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Highly porous metal–organic frameworks (MOFs), which have undergone exciting developments over the past few decades, show promise for a wide range of applications. However, many studies indicate that they suffer from significant stability issues, especially with respect to their interactions with water, which severely limits their practical potential. Here we demonstrate how the presence of ‘sacrificial’ bonds in the coordination environment of its metal centres (referred to as hemilability) endows a dehydrated copper-based MOF with good hydrolytic stability. On exposure to water, in contrast to the indiscriminate breaking of coordination bonds that typically results in structure degradation, it is non-structural weak interactions between the MOF’s copper paddlewheel clusters that are broken and the framework recovers its as-synthesized, hydrated structure. This MOF retained its structural integrity even after contact with water for one year, whereas HKUST-1, a compositionally similar material that lacks these sacrificial bonds, loses its crystallinity in less than a day under the same conditions.

Rolling element bearings are required to operate in a variety of use cases that determine voltage potentials will form between the rolling elements and races. When the electrical field strength causes the dielectric breakdown of the intermediary lubricant film electrical discharge can damage the bearing surfaces. To reduce the prevalence and severity of electrical discharge machining an improved understanding of the coupled electrical and mechanical behavior is necessary. This paper aims to improve understanding of the problem through a combined elastohydrodynamic and electrostatic numerical study of charged elastohydrodynamic conjunctions. The results show the effect of amplitude reduction means that for typical surface topographies found in EHL conjunctions the maximum field strength is adequately predicted by the elastohydrodynamic minimum film thickness and potential difference. The paper also indicates the width of the elevated electrical field strength region is dependent on EHL parameters which could have important implications on the magnitude of current density during dielectric breakdown.

To assess the compliance of air quality regulations, the Environmental Protection Agency (EPA) must know if a site exceeds a pre-specified level. In the case of ozone, the level for compliance is fixed at 75 parts per billion, which is high, but not extreme at all locations. We present a new space-time model for threshold exceedances based on the skew-t process. Our method incorporates a random partition to permit long-distance asymptotic independence while allowing for sites that are near one another to be asymptotically dependent, and we incorporate thresholding to allow the tails of the data to speak for themselves. We also introduce a transformed AR(1) time-series to allow for temporal dependence. Finally, our model allows for high-dimensional Bayesian inference that is comparable in computation time to traditional geostatistical methods for large data sets. We apply our method to an ozone analysis for July 2005, and find that our model improves over both Gaussian and max-stable methods in terms of predicting exceedances of a high level.

Coordination polymers derived from 5-benzyloxy isophthalic acid (H2BzOip) are rare, with only three reported that do not contain additional bridging ligands, of which two M2(BzOip)2(H2O) (M = Co and Zn) are isomorphous. It was hoped that by varying the solvent system in a reaction between H2BzOip and M(OAc)2 (M = Co and Zn), from water to a water/alcohol mixture, coordination polymers of different topology could be formed. Instead, two polymorphs of the existing M2(BzOip)2(H2O) (M = Co and Zn) were isolated from aqueous methanol and aqueous ethanol, in which a small number of guest solvent molecules are present in the crystals. These guest water molecules disrupt the hexaphenyl embrace motif, leading to varying degrees of disorder of the benzyl groups.

Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model spatial extremal dependence. Based on a low-rank max-stable model, we propose a data-driven approach to estimate meaningful basis functions using empirical pairwise extremal coefficients. These spatial empirical basis functions can be used to visualize the main trends in extremal dependence. In addition to exploratory analysis, we describe how these functions can be used in a Bayesian hierarchical model to model spatial extremes of large datasets. We illustrate our methods on extreme precipitations in eastern USA.

The assembly–disassembly–organization–reassembly (ADOR) mechanism is a recent method for preparing inorganic framework materials and, in particular, zeolites. This flexible approach has enabled the synthesis of isoreticular families of zeolites with unprecedented continuous control over porosity, and the design and preparation of materials that would have been difficult—or even impossible—to obtain using traditional hydrothermal techniques. Applying the ADOR process to a parent zeolite with the UTL framework topology, for example, has led to six previously unknown zeolites (named IPC- n , where n  = 2, 4, 6, 7, 9 and 10). To realize the full potential of the ADOR method, however, a further understanding of the complex mechanism at play is needed. Here, we probe the disassembly, organization and reassembly steps of the ADOR process through a combination of in situ solid-state NMR spectroscopy and powder X-ray diffraction experiments. We further use the insight gained to explain the formation of the unusual structure of zeolite IPC-6. The assembly–disassembly–organization–reassembly (ADOR) process has recently enabled the synthesis of unusual — and sometimes previously inaccessible — inorganic materials. Further insight into its complex mechanism has now been gained that explains the unexpected formation and structure of such a zeolite.

Many predominant spatial methods for binary data use a latent Gaussian process to capture spatial dependence. However, this may not be appropriate for rare data because these methods based on Gaussian processes are asymptotically independent as the event probability goes to zero. In this paper, we propose a method for rare binary data that builds on spatial extreme value theory. We model binary events as exceedances of a max-stable process and show that this construction maintains spatial dependence even as the event probability goes to zero. We compare our model to spatial probit and logistic methods through a simulation study and analysis of a survey of Tamarix ramosissima and Hedysarum scoparium . We find some evidence that for very rare data the max-stable extension provides an improvement in spatial prediction compared to Gaussian models.

Statistical methods for inference on spatial extremes of large datasets are yet to be developed. Motivated by standard dimension reduction techniques used in spatial statistics, we propose an approach based on empirical basis functions to explore and model spatial extremal dependence. Based on a low-rank max-stable model we propose a data-driven approach to estimate meaningful basis functions using empirical pairwise extremal coefficients. These spatial empirical basis functions can be used to visualize the main trends in extremal dependence. In addition to exploratory analysis, we describe how these functions can be used in a Bayesian hierarchical model to model spatial extremes of large datasets. We illustrate our methods on extreme precipitations in eastern U.S.

A sequence motif representing the DNA-binding specificity of a transcription factor (TF) is commonly modelled with a positional weight matrix (PWM). Focusing on understudied human TFs, we processed results of 4,237 experiments for 394 TFs, assayed using five different experimental platforms. By human curation, we approved a subset of experiments that yielded consistent motifs across platforms and replicates, and evaluated quantitatively the cross-platform performance of PWMs obtained with ten motif discovery tools. Notably, nucleotide composition and information content are not correlated with motif performance and do not help in detecting underperformers, while motifs with low information content, in many cases, describe well the binding specificity assessed across different experimental platforms. By combining multiple PMWs into a random forest, we demonstrate the potential of accounting for multiple modes of TF binding. Finally, we present the Codebook Motif Explorer ( https://mex.autosome.org ), cataloguing motifs, benchmarking results, and the underlying experimental data. Cross-platform benchmarking of DNA binding specificity models highlights top-performing motif discovery methods and demonstrates the potential of advanced models to capture alternative binding modes of human transcription factors.