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Europium is a useful proxy in identifying r-process elements such as Thorium and Uranium that act as long-term sources of generated heat in the mantle of terrestrial planetsdue to their radioactivity and long half-lives. Comparing these abundances to a proxyfor the bulk-silicate mantle, such as Magnesium, allows us to describe the geothermalevolution of terrestrial planets. We use stellar data from two independent teams, thefirst being Delgado-Mena et al. which has 566, out of a total of 1059 stars, having measured Europium values, and the second team being Battistini and Bensby, which has atotal of 378 stars, each with measured Europium values. Between these two datasets,there exist only 68 shared stars. With these datasets, we perform an analysis thataims to identify significant star-to-star variation in Europium. Our analysis involvesthe decorrelation of [Eu/H] values via a metallicity parameter in the form of either[Fe/H] or [Mg/H], as well as Teff while comparing the [Eu/H] vs. Age trend before thedecorrelation to the same trend after the decorrelation. For the Delgado-Mena dataset,we perform our analysis twice; first, the individual analysis of the thin disk stars andsecond, the analysis of the total population according to the galactic populations: thindisk, thick disk, high, and hαmr. The important takeaways from this study are noted assuch: the peak-to-peak [Eu/H] values are subject to inflation due to the correlation seenwith both metallicity and Teff parameters. Metallicity parameters impact this inflationgreater than the Teff parameter. Teff trends from the separate datasets are of similarmagnitude but of different signs, which is indicative of a different problem outside thescope of the purpose of this study. Our analysis of [Eu/H] values after compensatingfor trends in both Teff and a metallicity term results in a tight dispersion (standarddeviation) with these values for the Delgado-Mena data ranging from 0.064 to 0.087dex. Each of these values is smaller than the quoted average error of 0.1 dex for the raw[Eu/H] values. As for the Battistini & Bensby data, we see the values 0.088 and 0.092dex, both of which are more consistent with the quoted error of 0.08 dex. Comparingthese results to the quadratic sum of the average errors from both datasets with a valueof 0.13 dex, tells us that Delgado-Mena is overestimating their errors, whereas, for Battistini & Bensby, the results are inconclusive in determining whether they are over orunderestimating. We find evidence for true outliers in the data, with as many as tenstars that exceed 3σ difference for the [Fe/H] & Teff detrended Delgado-Mena data withan average residual [Eu/H] value of 0.17 dex, where we would expect about two outliersfor the size of our dataset. Similarly, we find five stars that exceed the same 3σ difference for the [Mg/H] & Teff detrended B&B data with an average residual [Eu/H] valueof 0.29 dex. For both datasets, the percentage of stars that exceed the 3σ difference isabout 1.5% of the [Eu/H] positive sample size. Averaging the standard deviations fromthe total population models, we find a [Eu/H] variation value of 0.085 dex which givesus a peak-to-peak range of ±0.26. What we find is little evidence of significant intrinsicstar-to-star variation of [Eu/H]; with peak-to-peak ranges significantly decreased by ouranalysis. The motivating literature suggests a range of ±0.5 dex, we find a range of±0.26 dex, and with relatively few extreme outliers, we are led to believe that the truevariation of [Eu/H] is less than the literature suggests.

This tutorial review focuses on recent advances in technologies for enzyme immobilisation, enabling their cost-effective use in the bio-based economy and continuous processing in general. The application of enzymes, particularly in aqueous media, is generally on a single use, throw-away basis which is neither cost-effective nor compatible with a circular economy concept. This shortcoming can be overcome by immobilising the enzyme as an insoluble recyclable solid, that is as a heterogeneous catalyst. This journal is

Additional file 1: SuppInfo Frederick et al. BAA-870 genome. Table S1. All sequenced Rhodococcus strains (353) according to the NCBI database (accessed 13/03/2019). Table S2. All complete sequenced Rhodococcus species ranked by release date according to the NCBI Genome database (accessed 11/03/2019). Table S3. Whole genome distance statistics between Rhodococcus rhodochrous ATCC BAA-870 and two closely matched strains. Table S4. Rhodococcus rhodochrous ATCC BAA-870 protein function breakdown based on BASys annotation COG classifications.

Additional file 1: SuppInfo Frederick et al. BAA-870 genome. Table S1. All sequenced Rhodococcus strains (353) according to the NCBI database (accessed 13/03/2019). Table S2. All complete sequenced Rhodococcus species ranked by release date according to the NCBI Genome database (accessed 11/03/2019). Table S3. Whole genome distance statistics between Rhodococcus rhodochrous ATCC BAA-870 and two closely matched strains. Table S4. Rhodococcus rhodochrous ATCC BAA-870 protein function breakdown based on BASys annotation COG classifications.