In the last decade, trends for competing electrocatalytic processes have been largely captured by volcano plots, which can be constructed by the analysis of adsorption free energies as derived from ...electronic structure theory in the density functional theory approximation. One prototypical example refers to the four-electron and two-electron oxygen reduction reactions (ORRs), resulting in the formation of water and hydrogen peroxide, respectively. The conventional thermodynamic volcano curve illustrates that the four-electron and two-electron ORRs reveal the same slopes at the volcano legs. This finding is related to two facts, namely, that only a single mechanistic description is considered in the model, and electrocatalytic activity is assessed by the concept of the limiting potential, a simple thermodynamic descriptor evaluated at the equilibrium potential. In the present contribution, the selectivity challenge of the four-electron and two-electron ORRs is analyzed, thereby accounting for two major expansions. First, different reaction mechanisms are included into the analysis, and second, G max(U), a potential-dependent activity measure that factors overpotential and kinetic effects into the evaluation of adsorption free energies, is applied for approximation of electrocatalytic activity. It is illustrated that the slope of the four-electron ORR is not constant at the volcano legs but rather is prone to change as soon as another mechanistic pathway is energetically preferred or another elementary step becomes the limiting one. Due to the varying slope of the four-electron ORR volcano, a trade-off between activity and selectivity for hydrogen peroxide formation is observed. It is demonstrated that the two-electron ORR is energetically preferred at the left and right volcano legs, thus opening a new strategy for the selective formation of H2O2 by an environmentally benign route.
Quantum chemical high-throughput screenings of compound libraries for the identifications of materials with the desired properties have emerged as beneficial tools to accelerate the discoveries of ...compounds of interest. The quantum chemical high-throughput screenings of compound libraries require the definitions of reliable descriptors enabling relationships between the observed physical properties and the computed electronic structures. The desire to enhance the discoveries of materials showing electronic instabilities which are related to possible metal-to-superconductor transitions stimulated our impetus to probe the feasibility of a descriptor for the identifications of materials with the aforementioned electronic instabilities in the forms of flat bands crossing the Fermi levels. To evaluate the reliability of the projected descriptor based on the flat band/steep band scenario for superconductors, we inspected the characteristics of the electronic band structures near the Fermi levels for a series of chalcogenide superconductors, whose electronic structures were computed and analyzed by means of first-principles-based high-throughput techniques.
Flat surfaces decorated with micro- and nanostructures are important tools in biomedical research used to control cellular shape, in studies of mechanotransduction, membrane mechanics, cell migration ...and cellular interactions with nanostructured surfaces. Existing methods to fabricate surface-bound nanostructures are typically limited either by resolution, aspect ratio or throughput. In this work, we explore electron beam lithography based structuring of the epoxy resist SU-8 on glass substrate. We focus on a systematic investigation of the process parameters and determine limits of the fabrication process, both in terms of spatial resolution, structure aspect ratio and fabrication throughput. The described approach is capable of producing high-aspect ratio, surface bound nanostructures with height ranging from 100 nm to 4000 nm and with in-plane resolution below 100 nm directly on a transparent substrate. Fabricated nanostructured surfaces can be integrated with common techniques for biomedical research, such as high numerical aperture optical microscopy. Furthermore, we show how the described approach can be used to make nanostructures with multiple heights on the same surface, something which is not readily achievable using alternative fabrication approaches. Our research paves an alternative way of manufacturing nanostructured surfaces with applications in life science research. 2019-0255
•Using the method Pourbaix diagram we identified the oxygen covered RuO2(110) surface as the catalytically active phase under chlorine evolution reaction (CER) conditions. This active phase is ...compared with the active phase in the Deacon process, the heterogeneous gas phase counterpart of the CER.
Constrained ab initio thermodynamics in the form of a Pourbaix diagram can greatly assist kinetic modeling of a particular electrochemical reaction such as the chlorine evolution reaction (CER) over RuO2(110). Pourbaix diagrams reveal stable surface structures, as a function of pH and the potential. The present DFT study indicates that the Pourbaix diagram in the CER potential region above 1.36 V and pH values around zero is dominated by a stable surface structure in which all coordinatively undercoordinated Ru sites (Rucus) are capped by on-top oxygen (Oot). This oxygen saturated RuO2(110) surface is considered to serve as the catalytically active phase in the CER, quite in contrast to the heterogeneously catalyzed HCl oxidation (Deacon process), for which the active RuO2(110) surface is mainly covered by on-top chlorine. The active sites in the CER are suggested to be RucusOot surface complexes, while in the Deacon process both undercoordinated surface Ru and oxygen sites must be available for the activation of HCl molecules.
To move from fossil-based energy resources to a society based on renewables, electrode materials free of precious noble metals are required to efficiently catalyze electrochemical processes in fuel ...cells, batteries, or electrolyzers. Materials screening operating at minimal computational cost is a powerful method to assess the performance of potential electrode compositions based on heuristic concepts. While the thermodynamic overpotential in combination with the volcano concept refers to the most popular descriptor-based analysis in the literature, this notion cannot reproduce experimental trends reasonably well. About two years ago, the concept of G max(η), based on the idea of the free-energy span model, has been proposed as a universal approach for the screening of electrocatalysts. In contrast to other available descriptor-based methods, G max(η) factors overpotential and kinetic effects by a dedicated evacuation scheme of adsorption free energies into an analysis of trends. In the present perspective, we discuss the application of G max(η) to different electrocatalytic processes, including the oxygen evolution and reduction reactions, the nitrogen reduction reaction, and the selectivity problem of the competing oxygen evolution and peroxide formation reactions, and we outline the advantages of this screening approach over previous investigations.
DFT‐based ab initio Pourbaix diagrams represent a powerful tool to resolve the stable surface structure of an electrocatalyst under different environmental parameters such as the applied electrode ...potential and pH. Herein, a general approach for anode and cathode materials in lithium‐ion batteries (LIBs) is presented that enables to transfer the concept of surface Pourbaix diagrams from electrocatalysis to electrode materials employed in LIBs. This novel approach is exemplified at the example of the (111) facet for a single‐crystalline spinel lithium titanate (LTO) model electrode by combining constrained thermodynamics and density functional theory calculations.
From electrocatalysis to battery science: Surface Pourbaix diagrams that rely on a constrained ab initio thermodynamics approach provide deep insights into the thermodynamically stable surface of an electrocatalyst at different environmental parameters such as the applied electrode potential and pH. This concept is transferred from electrocatalysis to electrode materials in lithium‐ion batteries and exemplified with a single‐crystalline spinel lithium titanate (111) model electrode.
Glassy, glass–ceramic, and crystalline lithium thiophosphates have attracted interest in their use as solid electrolytes in all-solid-state batteries. Despite similar structural motifs, including PS4 ...3–, P2S6 4–, and P2S7 4– polyhedra, these materials exhibit a wide range of possible compositions, crystal structures, and ionic conductivities. Here, we present a combined approach of Bragg diffraction, pair distribution function analysis, Raman spectroscopy, and 31P magic angle spinning nuclear magnetic resonance spectroscopy to study the underlying crystal structure of Li4P2S6. In this work, we show that the material crystallizes in a planar structural arrangement as a glass ceramic composite, explaining the observed relatively low ionic conductivity, depending on the fraction of glass content. Calculations based on density functional theory provide an understanding of occurring diffusion pathways and ionic conductivity of this Li+ ionic conductor.
This article presents a deep learning scheme for automatic defect detection in material surfaces. The success of deep learning model training is generally determined by the number of representative ...training samples and the quality of the annotation. It is extremely tedious and tiresome to annotate defects pixel-by-pixel in an image to train a semantic network model for defect segmentation. In this study, we propose a two-stage deep learning scheme to tackle the pixel-wise defect detection in textured surfaces without manual annotation. The first stage of the deep learning scheme uses two cycle-consistent adversarial network (CycleGAN) models to automatically synthesize and annotate defect pixels in an image. The synthesized defect images and their corresponding annotated results from the CycleGAN models are then used as the input-output pairs for training the U-Net semantic network. The proposed scheme requires only a few real defect samples for the training and completely requires no manual annotation work. It is practical and computationally very efficient for the implementation in manufacturing. Experimental results show that the proposed deep learning scheme can be applied for defect detection in a variety of textured and patterned surfaces, and results in high detection accuracy.
•This review presents the contribution of TMS to the management of dementia.•TMS can be used as a biomarker of the excitability and function of cerebral cortex in dementia.•Increasing evidence ...supports the beneficial effects of rTMS in Alzheimer’s disease-related dementias at mild/early stage.
Transcranial magnetic stimulation (TMS) is a powerful tool to probe in vivo brain circuits, as it allows to assess several cortical properties such asexcitability, plasticity and connectivity in humans. In the last 20 years, TMS has been applied to patients with dementia, enabling the identification of potential markers of thepathophysiology and predictors of cognitive decline; moreover, applied repetitively, TMS holds promise as a potential therapeutic intervention.
The objective of this paper is to present a comprehensive review of studies that have employed TMS in dementia and to discuss potential clinical applications, from the diagnosis to the treatment.
To provide a technical and theoretical framework, we first present an overview of the basic physiological mechanisms of the application of TMS to assess cortical excitability, excitation and inhibition balance, mechanisms of plasticity and cortico-cortical connectivity in the human brain. We then review the insights gained by TMS techniques into the pathophysiology and predictors of progression and response to treatment in dementias, including Alzheimer’s disease (AD)-related dementias and secondary dementias. We show that while a single TMS measure offers low specificity, the use of a panel of measures and/or neurophysiological index can support the clinical diagnosis and predict progression.
In the last part of the article, we discuss the therapeutic uses of TMS. So far, only repetitive TMS (rTMS) over the left dorsolateral prefrontal cortex and multisite rTMS associated with cognitive training have been shown to be, respectively, possibly (Level C of evidence) and probably (Level B of evidence) effective to improve cognition, apathy, memory, and language in AD patients, especially at a mild/early stage of the disease. The clinical use of this type of treatment warrants the combination of brain imaging techniques and/or electrophysiological tools to elucidate neurobiological effects of neurostimulation and to optimally tailor rTMS treatment protocols in individual patients or specific patient subgroups with dementia or mild cognitive impairment.
Understanding the complex interactions of different building blocks within a sophisticated drug-delivery system (DDS), aimed at targeted transport of the drug to malignant cells, requires modeling ...techniques on different time and length scales. On the example of the anthracycline antibiotic doxorubicin (DOX), we investigate a potential DDS component, consisting of a gold nanoparticle and a short peptide sequence as carriers of DOX. The combination of atomistic molecular dynamics simulations and density functional theory calculations facilitates compiling a volcano plot, which allows deriving general conclusions on DDS constituents for chemotherapeutic agents within the class of anthracycline antibiotics: the nanoparticle and peptide carrier moieties need to be chosen in such a way that the anthracycline body of the drug is able to intercalate between both entities or between two (π-stacking) residues of the peptide. Using the popular volcano framework as a guideline, the present article connects the catalysis and biosimulation communities, thereby identifying a strategy to overcome the limiting volcano relation by tuning the coordination number of the drug in the DDS component.
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