Mesoporous supraparticles with a tailored solid-liquid-gas interface for visual indication of H2 gas and NH3 vapours.

Dual-gasochromic supraparticles that undergo rapid eye-readable and gas-specific colour changes upon reaction with hydrogen or ammonia are reported. This functionality is achieved by tailoring the solid-liquid-gas interface within the mesoporous framework of supraparticles via spray-drying.

Magnetic in situ determination of surface coordination motifs by utilizing the degree of particle agglomeration.

Most analytical techniques used to study the surface chemical properties of superparamagnetic iron oxide nanoparticles (SPIONs) are barely suitable for in situ investigations in liquids, where SPIONs are mostly applied for hyperthermia therapy, diagnostic biosensing, magnetic particle imaging or water purification. Magnetic particle spectroscopy (MPS) can resolve changes in magnetic interactions of SPIONs within seconds at ambient conditions. Herein, we show that by adding mono- and divalent cations to citric acid capped SPIONs, the degree of agglomeration can be utilized to study the selectivity of cations towards surface coordination motifs via MPS. A favored chelate agent, like ethylenediaminetetraacetic acid (EDTA) for divalent cations, removes cations from coordination sites on the SPION surface and causes redispersion of agglomerates. The magnetic determination thereof represents what we call a "magnetically indicated complexometric titration". The relevance of agglomerate sizes for the MPS signal response is studied on a model system of SPIONs and the surfactant cetrimonium bromide (CTAB). Analytical ultracentrifugation (AUC) and cryogenic transmission electron microscopy (cryo-TEM) reveal that large micron-sized agglomerates are required to significantly change the MPS signal response. With this work, a fast and easy-to-use characterization method to determine surface coordination motifs of magnetic nanoparticles in optically dense media is demonstrated.

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis.

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations.

The MoSGrid portal offers an approach to carry out high-quality molecular simulations on distributed compute infrastructures to scientists with all kinds of background and experience levels. A user-friendly Web interface guarantees the ease-of-use of modern chemical simulation applications well established in the field. The usage of well-defined workflows annotated with metadata largely improves the reproducibility of simulations in the sense of good lab practice. The MoSGrid science gateway supports applications in the domains quantum chemistry (QC), molecular dynamics (MD), and docking. This paper presents the open-source MoSGrid architecture as well as lessons learned from its design.