Methacrylate ester as well as allylether based polycarboxylates (PCEs) were synthesized to plasticize pastes of cement and silica fume having a water/cement ratio of 0.22. Methacrylate ester copolymers were found to disperse cement well, whereas allylether copolymers are more effective with silica fume. Mechanistic investigations revealed that in cement pore solution, the surface charge of silica fume becomes positive by adsorption of Ca 2+ onto negatively charged silanolate groups present on the silica surface. This way, polycarboxylate copolymers adsorb to and disperse silica fume grains. Thus, mixtures of both copolymers were tested in cement-silica fume pastes. These blends provide significantly better dispersion than using only one polymer. Apparently, the surfaces of hydrating cement (here mainly ettringite) and silica fume are quite different with respect to their chemical composition. Therefore, PCEs with different molecular architectures are required to provide maximum coordination with calcium atoms present on these surfaces.

No abstract

How waterlike are ionic water clusters? Clusters with 10 to 100 water molecules are frequently used as model systems for reactions in aqueous solution. Concerted proton transfer, a key feature of liquid water, can be probed by observing the formation of HDO when D2O is used as a reactant (see scheme). Mass spectrometry shows that proton transfer occurs in H(H2O)n+, whereas the individual water molecules stay intact in (H2O)n− and O2(H2O)n−.

Wie ähnlich sind sich ionische Wassercluster und Wasser? Mittelgroße Cluster mit 10 bis 100 Wassermolekülen werden häufig als Modellsysteme für Reaktionen in wässriger Lösung herangezogen. Ein konzertierter Protonentransfer mit einer Umlagerung von O‐H‐Bindungen ist ein Schlüsselmerkmal flüssigen Wassers, und mit D2O als Reaktant dient die Bildung von HDO als chemischer Nachweis dieses Prozesses (siehe Schema). Durch Massenspektrometrie wird belegt, dass Protonentransfer in H(H2O)n+ stattfindet, wohingegen in (H2O)n− und O2(H2O)n− die Wassermoleküle intakt bleiben.

Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of predicting individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI) -an ECT seizure quality index -and whole-brain modal and average controllability, NCT metrics based on white matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N=50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.