Correlative electrochemical microscopy of Li-Ion (De)intercalation at a series of individual LiMn2 O4 particles

The redox activity (Li‐ion intercalation/deintercalation) of a series of individual LiMn2O4 particles of known geometry and (nano)structure, within an array, is determined using a correlative electrochemical microscopy strategy. Cyclic voltammetry (current–voltage curve, I–E) and galvanostatic charge/discharge (voltage–time curve, E–t) are applied at the single particle level, using scanning electrochemical cell microscopy (SECCM), together with co‐location scanning electron microscopy that enables the corresponding particle size, morphology, crystallinity, and other factors to be visualized. This study identifies a wide spectrum of activity of nominally similar particles and highlights how subtle changes in particle form can greatly impact electrochemical properties. SECCM is well‐suited for assessing single particles and constitutes a combinatorial method that will enable the rational design and optimization of battery electrode materials

Scanning electrochemical cell microscopy : a versatile method for highly localised corrosion related measurements on metal surfaces

The development of tools that can probe corrosion related phenomena at the (sub)microscale is recognized to be increasingly important in order to understand the surface structural factors (grain orientation, inclusions etc.) that control the (electro)chemical stability (corrosion susceptibility, pitting, passivity etc.) of metal surfaces. Herein we consider the application of scanning electrochemical cell microscopy (SECCM), a relatively new member of the electrochemical droplet cell (EDC) family, for corrosion research and demonstrate the power of this technique for resolving structure and activity at the (sub)microscale. Hundreds of spatially-resolved (2 μm droplet size) potentiodynamic polarization experiments have been carried out on the several hours timescale and correlated to complementary structural information from electron backscatter diffraction (EBSD) and energy dispersive x-ray spectroscopy (EDS) in order to determine the effect of grain orientation and inclusions on electrochemical processes at low carbon steel in neutral solution (10 mM KNO3). Through this approach, it has been shown unequivocally that for the low index planes, anodic currents in the passive region (an indicator of corrosion susceptibility) are greatest on (101) planes compared to (100) and (111) planes. Furthermore, individual sub-micron MnS inclusions have been probed and shown to undergo active dissolution followed by rapid repassivation. This study demonstrates the high versatility of SECCM and the considerable potential of this technique for addressing structure-activity problems in corrosion and electromaterials science

Nanoscale electrochemical visualization of grain-dependent anodic iron dissolution from low carbon steel

The properties of steels and other alloys are often tailored to suit specific applications through the manipulation of microstructure (e.g., grain structure). Such microscopic heterogeneities are also known to modulate corrosion susceptibility/resistance, but the exact dependency remains unclear, largely due to the challenge of probing and correlating local electrochemistry and structure at complex (alloy) surfaces. Herein, high-resolution scanning electrochemical cell microscopy (SECCM) is employed to perform spatially-resolved potentiodynamic polarisation measurements, which, when correlated to co-located structural information from electron backscatter diffraction (EBSD), analytical scanning electron microscopy (SEM) and scanning transmission electron microscopy (STEM), reveal the relationship between anodic metal (iron) dissolution and the crystallographic orientation of low carbon steel in aqueous sulfuric acid (pH 2.3). Considering hundreds of individual measurements made on each of the low-index planes of body-centred cubic (bcc) low carbon steel, the rate of iron dissolution, and thus overall corrosion susceptibility, increases in the order (101) < (111) < (100). These results are rationalized by complementary density functional theory (DFT) calculations, where the experimental rate of iron dissolution correlates with the energy required to remove (and ionise) one iron atom at the surface of a lattice, calculated for each low index orientation. Overall, this study further demonstrates how nanometre-resolved electrochemical techniques such as SECCM can be effectively utilised to vastly improve the understanding of structure-function in corrosion science, particularly when combined with complementary, co-located structural characterisation (EBSD, STEM etc.) and computational analysis (DFT)

Surface microstructural controls on electrochemical hydrogen absorption at polycrystalline palladium

The ease by which hydrogen is absorbed into a metal can be either advantageous or deleterious, depending on the material and application in question. For instance, in metals such as palladium (Pd), rapid absorption kinetics are seen as a beneficial property for hydrogen purification and storage applications, whereas the contrary is true for structural metals such as steel, which are susceptible to mechanical degradation in a process known as hydrogen embrittlement. It follows that understanding how the microstructure of metals (i.e., grains and grain boundaries) influences adsorption and absorption kinetics would be extremely powerful to rationally design materials (e.g., alloys) with either a high affinity for hydrogen or resistance to hydrogen embrittlement. To this end, scanning electrochemical cell microscopy (SECCM) is deployed herein to study surface structure-dependent electrochemical hydrogen absorption across the surface of flame annealed polycrystalline Pd in aqueous sulfuric acid (considered the model system for the study of hydrogen absorption). Correlating spatially-resolved cyclic voltammetric data from SECCM with co-located structural information from electron backscatter diffraction (EBSD) reveals a clear relationship between the crystal orientation and the rate of hydrogen adsorption-absorption. Grains that are closest to the low-index orientations [i.e., the {100}, {101}, and {111} facets, face-centered cubic (fcc) system] facilitate the lowest rates of hydrogen absorption, whereas grains of high-index orientation (e.g., {411}) promoted higher rates. Apparently enhanced kinetics are also seen at grain boundaries, which is thought to arise from physical deformation of the Pd surface adjacent to the boundary, resulting from the flame annealing and quenching process. As voltammetric measurements are made across a wide potential range, these studies also reveal palladium oxide formation and stripping to be surface structure-dependent processes, and further highlight the power of combined SECCM-EBSD for structure-activity measurements in electrochemical science

Using Social Software for Teamwork and Collaborative Project Management in Higher Education

This paper discusses the potential role of social software in supporting teamwork and collaborative project management in higher education. Based on the fact that social software has been widely spread among young students nowadays, using it for collaborative learning is believed to increase students' involvement and create learning incentives. Two social software platforms, Graaasp and Google Wave are examined in terms of sustaining collaborative learning activities. Relevant existing features and possible extensions that enhance the learning experience are addressed. Benefits and challenges resulting from the bottom-up learning paradigm are also presented

NT-proBNP by Itself Predicts Death and Cardiovascular Events in High-Risk Patients With Type 2 Diabetes Mellitus

BACKGROUND: NT-proBNP (N-terminal pro-B-type natriuretic peptide) improves the discriminatory ability of risk-prediction models in type 2 diabetes mellitus (T2DM) but is not yet used in clinical practice. We assessed the discriminatory strength of NT-proBNP by itself for death and cardiovascular events in high-risk patients with T2DM. METHODS AND RESULTS: Cox proportional hazards were used to create a base model formed by 20 variables. The discriminatory ability of the base model was compared with that of NT-proBNP alone and with NT-proBNP added, using C-statistics. We studied 5509 patients (with complete data) of 8561 patients with T2DM and cardiovascular and/or chronic kidney disease who were enrolled in the ALTITUDE (Aliskiren in Type 2 Diabetes Using Cardiorenal Endpoints) trial. During a median 2.6-year follow-up period, 469 patients died and 768 had a cardiovascular composite outcome (cardiovascular death, resuscitated cardiac arrest, nonfatal myocardial infarction, stroke, or heart failure hospitalization). NT-proBNP alone was as discriminatory as the base model for predicting death (C-statistic, 0.745 versus 0.744, P=0.95) and the cardiovascular composite outcome (C-statistic, 0.723 versus 0.731, P=0.37). When NT-proBNP was added, it increased the predictive ability of the base model for death (C-statistic, 0.779 versus 0.744, P<0.001) and for cardiovascular composite outcome (C-statistic, 0.763 versus 0.731, P<0.001). CONCLUSIONS: In high-risk patients with T2DM, NT-proBNP by itself demonstrated discriminatory ability similar to a multivariable model in predicting both death and cardiovascular events and should be considered for risk stratification. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00549757

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Source apportionment advances using polar plots of bivariate correlation and regression statistics

This paper outlines the development of enhanced bivariate polar plots that allow the concentrations of two pollutants to be compared using pair-wise statistics for exploring the sources of atmospheric pollutants. The new method combines bivariate polar plots, which provide source characteristic information, with pair-wise statistics that provide information on how two pollutants are related to one another. The pair-wise statistics implemented include weighted Pearson correlation and slope from two linear regression methods. The development uses a Gaussian kernel to locally weight the statistical calculations on a wind speed-direction surface together with variable-scaling. Example applications of the enhanced polar plots are presented by using routine air quality data for two monitoring sites in London, United Kingdom for a single year (2013). The London examples demonstrate that the combination of bivariate polar plots, correlation, and regression techniques can offer considerable insight into air pollution source characteristics, which would be missed if only scatter plots and mean polar plots were used for analysis. Specifically, using correlation and slopes as pair-wise statistics, long-range transport processes were isolated and black carbon (BC) contributions to PM2.5 for a kerbside monitoring location were quantified. Wider applications and future advancements are also discussed

Structural determinants of opioid and NOP receptor activity in derivatives of buprenorphine

The unique pharmacological profile of buprenorphine has led to its considerable success as an analgesic and as a treatment agent for drug abuse. Activation of nociceptin/orphanin FQ peptide (NOP) receptors has been postulated to account for certain aspects of buprenorphine’s behavioural profile. In order to investigate the role of NOP activation further, a series of buprenorphine analogues has been synthesised with the aim of increasing affinity for the NOP receptor. Binding and functional assay data on these new compounds indicate that the area around C20 in the orvinols is key to NOP receptor activity, with several compounds displaying higher affinity than buprenorphine. One compound, 1b, was found to be a mu opioid receptor partial agonist of comparable efficacy to buprenorphine, but with higher efficacy at NOP receptors