large-scale sustainable implementation of polymer electrolyte membrane fuel cells in vehicles, high-performance electrocatalysts with low platinum consumption are desirable for use as cathode material during the oxygen reduction reaction in fuel cells. Here we report a carbon black-supported cost-effective, efficient and durable platinum single-atom electrocatalyst with carbon monoxide/methanol tolerance for the cathodic oxygen reduction reaction. The acidic single-cell with such a catalyst as cathode delivers high performance, with power density up to 680 mW cm-2 at 80 °C with a low platinum loading of 0.09 mgPt cm-2, corresponding to a platinum utilization of 0.13 gPt kW-1 in the fuel cell. Good fuel cell durability is also observed. Theoretical calculations reveal that the main effective sites on such platinum single-atom electrocatalysts are single-pyridinic-nitrogen-atom-anchored single-platinum-atom centres, which are tolerant to carbon monoxide/methanol, but highly active for the oxygen reduction reaction.

First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications.

Abstract: Noncovalent van der Waals (vdW) or dispersion forces are ubiquitous in nature and influence the structure, stability, dynamics, and function of molecules and materials throughout chemistry, biology, physics, and materials science. These forces are quantum mechanical in origin and arise from electrostatic interactions between fluctuations in the electronic charge density. Here, we explore the conceptual and mathematical ingredients required for an exact treatment of vdW interactions, and present a systematic and unified framework for classifying the current first-principles vdW methods based on the adiabatic-connection fluctuation–dissipation (ACFD) theorem (namely the Rutgers–Chalmers vdW-DF, Vydrov–Van Voorhis (VV), exchange-hole dipole moment (XDM), Tkatchenko–Scheffler (TS), many-body dispersion (MBD), and random-phase approximation (RPA) approaches). Particular attention is paid to the intriguing nature of many-body vdW interactions, whose fundamental relevance has recently been highlighted in several...

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Abstract: Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

In situ observation of reactive oxygen species forming on oxygen-evolving iridium surfaces

Abstract: Water splitting performed in acidic media relies on the exceptional performance of iridium-based materials to catalyze the oxygen evolution reaction (OER). In the present work, we use in situ X-ray photoemission and absorption spectroscopy to resolve the long-standing debate about surface species present in iridium-based catalysts during the OER. We find that the surface of an initially metallic iridium model electrode converts into a mixed-valent, conductive iridium oxide matrix during the OER, which contains OII− and electrophilic OI− species. We observe a positive correlation between the OI− concentration and the evolved oxygen, suggesting that these electrophilic oxygen sites may be involved in catalyzing the OER. We can understand this observation by analogy with photosystem II; their electrophilicity renders the OI− species active in O–O bond formation, i.e. the likely potential- and rate-determining step of the OER. The ability of amorphous iridium oxyhydroxides to easily host such reactive, electrophilic species can explain their superior performance when compared to plain iridium metal or crystalline rutile-type IrO2.

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Abstract: Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.

Carbocatalysis in Liquid-Phase Reactions.

Abstract: Metal-free catalysis has gained broad interest for sustainable chemistry. Carbocatalysis is one green option for catalytic transformation in the gas phase as well as in the liquid phase. This is documented by the numerous reports on gas phase dehydrogenation and selective oxidation where carbon can be successful alternatives to metal oxide systems. Carbocatalysis for liquid phase reactions, especially for organic synthesis, is an emerging research discipline and has been under rapid development in recent years. This review provides a critical analysis on the state-of-the-art carbocatalysts for liquid phase reactions, with the focus on the underlying mechanisms, the advantages and limitations when only carbon, without any intentional metal species (supported systems), is used to catalyze some traditional liquid phase reactions.

“The Easier the Better” Preparation of Efficient Photocatalysts—Metastable Poly(heptazine imide) Salts

Abstract: Cost-efficient, visible-light-driven hydrogen production from water is an attractive potential source of clean, sustainable fuel. Here, it is shown that thermal solid state reactions of traditional carbon nitride precursors (cyanamide, melamine) with NaCl, KCl, or CsCl are a cheap and straightforward way to prepare poly(heptazine imide) alkali metal salts, whose thermodynamic stability decreases upon the increase of the metal atom size. The chemical structure of the prepared salts is confirmed by the results of X-ray photoelectron and infrared spectroscopies, powder X-ray diffraction and electron microscopy studies, and, in the case of sodium poly(heptazine imide), additionally by atomic pair distribution function analysis and 2D powder X-ray diffraction pattern simulations. In contrast, reactions with LiCl yield thermodynamically stable poly(triazine imides). Owing to the metastability and high structural order, the obtained heptazine imide salts are found to be highly active photocatalysts in Rhodamine B and 4-chlorophenol degradation, and Pt-assisted sacrificial water reduction reactions under visible light irradiation. The measured hydrogen evolution rates are up to four times higher than those provided by a benchmark photocatalyst, mesoporous graphitic carbon nitride. Moreover, the products are able to photocatalytically reduce water with considerable reaction rates, even when glycerol is used as a sacrificial hole scavenger.

Enhanced Carbon Dioxide Electroreduction to Carbon Monoxide over Defect-Rich Plasma-Activated Silver Catalysts

Abstract: Efficient, stable catalysts with high selectivity for a single product are essential to making the electroreduction of CO2 a viable route to the synthesis of industrial feedstocks and fuels. We reveal how a plasma oxidation pre-treatment can lead to an enhanced content of low-coordinated active sites which dramatically lower the overpotential and increase the activity of CO2 electroreduction to CO. At -0.6 V vs. RHE, more than 90% Faradaic efficiency towards CO could be achieved on a pre-oxidized silver foil. While transmission electron microscopy and operando X-ray absorption spectroscopy showed that oxygen species can survive in the bulk of the catalyst during the reaction, in situ X-ray photoelectron spectroscopy showed that the surface is metallic under reaction conditions. DFT calculations show how the defect-rich surface of the plasma-oxidized silver foils in the presence of local electric fields results in a drastic decrease in the overpotential for the electroreduction of CO2.

Potassium Poly(heptazine imides) from Aminotetrazoles: Shifting Band Gaps of Carbon Nitride-like Materials for More Efficient Solar Hydrogen and Oxygen Evolution

Abstract: Potassium poly(heptazine imide) (PHI) is a photocatalytically active carbon nitride material that was recently prepared from substituted 1,2,4-triazoles Here we show that the more acidic precursors, such as commercially available 5-aminotetrazole, upon pyrolysis in LiCl/KCl salt melt yield PHI with the greatly improved structural order and thermodynamic stability Tetrazole-derived PHIs feature long range crystallinities and unconventionally small layer-stacking distances leading to the altered electronic band structures as shown by Mott-Schottky analyses Under the optimized synthesis conditions, visible light driven hydrogen evolution rates reach twice the rate provided by the previous golden standard, mesoporous graphitic carbon nitride having much higher surface area More interestingly, the up to 07 V higher valence band potential of crystalline PHI compared to the ordinary carbon nitrides makes it an efficient water oxidation photocatalyst which works even in the absence of any metal-based co-catalysts under visible light To our knowledge, this is the first case of a metal free oxygen liberation from water as such

Interfacial charge distributions in carbon-supported palladium catalysts

Abstract: Controlling the charge transfer between a semiconducting catalyst carrier and the supported transition metal active phase represents an elite strategy for fine turning the electronic structure of the catalytic centers, hence their activity and selectivity. These phenomena have been theoretically and experimentally elucidated for oxide supports but remain poorly understood for carbons due to their complex nanoscale structure. Here, we combine advanced spectroscopy and microscopy on model Pd/C samples to decouple the electronic and surface chemistry effects on catalytic performance. Our investigations reveal trends between the charge distribution at the palladium–carbon interface and the metal’s selectivity for hydrogenation of multifunctional chemicals. These electronic effects are strong enough to affect the performance of large (~5 nm) Pd particles. Our results also demonstrate how simple thermal treatments can be used to tune the interfacial charge distribution, hereby providing a strategy to rationally design carbon-supported catalysts. Control over charge transfer in carbon-supported metal nanoparticles is essential for designing new catalysts. Here, the authors show that thermal treatments effectively tune the interfacial charge distribution in carbon-supported palladium catalysts with consequential changes in hydrogenation performance.

Generalized 4 × 4 matrix formalism for light propagation in anisotropic stratified media: study of surface phonon polaritons in polar dielectric heterostructures

Abstract: We present a generalized 4×4 matrix formalism for the description of light propagation in birefringent stratified media. In contrast to previous work, our algorithm is capable of treating arbitrarily anisotropic or isotropic, absorbing or non-absorbing materials and is free of discontinuous solutions. We calculate the reflection and transmission coefficients and derive equations for the electric field distribution for any number of layers. The algorithm is easily comprehensible and can be straightforwardly implemented in a computer program. To demonstrate the capabilities of the approach, we calculate the reflectivities, electric field distributions, and dispersion curves for surface phonon polaritons excited in the Otto geometry for selected model systems, where we observe several distinct phenomena ranging from critical coupling to mode splitting, and surface phonon polaritons in hyperbolic media.

First-principles modeling of molecular crystals: structures and stabilities, temperature and pressure

Abstract: The understanding of the structure, stability, and response properties of molecular crystals at finite temperature and pressure is crucial for the field of crystal engineering and their application. For a long time, the field of crystal-structure prediction and modeling of molecular crystals has been dominated by classical mechanistic force-field methods. However, due to increasing computational power and the development of more sophisticated quantum-mechanical approximations, first-principles approaches based on density functional theory can now be applied to practically relevant molecular crystals. The broad transferability of first-principles methods is especially imperative for polymorphic molecular crystals. This review highlights the current status of modeling molecular crystals from first principles. We give an overview of current state-of-the-art approaches and discuss in detail the main challenges and necessary approximations. So far, the main focus in this field has been on calculating stabilities and structures without considering thermal contributions. We discuss techniques that allow one to include thermal effects at a first-principles level in the harmonic or quasi-harmonic approximation, and that are already applicable to realistic systems, or will be in the near future. Furthermore, this review also discusses how to calculate vibrational and elastic properties. Finally, we present a perspective on future uses of first-principles calculations for modeling molecular crystals and summarize the many remaining challenges in this field. WIREs Comput Mol Sci 2017, 7:e1294. doi: 10.1002/wcms.1294 For further resources related to this article, please visit the WIREs website.

Glycan Analysis by Ion Mobility-Mass Spectrometry

Abstract: Carbohydrates form one of the major classes of biological macromolecules in living organisms. To investigate their properties and function, an in-depth knowledge of their underlying structure is essential. However, the inherent structural complexity of glycans represents a major challenge. Carbohydrates are often branched and exhibit diverse regio- and stereochemistry. This in turn leads to a vast number of possible isomers, which are difficult to distinguish by using established analytical tools. In the last decade, ion mobility-mass spectrometry, a technique that separates ions based on their mass, charge, size, and shape, has emerged as a powerful alternative for isomer distinction. This Minireview highlights recent advances in ion mobility-mass spectrometry of complex carbohydrates and discusses its role in future analysis workflows.

Glycan Fingerprinting via Cold-Ion Infrared Spectroscopy

Abstract: The diversity of stereochemical isomers present in glycans and glycoconjugates poses a formidable challenge for comprehensive structural analysis Typically, sophisticated mass spectrometry (MS)-based techniques are used in combination with chromatography or ion-mobility separation However, coexisting structurally similar isomers often render an unambiguous identification impossible Other powerful techniques such as gas-phase infrared (IR) spectroscopy have been limited to smaller glycans, since conformational flexibility and thermal activation during the measurement result in poor spectral resolution This limitation can be overcome by using cold-ion spectroscopy The vibrational fingerprints of cold oligosaccharide ions exhibit a wealth of well-resolved absorption features that are diagnostic for minute structural variations The unprecedented resolution of cold-ion spectroscopy coupled with tandem MS may render this the key technology to unravel complex glycomes

Ab Initio Green-Kubo Approach for the Thermal Conductivity of Solids.

Abstract: We herein present a first-principles formulation of the Green-Kubo method that allows the accurate assessment of the phonon thermal conductivity of solid semiconductors and insulators in equilibrium ab initio molecular dynamics calculations. Using the virial for the nuclei, we propose a unique ab initio definition of the heat flux. Accurate size and time convergence are achieved within moderate computational effort by a robust, asymptotically exact extrapolation scheme. We demonstrate the capabilities of the technique by investigating the thermal conductivity of extreme high and low heat conducting materials, namely, Si (diamond structure) and tetragonal ${\mathrm{ZrO}}_{2}$.

Surface Chemistry of Perovskite-Type Electrodes During High Temperature CO2 Electrolysis Investigated by Operando Photoelectron Spectroscopy

Abstract: Any substantial move of energy sources from fossil fuels to renewable resources requires large scale storage of excess energy, for example, via power to fuel processes In this respect electrochemical reduction of CO2 may become very important, since it offers a method of sustainable CO production, which is a crucial prerequisite for synthesis of sustainable fuels Carbon dioxide reduction in solid oxide electrolysis cells (SOECs) is particularly promising owing to the high operating temperature, which leads to both improved thermodynamics and fast kinetics Additionally, compared to purely chemical CO formation on oxide catalysts, SOECs have the outstanding advantage that the catalytically active oxygen vacancies are continuously formed at the counter electrode, and move to the working electrode where they reactivate the oxide surface without the need of a preceding chemical (eg, by H2) or thermal reduction step In the present work, the surface chemistry of (La,Sr)FeO3−δ and (La,Sr)CrO3−δ based perovs

Femtosecond Spin Current Pulses Generated by the Nonthermal Spin-Dependent Seebeck Effect and Interacting with Ferromagnets in Spin Valves.

Abstract: Using the sensitivity of optical second harmonic generation to currents, we demonstrate the generation of 250-fs long spin current pulses in $\mathrm{Fe}/\mathrm{Au}/\mathrm{Fe}/\mathrm{MgO}(001)$ spin valves. The temporal profile of these pulses indicates ballistic transport of hot electrons across a sub-100 nm Au layer. The pulse duration is primarily determined by the thermalization time of laser-excited hot carriers in Fe. Considering the calculated spin-dependent $\mathrm{Fe}/\mathrm{Au}$ interface transmittance we conclude that a nonthermal spin-dependent Seebeck effect is responsible for the generation of ultrashort spin current pulses. The demonstrated rotation of spin polarization of hot electrons upon interaction with noncollinear magnetization at $\mathrm{Au}/\mathrm{Fe}$ interfaces holds high potential for future spintronic devices.

Insightful classification of crystal structures using deep learning

Abstract: Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 100 000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

Abstract: With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops “converters” for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

Engineering Interface with One-Dimensional Co3O4 Nanostructure in Catalytic Membrane Electrode: Toward an Advanced Electrocatalyst for Alcohol Oxidation.

Abstract: Electrochemical oxidation has attracted vast interest as a promising alternative to traditional chemical processes in fine chemical synthesis owing to its fast and sustainable features. An electrocatalytic membrane reactor (ECMR) with a three-dimensional (3D) electrode has been successfully designed for the selective oxidation of alcohols with high current efficiency to the corresponding acids or ketones. The anode electrode was fabricated by the in situ loading of one-dimensional (1D) Co3O4 nanowires (NWs) on the conductive porous Ti membrane (Co3O4 NWs/Ti) via the combination of a facile hydrothermal synthesis and subsequent thermal treatment. The electrocatalytic oxidation (ECO) results of alcohols exhibited superior catalytic performance with a higher current efficiency on the Co3O4 NWs/Ti membrane compared with those of Co3O4 nanoparticles on the Ti membrane (Co3O4 NPs/Ti). Even under low reaction temperatures such as 0 °C, it still displayed a very high ECO activity for alcohol oxidation in the ECMR...

Automated glycan assembly using the Glyconeer 2.1 synthesizer.

Abstract: Reliable and rapid access to defined biopolymers by automated DNA and peptide synthesis has fundamentally altered biological research and medical practice. Similarly, the procurement of defined glycans is key to establishing structure–activity relationships and thereby progress in the glycosciences. Here, we describe the rapid assembly of oligosaccharides using the commercially available Glyconeer 2.1 automated glycan synthesizer, monosaccharide building blocks, and a linker-functionalized polystyrene solid support. Purification and quality-control protocols for the oligosaccharide products have been standardized. Synthetic glycans prepared in this way are useful reagents as the basis for glycan arrays, diagnostics, and carbohydrate-based vaccines.

Structure and Fluxionality of B13+ Probed by Infrared Photodissociation Spectroscopy.

Abstract: We use cryogenic ion vibrational spectroscopy to characterize the structure and fluxionality of the magic number boron cluster B13+. The infrared photodissociation (IRPD) spectrum of the D2-tagged all-11B isotopologue of B13+ is reported in the spectral range from 435 to 1790 cm−1 and unambiguously assigned to a planar boron double wheel structure based on a comparison to simulated IR spectra of low energy isomers from density-functional-theory (DFT) computations. Born–Oppenheimer DFT molecular dynamics simulations show that B13+ exhibits internal quasi-rotation already at 100 K. Vibrational spectra derived from these simulations allow extracting the first spectroscopic evidence from the IRPD spectrum for the exceptional fluxionality of B13+.

Towards Organic Zeolites and Inclusion Catalysts: Heptazine Imide Salts Can Exchange Metal Cations in the Solid State

Abstract: Highly crystalline potassium (heptazine imides) were prepared by the thermal condensation of substituted 1,2,4-triazoles in eutectic salt melts. These semiconducting salts are already known to be highly active photocatalysts, for example, for the visible-light-driven generation of hydrogen from water. Herein, we show that within the solid-state structure, potassium ions can be exchanged to other metal ions while the crystal habitus is essentially preserved.

Nanoscale π-π stacked molecules are bound by collective charge fluctuations.

Abstract: Non-covalent π-π interactions are central to chemical and biological processes, yet the full understanding of their origin that would unite the simplicity of empirical approaches with the accuracy of quantum calculations is still missing. Here we employ a quantum-mechanical Hamiltonian model for van der Waals interactions, to demonstrate that intermolecular electron correlation in large supramolecular complexes at equilibrium distances is appropriately described by collective charge fluctuations. We visualize these fluctuations and provide connections both to orbital-based approaches to electron correlation, as well as to the simple London pairwise picture. The reported binding energies of ten supramolecular complexes obtained from the quantum-mechanical fluctuation model joined with density functional calculations are within 5% of the reference energies calculated with the diffusion quantum Monte-Carlo method. Our analysis suggests that π-π stacking in supramolecular complexes can be characterized by strong contributions to the binding energy from delocalized, collective charge fluctuations-in contrast to complexes with other types of bonding.

Fabrication of nanoscale NiO/Ni heterostructures as electrocatalysts for efficient methanol oxidation

Abstract: Nanoscale NiO/Ni heterostructures on oxygen-functionalized carbon nanotubes with low Ni loading (3–4 wt%) are fabricated by delicate thermal-annealing treatments, which are designed according to the temperature-programmed thermal analysis. Activity and stability tests demonstrate that NiO/Ni heterostructures with a stable Ni core inside an oxyhydroxide shell (in solution) exhibit enhanced stability and catalytic activity for methanol oxidation.