A water wire in L-prolyl-L-serine monohydrate

Optically active derivatives of terephthalic acid: four crystal structures from two powder patterns

A novel important class of nanoporous crystalline solids, metal-organic frameworks (MOFs), composed of organic ligands (linkers) and metal ions, is now considered as a platform for the development of various functional hybrid materials. In order to design new MOF-based asymmetric catalysts, two terephthalic acid derivatives, namely 2-{[1-(1-tert-butoxycarbonyl)-L-prolyl]amino}terephthalic acid, C₁₈H₂₂N₂O₇, (1), and 2-(L-prolylamino)terephthalic acid, C₁₃H₁₄N₂O₅, (2), which could find potential applications as chiral linkers for the construction of enantioselective MOFs, were synthesized and their powder samples were measured at synchrotron station ID22 (ESRF). Each sample contained two unknown crystalline phases, so four new crystal structures were determined, namely, the 2.24-hydrate of (1), (1a) (space group C222₁), and the 2.08-hydrate of (1), (1b) (P222₁), which are crystallohydrates, and two polymorphs of (2), i.e. (2a) (C222₁) and (2b) (P2₁2₁2₁), and were validated with DFT-d (dispersion-corrected density functional theory) optimizations.

An alternative to the goodness of fit

An alternative measure to the goodness of fit (GoF) is developed and applied to experimental data. The alternative goodness of fit squared (aGoFs) demonstrates that the GoF regularly fails to provide evidence for the presence of systematic errors, because certain requirements are not met. These requirements are briefly discussed. It is shown that in many experimental data sets a correlation between the squared residuals and the variance of observed intensities exists. These correlations corrupt the GoF and lead to artificially reduced values in the GoF and in the numerical value of thewR(F2). Remaining systematic errors in the data sets are veiled by this mechanism. In data sets where these correlations do not appear for the entire data set, they often appear for the decile of largest variances of observed intensities. Additionally, statistical errors for the squared goodness of fit, GoFs, and the aGoFs are developed and applied to experimental data. This measure shows how significantly the GoFs and aGoFs deviate from the ideal value one.

More about residual values

The usual residual values are complemented by expectation values based solely on the experimental data and the number of model parameters. These theoreticalRvalues serve as benchmark values when all of the basic assumptions for a least-squares refinement,i.e.no systematic errors and a fully adequate model capable of describing the data, are fulfilled. The prediction ofRvalues as presented here is applicable to any field where model parameters are fitted to data with known precision. For crystallographic applications,F2-based residual benchmark values are given. They depend on the first and second moments of variance, intensity and significance distributions, 〈σ2〉, 〈Io2〉, 〈Io2/σ2〉. Possible applications of the theoreticalRvalues are, for example, as a data-quality measure or the detection of systematic deviations between experimental data and model predicted data, although the theoreticalRvalues cannot identify the origin of these systematic deviations. The change inRvalues due to application of a weighting scheme is quantified with the theoreticalRvalues.