Ueber das Verhalten des Glycerins zu Chloroform

Ohne Zusammenfassung     

Zur Erkennung schwefelsaurer Alkalien im Bittersalz

Ohne Zusammenfassung     

Self‐assembly of π‐conjugated bis(terpyridine) ligands with zinc(II) ions: New metallosupramolecular materials for optoelectronic applications

A series of main‐chain metallopolymers ( P1–P10 ) was prepared by the self‐assembly of rigid‐linear π‐conjugated bis(terpyridine) monomers ( 1–10 ) with Zn^II ions and was fully characterized. The polymerization was additionally confirmed by UV/vis titration experiments. A strong increase in viscosities (around 1.6 times) relative to those of the monomer solutions was found. The thermal stability of P1–P10 compared with that of 1–10 was enhanced as a result of the metallopolymerization. The electro‐optical properties of the materials were investigated in detail. Tuning of the electrochemical and photophysical properties was enabled; thus, bright purple to green photoluminescent (PL) emission (PL quantum yields of 0.12–0.81) for P1–P10 was observed in solution with the emission color strongly depending on the nature of the π‐conjugated bis(terpyridine) system. Thin homogeneous films of P6 were prepared by solution processing, that is, spin‐coating and inkjet‐printing, and exhibited intense yellow PL emission in the solid state. © 2009 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 47: 4083–4098, 2009     

Error estimation in nonlinear optimization

Methods are developed and analyzed for estimating the distance to a local minimizer of a nonlinear programming problem. One estimate, based on the solution of a constrained convex quadratic program, can be used when strict complementary slackness and the second-order sufficient optimality conditions hold. A second estimate, based on the solution of an unconstrained nonconvex, nonsmooth optimization problem, is valid even when strict complementary slackness is violated. Both estimates are valid in a neighborhood of a local minimizer. An active set algorithm is developed for computing a stationary point of the nonsmooth error estimator. Each iteration of the algorithm requires the solution of a symmetric, positive semidefinite linear system, followed by a line search. Convergence is achieved in a finite number of iterations. The error bounds are based on stability properties for nonlinear programs. The theory is illustrated by some numerical examples.