Blending PEG-based polymers and their use in surface micro-patterning by the FIMIC method to obtain topographically smooth patterns of elasticity

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We have designed and fabricated a library of polyethylene glycol (PEG)-based polymer blends, including blends of two PEG-based polymers that are liquid at room temperature where the optimisation of the blending method allows for the incorporation of higher molecular-weight PEG-based polymers which are solid at room temperature. The absence of a solvent in these blends makes them perfect candidates for use in our recently developed Fill-Molding in Capillaries (FIMIC) patterning method. As our FIMIC samples have shown to be not completely smooth (a small topography up to several nanometers has been seen previously), and this is likely to affect the cellular behaviour, we have improved our technique in order to obtain virtually smooth samples that exhibit a pattern of elasticity only. It is demonstrated that, by taking advantage of the differential swelling of the pattern components, we can level out the undesired topographic difference. In particular, by employing blends of materials, (1) the swelling degree of each component can be fine-tuned to even out any topography and (2) the use of the same blends in the sample, yet with varying cross-linker amounts, ensures the swelling degree and elasticity change without changing the surface chemistry significantly. Genuine, binary patterns of elasticity can thus be fabricated, which are a great asset to study cell migration phenomena in systematic detail.DFG, EXC 314, Unifying Concepts in Catalysi

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities

Hydrophobically Modified Sulfobetaine Copolymers with Tunable Aqueous UCST through Postpolymerization Modification of Poly(pentafluorophenyl acrylate)

Polysulfobetaines, polymers carrying highly polar zwitterionic side chains, present a promising research field by virtue of their antifouling properties, hemocompatibility, and stimulus-responsive behavior. However, limited synthetic approaches exist to produce sulfobetaine copolymers comprising hydrophobic components. Postpolymerization modification of an activated ester precursor, poly(pentafluorophenyl acrylate), employing a zwitterionic amine, 3-((3-aminopropyl)dimethylammonio)propane-1-sulfonate, ADPS, is presented as a novel, one-step synthetic concept toward sulfobetaine (co)polymers. Modifications were performed in homogeneous solution using propylene carbonate as solvent with mixtures of ADPS and pentylamine, benzylamine, and dodecylamine producing a series of well-defined statistical acrylamido sulfobetaine copolymers containing hydrophobic pentyl, benzyl, or dodecylacrylamide comonomers with well-controllable molar composition as evidenced by NMR and FT-IR spectroscopy and size exclusion chromatography.This synthetic strategy was exploited to investigate, for the first time, the influence of hydrophobic modification on the upper critical solution temperature (UCST) of sulfobetaine copolymers in aqueous solution. Surprisingly, incorporation of pentyl groups was found to increase solubility over a wide composition range, whereas benzyl groups decreased solubility—an effect attributed to different entropic and enthalpic contributions of both functional groups. While UCST transitions of polysulfobetaines are typically limited to higher molar mass samples, incorporation of 0–65 mol % of benzyl groups into copolymers with molar masses of 25.5–34.5 kg/mol enabled sharp, reversible transitions from 6 to 82 °C in solutions containing up to 76 mM NaCl, as observed by optical transmittance and dynamic light scattering. Both synthesis and systematic UCST increase of sulfobetaine copolymers presented here are expected to expand the scope and applicability of these smart materials