Nickel phosphonate MOF as efficient water splitting photocatalyst

A novel microporous two-dimensional(2D)Ni-based phosphonate metal-organic framework(MOF;denoted as IEF-13)has been successfully synthesized by a simple and green hydrothermal method and fully characterized using a combination of experimental and computational techniques.Structure resolution by single-crystal X-ray diffraction reveals that IEF-13 crystallizes in the triclinic space group Pi having bi-octahedra nickel nodes and a photo/electroactive tritopic phosphonate ligand.Remarkably,this material exhibits coordinatively unsaturated nickel(II)sites,free-P0₃H₂and-P0₃H acidic groups,a C0₂accessible microporosity,and an exceptional thermal and chemical stability.Further,its in-deep optoelectronic characterization evidences a photoresponse suitable for photocatalysis.In this sense,the photocatalytic activity for challenging H₂generation and overall water splitting in absence of any co-catalyst using UV-Vis irradiation and simulated sunlight has been evaluated,constituting the first report for a phosphonate-MOF photocatalyst.IEF-13 is able to produce up to 2,200 fimol of H₂per gram using methanol as sacrificial agent,exhibiting stability,maintaining its crystal structure and allowing its recycling.Even more,170μmol of H₂per gram were produced using IEF-13 as photocatalyst in the absence of any co-catalyst for the overall water splitting,being this reaction limited by the 0₂reduction.The present work opens new avenues for further optimization of the photocatalytic activity in this type of multifunctional materials.     

Distribution-free uncertainty quantification for kernel methods by gradient perturbations

Machine Learning - We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence,...     

The impacts of cattle access points on deposited sediment levels in headwater streams in Ireland

Cattle access to streams has been linked globally with degradation of stream water quality, driven largely by bank erosion and resultant instream, fine sediment deposition. The majority of evidence on such effects is however based in arid and semiarid regions of the United States and Australia, with few studies relating to cool temperate climates such as Northwest Europe. In this study, “Quorer” resuspendable sediment samples were taken from riffle geomorphic units upstream (control) and at two points downstream (pressure and recovery) of cattle access points in headwater streams in agricultural catchments in Ireland to assess levels of deposited stream sediment. Samples were taken in April/May (2016) prior to the grazing season and in October (2016) at the end of the grazing season. Sites in good‐high ecological status catchments and less than good ecological status catchments were included in the study. Higher levels of sediment were found downstream of cattle access points in both good‐high status and less than good status catchments; however, the impacts of access points were spatially confined to, in most cases, the area immediately downstream of the point of access. There was a strong correlation between deposited sediment mass and organic matter (OM) mass, with levels of OM increasing linearly with deposited sediment mass. Levels of measured sediment were negatively correlated with riparian habitat health (measured using a qualitative habitat assessment). The results of this study highlight the need for measures to prevent cattle access to headwater streams where access points can be many in order to manage local habitat quality and downstream water quality issues.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.