In the human body, high-molecular-weight glycoproteins called mucins play a key role in protecting epithelial surfaces against pathogenic attack, controlling the passage of molecules toward the tissue and enabling boundary lubrication with very low friction coefficients. However, neither the molecular mechanisms nor the chemical motifs of those biomacromolecules involved in these fundamental processes are fully understood. Thus, identifying the key features that render biomacromolecules such as mucins outstanding boundary lubricants could set the stage for creating versatile artificial superlubricants. We here demonstrate the importance of the hydrophobic terminal peptide domains of porcine gastric mucin (MUC5AC) and human salivary mucin (MUC5B) in the processes of adsorbing to and lubricating a hydrophobic PDMS surface. Tryptic digestion of those mucins results in removal of those terminal domains, which is accompanied by a loss of lubricity as well as surface adsorption. We show that this loss can in part be compensated by attaching hydrophobic phenyl groups to the glycosylated central part of the mucin macromolecule. Furthermore, we demonstrate that the simple biopolysaccharide dextran can be functionalized with hydrophobic groups which confers efficient surface adsorption and good lubricity on PDMS to the polysaccharide.

Surface modification with polyphenolic molecules has been pursued in biomedical materials owing to their antioxidant, anti‐inflammatory, and antimicrobial characteristics. Recently, the use of silicic acid (Siaq) as a mediator for efficient surface deposition of tannic acid (TA) was reported, but the postulated Si‐TA polymeric networks were not characterized. Herein, we present unambiguous evidence for silicate‐TA networks that involve Si−O−C motifs by using solid‐state NMR spectroscopy, further supported by XPS and ToF‐SIMS. By using QCM‐D we demonstrate the advantages of Siaq, compared to using transition‐metal ions, to improve the coating efficiency under mildly acidic conditions. The presented homogenous coating buildup and validated applicability in inorganic buffers broadens the use of TA for surface modifications in technological and biomedical applications.

Tannic acid (TA) adheres to a broad variety of different materials and forms versatile surface coatings for technical and biological applications. In mild alkaline conditions, auto-oxidation processes occur and a firm monolayer is formed. Up to now, thicker coatings are only obtained in cross-linked multilayer fashion. This study presents an alternative method to form continuous TA coatings using ortho-silicic acid (Siaq). Adsorption kinetics and physical properties of TA coatings in the presence of Siaq were determined using a quartz crystal microbalance (QCM-D) and nanoplasmonic spectroscopy (NPS). An in situ TA layer thickness of 200 nm was obtained after 24 h in solutions supplemented with 80 µM Siaq. Dry state measurements indicated a highly hydrated layer in situ. Furthermore, chemical analysis by FTIR spectroscopy revealed possible complexation of TA by Siaq, whereas UV-vis spectroscopy did not indicate an interaction of Siaq in the auto-oxidation process of TA. Investigation of additional metalloid ions showed that germanic acid was also able to initiate a continuous coating formation of TA, whereas boric acid prevented the polymerization process. In comparison to TA, the coating formation of pyrogallol (PG) and gallic acid (GA) was not affected by Siaq. PG formed continuous coatings also without Siaq, while GA only formed a monolayer in presence of Siaq. However, Siaq induced a continuous layer formation of ellagic acid (EA). These results indicate the specific importance of ortho-silicic acid in the coating formation of polyphenolic molecules with multiple ortho-dihydroxy groups and open new possibilities to deposit TA on interfaces.

Wiedmer et al.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractStarting with the liberalization of electricity trading, this market grew rapidly over the last decade. However, while spot and future markets are rather liquid nowadays, option trading is still limited. One of the potential reasons for this is that the spot price process of electricity is still puzzling researchers and practitioners. In this paper, we propose an approach to model spot prices that combines mean-reversion, spikes and stochastic volatility. Thereby we use different mean-reversion rates for "normal" and "extreme" (spike) periods. Another feature of the model is its ability to capture correlation structures of electricity price spikes. Furthermore, all model parameters can easily be estimated with help of historical data. Consequently, we argue that this model does not only extend academic literature on electricity spot price modeling, but is also suitable for practical purposes, e.g. as underlying price model for option pricing. List of SymbolsSeasonality function value at time t Φ(·)Normal distribution function Sigma Correlation matrix Ω Probability space α x Mean-reversion rate of the "normal" diffusion process α yMean-reversion rate of the "extreme" spike process α zMean-reversion rate of the unseparated process γ Regression coeffienct ε(t)Daily changes of Z(t) not caused by mean-reversion λ Spot price at time t T Time horizion X(t) "Normal" diffusion process at time t Y(t) "Extreme" spike process at time t at time t Z(t)Unseparated stochastic process at time t dB(t) Independent Increments of the Brownian motion dI(t) Independent Increments of the compound Poisson process m(t)Moving average filter at time t sea(t)Yearly seasonality at time t w(t mod 7)Weekly seasonality at time t ii 1 INTRODUCTION