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With growing interest in laboratory automation and high-throughput systems, the amount of generated experimental data is rapidly increasing while analysis methods still require many manual work hours from experts. This is prevalent in X-ray photoelectron spectroscopy (XPS), where quantification is a complex, time-consuming, and error-prone task. We therefore propose a neural network-based workflow to make this process more approachable. As training data availability ranges from insufficient to non-existent, our workflow creates a synthetic dataset containing XPS signals and corresponding area percentages based on binding energies supplied by the user. As a result, no previous measurements are needed. After training on the synthetic data, the neural network can predict area percentages of the known binding energies with high confidence. This workflow can therefore be adapted for XPS quantification tasks to filter significant data and supervise processes. Moreover, this enables non-experts to analyze spectra and can help experts to reduce focus on important spectra.

Within the framework of surface–adsorbate interactions relevant to chemical reactions of spent nuclear fuel, the study of actinide oxide systems remains one of the most challenging tasks at both the experimental and computational levels. Consequently, our understanding of the effect of their unique electronic configurations on surface reactions lags behind that of d-block oxides. To investigate the surface properties of this system, we present the first infrared spectroscopy analysis of carbon monoxide (CO) interaction with a monocrystalline actinide oxide, UO2(111). Using a monocrystalline form largely avoids issues related to super-stoichiometries (UO2+x) and makes the experimental data suitable for further theoretical studies. Our findings reveal that CO adsorbs molecularly and shows a pronounced blue shift of the vibrational frequency to 2160 cm−1 relative to the gas-phase value. Interpreted through density functional theory (DFT) at different levels of computation, results indicate that to accurately describe the interaction between the CO molecule and the surface, it is essential to consider hybrid functionals, the non-collinearity of uranium's local magnetic moments, and spin–orbit coupling. Moreover, an intense IR absorption band at 978 cm−1 emerged upon CO adsorption, tentatively attributed to the frequency shift of the O–U–O asymmetric stretch of the UO2(111) surface in the presence of adsorbed oxygen. This new band, together with the observation of the importance of the relativistic effects in determining the nature of the chemical bonding of CO, is poised to broaden our understanding of actinide surface reactions. [ABSTRACT FROM AUTHOR]

We investigate the electronic and vibrational properties of bottom-up synthesized aligned armchair graphene nanoribbons of N = 7 carbon atoms width periodically doped by substitutional boron atoms (B-7AGNRs). Using angle-resolved photoemission spectroscopy and density functional theory calculations, we find that the dopant-derived valence and conduction band states are notably hybridized with electronic states of Au substrate and spread in energy. The interaction with the substrate leaves the bands with pure carbon character rather unperturbed. This results in an identical effective mass of ≈0.2 m0 for the next-highest valence band compared with pristine 7AGNRs. We probe the phonons of B-7AGNRs by ultrahigh-vacuum (UHV) Raman spectroscopy and reveal the existence of characteristic splitting and red shifts in Raman modes due to the presence of substitutional boron atoms. Comparing the Raman spectra for three visible lasers (red, green, and blue), we find that interaction with gold suppresses the Raman signal from B-7AGNRs and the energy of the green laser (2.33 eV) is closer to the resonant E22 transition. The hybridized electronic structure of the B-7AGNR-Au interface is expected to improve electrical characteristics of contacts between graphene nanoribbon and Au. The Raman fingerprint allows the easy identification of B-7AGNRs, which is particularly useful for device fabrication.

The facet-dependent adsorption of CO on oxidized and reduced CeO2 single crystal surfaces is reviewed, with emphasis on the effect of CO coverage and the ability of state-of-the-art quantum-mechanical methods to provide reliable energies and an accurate description of the IR vibrational frequency of CO. Comparison with detailed, high-resolution experimental infrared reflection absorption spectroscopy data obtained for single crystal samples allows the assignment of the different CO vibrational bands observed on all three low-index ceria surfaces. Good agreement is achieved with the hybrid density functional theory approach with the HSE06 functional and with saturation coverage. It is shown that CO is very sensitive to the structure of cerium oxide surfaces and to the presence of oxygen vacancies. The combined theoretical-experimental approach offers new opportunities for a better characterization of ceria nanoparticles and for unraveling changes occurring during reactions involving CO at higher pressures. [ABSTRACT FROM AUTHOR]

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In photo‐induced charge separation, organic thin films with donor and acceptor chromophores are vital for uses such as artificial photosynthesis and photodetection. The main challenges include optimizing charge separation efficiency and identifying the ideal acceptor/donor ratio. Achieving this is difficult due to the variability in molecular configurations within these typically amorphous organic aggregates. Metal–organic frameworks (MOFs) provide a structured solution by enabling systematic design of donor/acceptor blends with adjustable ratios within a crystalline lattice. We demonstrate this approach by incorporating donor and acceptor naphthalenediimide (NDI) chromophores as linkers in a highly oriented, monolithic MOF thin film. By adjusting the NDI acceptor linker concentration during the layer‐by‐layer assembly of surface‐anchored MOF thin films (SURMOFs), we significantly enhanced charge separation efficiency. Surprisingly, the optimum acceptor concentration was only 3 %, achieving a forty‐fold increase in photodetection efficiency compared to baseline NDI donor‐based SURMOFs. This unexpected behaviour was clarified through theoretical analysis enabled by the well‐defined crystalline structure of the SURMOFs. Using density functional theory and kinetic Monte Carlo simulations, we identified two opposing effects from acceptors: the positive effect of suppressing undesirable charge carrier recombination is offset at high concentrations by a reduction in charge‐carrier mobility. [ABSTRACT FROM AUTHOR]

Metal–organic Frameworks (MOFs), especially as thin films, are increasingly recognized for their potential in device integration, notably in sensors and photo detectors. A critical factor in the performance of many MOF‐based devices is the quality of the MOF interfaces. Achieving MOF thin films with smooth surfaces and low defect densities is essential. Given the extensive parameter space governing MOF thin film deposition, the use of machine learning (ML) methods to optimize deposition conditions is highly beneficial. Combined with robotic fabrication, ML can more effectively explore this space than traditional methods, simultaneously varying multiple parameters to improve optimization efficiency. Importantly, ML can provide deeper insights into the synthesis of MOF thin films, an essential area of research. This study focuses on refining an HKUST‐1 SURMOF (surface‐mounted MOF) to achieve minimal surface roughness and high crystallinity, including a quantitative analysis of the importance of the various synthesis parameters. Using the SyCoFinder ML technique, thin film surface quality is markedly enhanced in just three generations created by a genetic algorithm, covering 30 distinct parameter sets. This method greatly reduces the need for extensive experimentation. Moreover, the results enhance the understanding of the vast synthesis parameter space in HKUST‐1 SURMOF growth and broaden the applications of MOF thin films in electronic and optoelectronic devices. [ABSTRACT FROM AUTHOR]

CeO2‐supported noble metal clusters are attractive catalytic materials for several applications. However, their atomic dispersion under oxidizing reaction conditions often leads to catalyst deactivation. In this study, the noble metal cluster formation threshold is rationally adjusted by using a mixed CeO2‐Al2O3 support. The preferential location of Pd on CeO2 islands leads to a high local surface noble metal concentration and promotes the in situ formation of small Pd clusters at a rather low noble metal loading (0.5 wt %), which are shown to be the active species for CO conversion at low temperatures. As elucidated by complementary in situ/operando techniques, the spatial separation of CeO2 islands on Al2O3 confines the mobility of Pd, preventing the full redispersion or the formation of larger noble metal particles and maintaining a high CO oxidation activity at low temperatures. In a broader perspective, this approach to more efficiently use the noble metal can be transferred to further systems and reactions in heterogeneous catalysis. [ABSTRACT FROM AUTHOR]

Thin films of crystalline solids with substantial free volume built from organic chromophores and metal secondary building units (SBUs) are promising for engineering new optoelectronic properties through control of interchromophore coupling. Zn‐based SBUs are especially relevant in this case because they avoid quenching the chromophore's luminescence. We find that layer‐by‐layer spin‐coating using Zn acetate dihydrate and benzene‐1,4‐dicarboxylic acid (H2BDC) and biphenyl‐4,4'‐dicarboxylic acid (H2BPDC) linkers readily produces crystalline thin films. However, analysis of the grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) data reveals the structures of these films vary significantly with the linker, and with the metal‐to‐linker molar ratio used for fabrication. Under equimolar conditions, H2BPDC creates a type of structure like that proposed for SURMOF‐2, whereas H2BDC generates a different metal‐hydroxide‐organic framework. Large excess of Zn2+ ions causes the growth of layered zinc hydroxides, irrespective of the linker used. Density functional theory (DFT) calculations provide structural models with minimum total energy that are consistent with the experimentally observed diffractograms. In the broader sense, this work illustrates the importance in this field of careful structure determination, e. g., by utilizing GIWAXS and DFT simulations to determine the structure of the obtained crystalline metal‐organic thin films, such that properties can be rationally engineered and explained. [ABSTRACT FROM AUTHOR]

Copyright of Angewandte Chemie is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The surface structure and chemical properties of Y‐stabilized zirconia (YSZ) have been subjects of intense debate over the past three decades. However, a thorough understanding of chemical processes occurring at YSZ powders faces significant challenges due to the absence of reliable reference data acquired for well‐controlled model systems. Here, we present results from polarization‐resolved infrared reflection absorption spectroscopy (IRRAS) obtained for differently oriented, Y‐doped ZrO2 single‐crystal surfaces after exposure to CO and D2O. The IRRAS data reveal that the polar YSZ(100) surface undergoes reconstruction, characterized by an unusual, red‐shifted CO band at 2132 cm−1. Density functional theory calculations allowed to relate this unexpected observation to under‐coordinated Zr4+ cations in the vicinity of doping‐induced O vacancies. This reconstruction leads to a strongly increased chemical reactivity and water spontaneously dissociates on YSZ(100). The latter, which is an important requirement for catalysing the water‐gas‐shift (WGS) reaction, is absent for YSZ(111), where only associative adsorption was observed. Together with a novel analysis Scheme these reference data allowed for an operando characterisation of YSZ powders using DRIFTS (diffuse reflectance infrared Fourier transform spectroscopy). These findings facilitate rational design and tuning of YSZ‐based powder materials for catalytic applications, in particular CO oxidation and the WGS reaction. [ABSTRACT FROM AUTHOR]

The basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state‐of‐the‐art materials research needs to consider the integration of research data management (RDM) workflows and, in the end, the implementation of process automation concepts for all parts of the main modules. Here, the state‐of‐the‐art methods of RDM in academia are described and a perspective on the future of digitalized molecular material systems workflows is given. The different elements of an integrated research data management strategy are described, and examples of automated processes are depicted. As such, the use of electronic lab notebooks for comprehensive documentation, the use of data‐integration and data‐conversion strategies, and the establishment of two platforms that enable the automated synthesis of chemical components for materials and the analysis of materials by electron microscopy, are highlighted. Two examples of beneficial effects of successful RDM strategies are presented, showing a sophisticated tool for data prediction based on machine learning and options for creating community‐driven databases by extracting and re‐using data from different scientific projects. [ABSTRACT FROM AUTHOR]

The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time‐consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high‐performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches. [ABSTRACT FROM AUTHOR]

Layer‐by‐layer synthesis of surface‐coordinated metal–organic frameworks (SURMOF) enables the assembly of asymmetric, dipolar linkers into non‐centrosymmetric pillar‐layered structures. Using appropriate substrate terminations can yield oriented growth with the dipoles aligned perpendicular to the surface. The aligned pillar linkers give rise to a built‐in electrostatic field. In addition, the non‐centrosymmetric structure of the SURMOF gives rise to intriguing nonlinear optical features, such as second harmonic generation. Previous research with methyl‐functionalized bipyridine pillar linkers have demonstrated that this approach works in principle, but so far the total degree of alignment is only very small. Herein, a multiscale modelling approach is presented for in‐silico SURMOF assembly to identify and overcome limitations in the growth of pillar‐layered SURMOFs and to develop a strategy to maximize linker alignment. Using master equation models and kinetic Monte Carlo simulations, it is found that the formation of a highly ordered state corresponding to the thermodynamic equilibrium is often prevented by long‐lasting transient effects. Based on ab initio binding energies for a wide selection of hypothetical pillar linkers, a fast‐binding, slow‐relaxation scheme is able to be identified during the SURMOF growth for a range of different pillar linkers. These observations allow them to derive a rational strategy for the design of novel linkers to yield SURMOF‐based non‐centrosymmetric materials with substantially improved properties. [ABSTRACT FROM AUTHOR]